commit 86131ebe5ef798c79be363ac53692be6f9dbdcd1
parent 24565f8473080c94b247a7360bc30b0c9a4a9fea
Author: Andrew <andrewlaack1@gmail.com>
Date: Thu, 27 Jun 2024 17:03:44 -0500
Used DNN to generate text based on shakespeare
Diffstat:
1 file changed, 2909 insertions(+), 0 deletions(-)
diff --git a/nnTextGeneration/NNTextGeneration.ipynb b/nnTextGeneration/NNTextGeneration.ipynb
@@ -0,0 +1,2909 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 54,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import keras\n",
+ "import tensorflow as tf\n",
+ "\n",
+ "f = open('../datasets/shakespeare/allText.txt', 'r')\n",
+ "\n",
+ "allText = f.readlines()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 55,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "txtAll = ''.join(str(element) for element in allText)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "5447117"
+ ]
+ },
+ "execution_count": 56,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "len(txtAll)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 57,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "unique_chars = set(txtAll)\n",
+ "total_unique = len(unique_chars)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " /* Definition of color scheme common for light and dark mode */\n",
+ " --sklearn-color-text: black;\n",
+ " --sklearn-color-line: gray;\n",
+ " /* Definition of color scheme for unfitted estimators */\n",
+ " --sklearn-color-unfitted-level-0: #fff5e6;\n",
+ " --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+ " --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+ " --sklearn-color-unfitted-level-3: chocolate;\n",
+ " /* Definition of color scheme for fitted estimators */\n",
+ " --sklearn-color-fitted-level-0: #f0f8ff;\n",
+ " --sklearn-color-fitted-level-1: #d4ebff;\n",
+ " --sklearn-color-fitted-level-2: #b3dbfd;\n",
+ " --sklearn-color-fitted-level-3: cornflowerblue;\n",
+ "\n",
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+ " --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+ " --sklearn-color-icon: #696969;\n",
+ "\n",
+ " @media (prefers-color-scheme: dark) {\n",
+ " /* Redefinition of color scheme for dark theme */\n",
+ " --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
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+ "\n",
+ "#sk-container-id-4 {\n",
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+ " padding: 0;\n",
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+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-dashed-wrapped {\n",
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+ " /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
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+ " default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+ " See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+ " display: inline-block !important;\n",
+ " position: relative;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+ " display: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-parallel-item,\n",
+ "div.sk-serial,\n",
+ "div.sk-item {\n",
+ " /* draw centered vertical line to link estimators */\n",
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+ "}\n",
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+ "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
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+ "}\n",
+ "\n",
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+ " margin: 0.2em;\n",
+ " border-radius: 0.25em;\n",
+ " color: var(--sklearn-color-text);\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
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+ "}\n",
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+ "}\n",
+ "\n",
+ "/* Pipeline/ColumnTransformer-specific style */\n",
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+ " color: var(--sklearn-color-text);\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
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+ "}\n",
+ "\n",
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+ " /* fitted */\n",
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+ "}\n",
+ "\n",
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+ "#sk-container-id-4 div.sk-label label {\n",
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+ " color: var(--sklearn-color-text-on-default-background);\n",
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+ "}\n",
+ "\n",
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+ " text-align: center;\n",
+ "}\n",
+ "\n",
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+ "#sk-container-id-4 div.sk-estimator {\n",
+ " font-family: monospace;\n",
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+ " border-radius: 0.25em;\n",
+ " box-sizing: border-box;\n",
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+ " background-color: var(--sklearn-color-unfitted-level-0);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-estimator.fitted {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-0);\n",
+ "}\n",
+ "\n",
+ "/* on hover */\n",
+ "#sk-container-id-4 div.sk-estimator:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-2);\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-2);\n",
+ "}\n",
+ "\n",
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+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted,\n",
+ "a:link.sk-estimator-doc-link.fitted,\n",
+ "a:visited.sk-estimator-doc-link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
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+ "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+ ".sk-estimator-doc-link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover,\n",
+ "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+ ".sk-estimator-doc-link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "/* Span, style for the box shown on hovering the info icon */\n",
+ ".sk-estimator-doc-link span {\n",
+ " display: none;\n",
+ " z-index: 9999;\n",
+ " position: relative;\n",
+ " font-weight: normal;\n",
+ " right: .2ex;\n",
+ " padding: .5ex;\n",
+ " margin: .5ex;\n",
+ " width: min-content;\n",
+ " min-width: 20ex;\n",
+ " max-width: 50ex;\n",
+ " color: var(--sklearn-color-text);\n",
+ " box-shadow: 2pt 2pt 4pt #999;\n",
+ " /* unfitted */\n",
+ " background: var(--sklearn-color-unfitted-level-0);\n",
+ " border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link.fitted span {\n",
+ " /* fitted */\n",
+ " background: var(--sklearn-color-fitted-level-0);\n",
+ " border: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "\n",
+ ".sk-estimator-doc-link:hover span {\n",
+ " display: block;\n",
+ "}\n",
+ "\n",
+ "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link {\n",
+ " float: right;\n",
+ " font-size: 1rem;\n",
+ " line-height: 1em;\n",
+ " font-family: monospace;\n",
+ " background-color: var(--sklearn-color-background);\n",
+ " border-radius: 1rem;\n",
+ " height: 1rem;\n",
+ " width: 1rem;\n",
+ " text-decoration: none;\n",
+ " /* unfitted */\n",
+ " color: var(--sklearn-color-unfitted-level-1);\n",
+ " border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+ " /* fitted */\n",
+ " border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+ " color: var(--sklearn-color-fitted-level-1);\n",
+ "}\n",
+ "\n",
+ "/* On hover */\n",
+ "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+ " /* unfitted */\n",
+ " background-color: var(--sklearn-color-unfitted-level-3);\n",
+ " color: var(--sklearn-color-background);\n",
+ " text-decoration: none;\n",
+ "}\n",
+ "\n",
+ "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+ " /* fitted */\n",
+ " background-color: var(--sklearn-color-fitted-level-3);\n",
+ "}\n",
+ "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> LabelEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.LabelEncoder.html\">?<span>Documentation for LabelEncoder</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LabelEncoder()</pre></div> </div></div></div></div>"
+ ],
+ "text/plain": [
+ "LabelEncoder()"
+ ]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "\n",
+ "label_encoder = LabelEncoder()\n",
+ "label_encoder.fit(list(txtAll))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "encoded_text = label_encoder.transform(list(txtAll))\n",
+ "encoded_text = encoded_text.tolist()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "if len(encoded_text) % 50 != 0:\n",
+ " padding_length = 50 - (len(encoded_text) % 50)\n",
+ " encoded_text.extend([0] * padding_length) # Padding with 0s\n",
+ "\n",
+ "# Convert the list to a NumPy array\n",
+ "encoded_array = np.array(encoded_text)\n",
+ "\n",
+ "# Reshape the array into a 2D array with each feature of length 50\n",
+ "reshaped_array = encoded_array.reshape(-1, 50)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(108943, 49)\n",
+ "(108943,)\n"
+ ]
+ }
+ ],
+ "source": [
+ "X = reshaped_array[:, :-1]\n",
+ "y = reshaped_array[:,49]\n",
+ "print(X.shape)\n",
+ "print(y.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_inverted = np.zeros((len(y), total_unique), dtype=int)\n",
+ "\n",
+ "for i, class_index in enumerate(y):\n",
+ " y_inverted[i, class_index] = 1\n",
+ "\n",
+ "y = y_inverted"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
+ " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
+ ]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "y[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = keras.Sequential(layers=[\n",
+ " keras.layers.Input((49,)),\n",
+ " keras.layers.Normalization(),\n",
+ " keras.layers.Dense(512, activation='relu'),\n",
+ " keras.layers.Dense(512, activation='relu'),\n",
+ " keras.layers.Dense(512, activation='relu'),\n",
+ " keras.layers.Dense(512, activation='relu'),\n",
+ " \n",
+ " keras.layers.BatchNormalization(),\n",
+ " keras.layers.Dense(256, activation='relu'),\n",
+ " keras.layers.Dense(256, activation='relu'),\n",
+ " keras.layers.Dense(256, activation='relu'),\n",
+ " keras.layers.Dense(256, activation='relu'),\n",
+ "\n",
+ " keras.layers.BatchNormalization(),\n",
+ " keras.layers.Dense(128, activation='relu'),\n",
+ " keras.layers.Dense(128, activation='relu'),\n",
+ " keras.layers.Dense(128, activation='relu'),\n",
+ " keras.layers.Dense(128, activation='relu'),\n",
+ "\n",
+ " keras.layers.BatchNormalization(),\n",
+ " keras.layers.Dense(64, activation='relu'),\n",
+ " keras.layers.Dense(64, activation='relu'),\n",
+ " keras.layers.Dense(64, activation='relu'),\n",
+ " keras.layers.Dense(64, activation='relu'),\n",
+ "\n",
+ " keras.layers.Dense(total_unique, activation='softmax'),\n",
+ "])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.compile(loss=keras.losses.categorical_crossentropy, optimizer='adam', metrics=['accuracy'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 22ms/step - accuracy: 0.2271 - loss: 3.3681\n",
+ "Epoch 2/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.2750 - loss: 2.6686\n",
+ "Epoch 3/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.2945 - loss: 2.5928\n",
+ "Epoch 4/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.3078 - loss: 2.5371\n",
+ "Epoch 5/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.3141 - loss: 2.4976\n",
+ "Epoch 6/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3169 - loss: 2.4681\n",
+ "Epoch 7/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.3219 - loss: 2.4505\n",
+ "Epoch 8/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3302 - loss: 2.4170\n",
+ "Epoch 9/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3322 - loss: 2.3931\n",
+ "Epoch 10/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3382 - loss: 2.3645\n",
+ "Epoch 11/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3432 - loss: 2.3459\n",
+ "Epoch 12/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.3506 - loss: 2.3196\n",
+ "Epoch 13/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.3525 - loss: 2.3114\n",
+ "Epoch 14/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3616 - loss: 2.2779\n",
+ "Epoch 15/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.3613 - loss: 2.2612\n",
+ "Epoch 16/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.3613 - loss: 2.2581\n",
+ "Epoch 17/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.3673 - loss: 2.2312\n",
+ "Epoch 18/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.3747 - loss: 2.2096\n",
+ "Epoch 19/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3750 - loss: 2.2007\n",
+ "Epoch 20/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.3808 - loss: 2.1852\n",
+ "Epoch 21/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.3772 - loss: 2.1907\n",
+ "Epoch 22/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3825 - loss: 2.1631\n",
+ "Epoch 23/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.3870 - loss: 2.1579\n",
+ "Epoch 24/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.3869 - loss: 2.1487\n",
+ "Epoch 25/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.3912 - loss: 2.1303\n",
+ "Epoch 26/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.3968 - loss: 2.1116\n",
+ "Epoch 27/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.3916 - loss: 2.1243\n",
+ "Epoch 28/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.3992 - loss: 2.0969\n",
+ "Epoch 29/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.3992 - loss: 2.1046\n",
+ "Epoch 30/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.3968 - loss: 2.1021\n",
+ "Epoch 31/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4013 - loss: 2.0879\n",
+ "Epoch 32/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4012 - loss: 2.0825\n",
+ "Epoch 33/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4037 - loss: 2.0662\n",
+ "Epoch 34/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4019 - loss: 2.0757\n",
+ "Epoch 35/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4024 - loss: 2.0694\n",
+ "Epoch 36/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4079 - loss: 2.0560\n",
+ "Epoch 37/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4095 - loss: 2.0381\n",
+ "Epoch 38/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4129 - loss: 2.0292\n",
+ "Epoch 39/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4092 - loss: 2.0381\n",
+ "Epoch 40/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4172 - loss: 2.0226\n",
+ "Epoch 41/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4139 - loss: 2.0188\n",
+ "Epoch 42/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4191 - loss: 1.9991\n",
+ "Epoch 43/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4153 - loss: 2.0124\n",
+ "Epoch 44/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4199 - loss: 1.9898\n",
+ "Epoch 45/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4204 - loss: 1.9972\n",
+ "Epoch 46/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4219 - loss: 1.9935\n",
+ "Epoch 47/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4214 - loss: 1.9932\n",
+ "Epoch 48/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4264 - loss: 1.9747\n",
+ "Epoch 49/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4257 - loss: 1.9769\n",
+ "Epoch 50/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4307 - loss: 1.9599\n",
+ "Epoch 51/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4258 - loss: 1.9655\n",
+ "Epoch 52/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4287 - loss: 1.9566\n",
+ "Epoch 53/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4277 - loss: 1.9602\n",
+ "Epoch 54/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4338 - loss: 1.9412\n",
+ "Epoch 55/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4309 - loss: 1.9463\n",
+ "Epoch 56/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4347 - loss: 1.9294\n",
+ "Epoch 57/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4327 - loss: 1.9365\n",
+ "Epoch 58/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4310 - loss: 1.9345\n",
+ "Epoch 59/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4366 - loss: 1.9151\n",
+ "Epoch 60/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4372 - loss: 1.9275\n",
+ "Epoch 61/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4368 - loss: 1.9183\n",
+ "Epoch 62/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4397 - loss: 1.9083\n",
+ "Epoch 63/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4425 - loss: 1.8937\n",
+ "Epoch 64/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4425 - loss: 1.8957\n",
+ "Epoch 65/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4413 - loss: 1.8956\n",
+ "Epoch 66/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4468 - loss: 1.8750\n",
+ "Epoch 67/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4485 - loss: 1.8820\n",
+ "Epoch 68/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4476 - loss: 1.8769\n",
+ "Epoch 69/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4477 - loss: 1.8756\n",
+ "Epoch 70/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4482 - loss: 1.8744\n",
+ "Epoch 71/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4471 - loss: 1.8819\n",
+ "Epoch 72/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4496 - loss: 1.8640\n",
+ "Epoch 73/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4496 - loss: 1.8623\n",
+ "Epoch 74/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4522 - loss: 1.8465\n",
+ "Epoch 75/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4525 - loss: 1.8496\n",
+ "Epoch 76/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4506 - loss: 1.8544\n",
+ "Epoch 77/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4524 - loss: 1.8487\n",
+ "Epoch 78/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4564 - loss: 1.8302\n",
+ "Epoch 79/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4567 - loss: 1.8357\n",
+ "Epoch 80/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4583 - loss: 1.8270\n",
+ "Epoch 81/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4520 - loss: 1.8599\n",
+ "Epoch 82/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4655 - loss: 1.8077\n",
+ "Epoch 83/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4546 - loss: 1.8399\n",
+ "Epoch 84/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4640 - loss: 1.8054\n",
+ "Epoch 85/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4640 - loss: 1.8035\n",
+ "Epoch 86/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4662 - loss: 1.7950\n",
+ "Epoch 87/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4644 - loss: 1.8035\n",
+ "Epoch 88/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4624 - loss: 1.8055\n",
+ "Epoch 89/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4636 - loss: 1.8035\n",
+ "Epoch 90/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4639 - loss: 1.8010\n",
+ "Epoch 91/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4652 - loss: 1.8117\n",
+ "Epoch 92/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4698 - loss: 1.7819\n",
+ "Epoch 93/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4698 - loss: 1.7787\n",
+ "Epoch 94/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4708 - loss: 1.7826\n",
+ "Epoch 95/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4694 - loss: 1.7823\n",
+ "Epoch 96/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4781 - loss: 1.7530\n",
+ "Epoch 97/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4761 - loss: 1.7552\n",
+ "Epoch 98/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4728 - loss: 1.7708\n",
+ "Epoch 99/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4723 - loss: 1.7650\n",
+ "Epoch 100/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4752 - loss: 1.7565\n",
+ "Epoch 101/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4703 - loss: 1.7706\n",
+ "Epoch 102/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4732 - loss: 1.7519\n",
+ "Epoch 103/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4775 - loss: 1.7461\n",
+ "Epoch 104/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4812 - loss: 1.7413\n",
+ "Epoch 105/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4771 - loss: 1.7437\n",
+ "Epoch 106/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4809 - loss: 1.7321\n",
+ "Epoch 107/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4812 - loss: 1.7358\n",
+ "Epoch 108/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4859 - loss: 1.7176\n",
+ "Epoch 109/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4826 - loss: 1.7199\n",
+ "Epoch 110/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4821 - loss: 1.7274\n",
+ "Epoch 111/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4808 - loss: 1.7292\n",
+ "Epoch 112/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4800 - loss: 1.7344\n",
+ "Epoch 113/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4861 - loss: 1.7192\n",
+ "Epoch 114/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4854 - loss: 1.7070\n",
+ "Epoch 115/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.4861 - loss: 1.7161\n",
+ "Epoch 116/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4849 - loss: 1.7070\n",
+ "Epoch 117/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.4877 - loss: 1.7024\n",
+ "Epoch 118/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4871 - loss: 1.7039\n",
+ "Epoch 119/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.4849 - loss: 1.7133\n",
+ "Epoch 120/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4917 - loss: 1.6864\n",
+ "Epoch 121/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4899 - loss: 1.6962\n",
+ "Epoch 122/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4897 - loss: 1.6869\n",
+ "Epoch 123/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4916 - loss: 1.6852\n",
+ "Epoch 124/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4925 - loss: 1.6854\n",
+ "Epoch 125/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4921 - loss: 1.6800\n",
+ "Epoch 126/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4943 - loss: 1.6713\n",
+ "Epoch 127/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4964 - loss: 1.6692\n",
+ "Epoch 128/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4967 - loss: 1.6680\n",
+ "Epoch 129/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4974 - loss: 1.6610\n",
+ "Epoch 130/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4988 - loss: 1.6629\n",
+ "Epoch 131/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4995 - loss: 1.6576\n",
+ "Epoch 132/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4958 - loss: 1.6672\n",
+ "Epoch 133/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5007 - loss: 1.6544\n",
+ "Epoch 134/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5005 - loss: 1.6519\n",
+ "Epoch 135/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.4951 - loss: 1.6742\n",
+ "Epoch 136/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5003 - loss: 1.6404\n",
+ "Epoch 137/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5032 - loss: 1.6374\n",
+ "Epoch 138/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5032 - loss: 1.6434\n",
+ "Epoch 139/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5029 - loss: 1.6366\n",
+ "Epoch 140/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5078 - loss: 1.6248\n",
+ "Epoch 141/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5023 - loss: 1.6392\n",
+ "Epoch 142/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5026 - loss: 1.6353\n",
+ "Epoch 143/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5043 - loss: 1.6244\n",
+ "Epoch 144/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5081 - loss: 1.6189\n",
+ "Epoch 145/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5062 - loss: 1.6226\n",
+ "Epoch 146/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5057 - loss: 1.6286\n",
+ "Epoch 147/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5083 - loss: 1.6156\n",
+ "Epoch 148/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5097 - loss: 1.6125\n",
+ "Epoch 149/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5121 - loss: 1.6054\n",
+ "Epoch 150/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5087 - loss: 1.6077\n",
+ "Epoch 151/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5101 - loss: 1.6156\n",
+ "Epoch 152/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.5170 - loss: 1.5922\n",
+ "Epoch 153/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5120 - loss: 1.5988\n",
+ "Epoch 154/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5000 - loss: 1.6604\n",
+ "Epoch 155/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.4942 - loss: 1.6816\n",
+ "Epoch 156/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5139 - loss: 1.5979\n",
+ "Epoch 157/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5185 - loss: 1.5757\n",
+ "Epoch 158/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5215 - loss: 1.5695\n",
+ "Epoch 159/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5182 - loss: 1.5670\n",
+ "Epoch 160/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5181 - loss: 1.5870\n",
+ "Epoch 161/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5233 - loss: 1.5650\n",
+ "Epoch 162/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5186 - loss: 1.5742\n",
+ "Epoch 163/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5227 - loss: 1.5634\n",
+ "Epoch 164/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5217 - loss: 1.5613\n",
+ "Epoch 165/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5267 - loss: 1.5567\n",
+ "Epoch 166/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5167 - loss: 1.5799\n",
+ "Epoch 167/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5267 - loss: 1.5453\n",
+ "Epoch 168/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5226 - loss: 1.5587\n",
+ "Epoch 169/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5226 - loss: 1.5616\n",
+ "Epoch 170/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5278 - loss: 1.5388\n",
+ "Epoch 171/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5264 - loss: 1.5433\n",
+ "Epoch 172/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5233 - loss: 1.5597\n",
+ "Epoch 173/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5306 - loss: 1.5268\n",
+ "Epoch 174/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5281 - loss: 1.5405\n",
+ "Epoch 175/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5302 - loss: 1.5272\n",
+ "Epoch 176/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5286 - loss: 1.5385\n",
+ "Epoch 177/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5377 - loss: 1.5031\n",
+ "Epoch 178/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5306 - loss: 1.5223\n",
+ "Epoch 179/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5351 - loss: 1.5167\n",
+ "Epoch 180/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5356 - loss: 1.5032\n",
+ "Epoch 181/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5334 - loss: 1.5131\n",
+ "Epoch 182/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5367 - loss: 1.5025\n",
+ "Epoch 183/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5361 - loss: 1.5051\n",
+ "Epoch 184/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5368 - loss: 1.4994\n",
+ "Epoch 185/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5403 - loss: 1.4928\n",
+ "Epoch 186/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5398 - loss: 1.4964\n",
+ "Epoch 187/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5409 - loss: 1.4890\n",
+ "Epoch 188/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5391 - loss: 1.4902\n",
+ "Epoch 189/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5415 - loss: 1.4953\n",
+ "Epoch 190/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5428 - loss: 1.4855\n",
+ "Epoch 191/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5424 - loss: 1.4894\n",
+ "Epoch 192/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5413 - loss: 1.4848\n",
+ "Epoch 193/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5425 - loss: 1.4801\n",
+ "Epoch 194/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5416 - loss: 1.4762\n",
+ "Epoch 195/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5458 - loss: 1.4716\n",
+ "Epoch 196/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5466 - loss: 1.4693\n",
+ "Epoch 197/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5477 - loss: 1.4621\n",
+ "Epoch 198/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5398 - loss: 1.4952\n",
+ "Epoch 199/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5443 - loss: 1.4673\n",
+ "Epoch 200/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5490 - loss: 1.4545\n",
+ "Epoch 201/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5422 - loss: 1.4762\n",
+ "Epoch 202/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5507 - loss: 1.4548\n",
+ "Epoch 203/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5488 - loss: 1.4465\n",
+ "Epoch 204/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5511 - loss: 1.4449\n",
+ "Epoch 205/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5524 - loss: 1.4439\n",
+ "Epoch 206/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5519 - loss: 1.4392\n",
+ "Epoch 207/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5520 - loss: 1.4404\n",
+ "Epoch 208/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5525 - loss: 1.4432\n",
+ "Epoch 209/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5520 - loss: 1.4437\n",
+ "Epoch 210/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5562 - loss: 1.4233\n",
+ "Epoch 211/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5573 - loss: 1.4272\n",
+ "Epoch 212/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5604 - loss: 1.4158\n",
+ "Epoch 213/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5603 - loss: 1.4158\n",
+ "Epoch 214/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5507 - loss: 1.4400\n",
+ "Epoch 215/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5559 - loss: 1.4305\n",
+ "Epoch 216/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5593 - loss: 1.4149\n",
+ "Epoch 217/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5593 - loss: 1.4063\n",
+ "Epoch 218/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5568 - loss: 1.4241\n",
+ "Epoch 219/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5582 - loss: 1.4170\n",
+ "Epoch 220/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5598 - loss: 1.4096\n",
+ "Epoch 221/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5640 - loss: 1.3989\n",
+ "Epoch 222/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5678 - loss: 1.3830\n",
+ "Epoch 223/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5601 - loss: 1.4153\n",
+ "Epoch 224/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5664 - loss: 1.3819\n",
+ "Epoch 225/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5591 - loss: 1.4181\n",
+ "Epoch 226/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5669 - loss: 1.3881\n",
+ "Epoch 227/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5685 - loss: 1.3829\n",
+ "Epoch 228/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5664 - loss: 1.3880\n",
+ "Epoch 229/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5689 - loss: 1.3854\n",
+ "Epoch 230/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5698 - loss: 1.3786\n",
+ "Epoch 231/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5680 - loss: 1.3789\n",
+ "Epoch 232/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5690 - loss: 1.3788\n",
+ "Epoch 233/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5698 - loss: 1.3777\n",
+ "Epoch 234/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5745 - loss: 1.3570\n",
+ "Epoch 235/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5702 - loss: 1.3723\n",
+ "Epoch 236/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5736 - loss: 1.3636\n",
+ "Epoch 237/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5748 - loss: 1.3608\n",
+ "Epoch 238/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5762 - loss: 1.3551\n",
+ "Epoch 239/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5744 - loss: 1.3579\n",
+ "Epoch 240/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5753 - loss: 1.3501\n",
+ "Epoch 241/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5778 - loss: 1.3451\n",
+ "Epoch 242/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5792 - loss: 1.3486\n",
+ "Epoch 243/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5766 - loss: 1.3423\n",
+ "Epoch 244/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5747 - loss: 1.3544\n",
+ "Epoch 245/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5747 - loss: 1.3584\n",
+ "Epoch 246/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5828 - loss: 1.3288\n",
+ "Epoch 247/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5826 - loss: 1.3264\n",
+ "Epoch 248/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5817 - loss: 1.3334\n",
+ "Epoch 249/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5818 - loss: 1.3363\n",
+ "Epoch 250/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5851 - loss: 1.3166\n",
+ "Epoch 251/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5773 - loss: 1.3429\n",
+ "Epoch 252/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5850 - loss: 1.3183\n",
+ "Epoch 253/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5844 - loss: 1.3212\n",
+ "Epoch 254/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5866 - loss: 1.3219\n",
+ "Epoch 255/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5866 - loss: 1.3149\n",
+ "Epoch 256/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5888 - loss: 1.3072\n",
+ "Epoch 257/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5930 - loss: 1.2951\n",
+ "Epoch 258/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5919 - loss: 1.2967\n",
+ "Epoch 259/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5839 - loss: 1.3190\n",
+ "Epoch 260/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5925 - loss: 1.2923\n",
+ "Epoch 261/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.5926 - loss: 1.3006\n",
+ "Epoch 262/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5907 - loss: 1.2991\n",
+ "Epoch 263/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5889 - loss: 1.3093\n",
+ "Epoch 264/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5880 - loss: 1.3076\n",
+ "Epoch 265/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5954 - loss: 1.2879\n",
+ "Epoch 266/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5946 - loss: 1.2954\n",
+ "Epoch 267/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5957 - loss: 1.2820\n",
+ "Epoch 268/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5948 - loss: 1.2785\n",
+ "Epoch 269/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5888 - loss: 1.3029\n",
+ "Epoch 270/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5993 - loss: 1.2717\n",
+ "Epoch 271/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5987 - loss: 1.2719\n",
+ "Epoch 272/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6036 - loss: 1.2632\n",
+ "Epoch 273/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5980 - loss: 1.2702\n",
+ "Epoch 274/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.5985 - loss: 1.2718\n",
+ "Epoch 275/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6027 - loss: 1.2586\n",
+ "Epoch 276/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.5965 - loss: 1.2796\n",
+ "Epoch 277/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6007 - loss: 1.2685\n",
+ "Epoch 278/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6070 - loss: 1.2529\n",
+ "Epoch 279/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6031 - loss: 1.2608\n",
+ "Epoch 280/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.5997 - loss: 1.2676\n",
+ "Epoch 281/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.6030 - loss: 1.2657\n",
+ "Epoch 282/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6059 - loss: 1.2452\n",
+ "Epoch 283/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6065 - loss: 1.2460\n",
+ "Epoch 284/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6046 - loss: 1.2537\n",
+ "Epoch 285/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6097 - loss: 1.2330\n",
+ "Epoch 286/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6074 - loss: 1.2371\n",
+ "Epoch 287/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6093 - loss: 1.2410\n",
+ "Epoch 288/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6067 - loss: 1.2423\n",
+ "Epoch 289/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6096 - loss: 1.2297\n",
+ "Epoch 290/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.6083 - loss: 1.2323\n",
+ "Epoch 291/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6101 - loss: 1.2364\n",
+ "Epoch 292/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6090 - loss: 1.2333\n",
+ "Epoch 293/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6125 - loss: 1.2209\n",
+ "Epoch 294/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6090 - loss: 1.2359\n",
+ "Epoch 295/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.6100 - loss: 1.2237\n",
+ "Epoch 296/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6145 - loss: 1.2215\n",
+ "Epoch 297/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6091 - loss: 1.2356\n",
+ "Epoch 298/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.6121 - loss: 1.2254\n",
+ "Epoch 299/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6187 - loss: 1.2014\n",
+ "Epoch 300/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6211 - loss: 1.1949\n",
+ "Epoch 301/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6184 - loss: 1.2013\n",
+ "Epoch 302/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6163 - loss: 1.2146\n",
+ "Epoch 303/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6184 - loss: 1.2030\n",
+ "Epoch 304/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6183 - loss: 1.2006\n",
+ "Epoch 305/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6149 - loss: 1.2087\n",
+ "Epoch 306/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6204 - loss: 1.1937\n",
+ "Epoch 307/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6201 - loss: 1.1955\n",
+ "Epoch 308/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6185 - loss: 1.1964\n",
+ "Epoch 309/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6198 - loss: 1.2004\n",
+ "Epoch 310/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6226 - loss: 1.1950\n",
+ "Epoch 311/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6238 - loss: 1.1826\n",
+ "Epoch 312/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6272 - loss: 1.1805\n",
+ "Epoch 313/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6186 - loss: 1.1993\n",
+ "Epoch 314/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6184 - loss: 1.1952\n",
+ "Epoch 315/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6267 - loss: 1.1866\n",
+ "Epoch 316/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6264 - loss: 1.1754\n",
+ "Epoch 317/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6255 - loss: 1.1831\n",
+ "Epoch 318/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6283 - loss: 1.1690\n",
+ "Epoch 319/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6283 - loss: 1.1688\n",
+ "Epoch 320/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6310 - loss: 1.1591\n",
+ "Epoch 321/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6292 - loss: 1.1677\n",
+ "Epoch 322/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6295 - loss: 1.1654\n",
+ "Epoch 323/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6332 - loss: 1.1523\n",
+ "Epoch 324/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6293 - loss: 1.1633\n",
+ "Epoch 325/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6285 - loss: 1.1675\n",
+ "Epoch 326/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6296 - loss: 1.1652\n",
+ "Epoch 327/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6292 - loss: 1.1645\n",
+ "Epoch 328/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6293 - loss: 1.1651\n",
+ "Epoch 329/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6375 - loss: 1.1441\n",
+ "Epoch 330/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6345 - loss: 1.1475\n",
+ "Epoch 331/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6325 - loss: 1.1528\n",
+ "Epoch 332/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6350 - loss: 1.1451\n",
+ "Epoch 333/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6367 - loss: 1.1446\n",
+ "Epoch 334/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6328 - loss: 1.1607\n",
+ "Epoch 335/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6310 - loss: 1.1578\n",
+ "Epoch 336/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6350 - loss: 1.1521\n",
+ "Epoch 337/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6364 - loss: 1.1410\n",
+ "Epoch 338/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6375 - loss: 1.1363\n",
+ "Epoch 339/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6396 - loss: 1.1301\n",
+ "Epoch 340/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6364 - loss: 1.1394\n",
+ "Epoch 341/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6405 - loss: 1.1273\n",
+ "Epoch 342/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6383 - loss: 1.1446\n",
+ "Epoch 343/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6399 - loss: 1.1274\n",
+ "Epoch 344/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6387 - loss: 1.1319\n",
+ "Epoch 345/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6395 - loss: 1.1343\n",
+ "Epoch 346/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6420 - loss: 1.1208\n",
+ "Epoch 347/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6422 - loss: 1.1258\n",
+ "Epoch 348/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6468 - loss: 1.1037\n",
+ "Epoch 349/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6452 - loss: 1.1099\n",
+ "Epoch 350/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6364 - loss: 1.1441\n",
+ "Epoch 351/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6423 - loss: 1.1259\n",
+ "Epoch 352/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6424 - loss: 1.1154\n",
+ "Epoch 353/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6460 - loss: 1.1080\n",
+ "Epoch 354/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6447 - loss: 1.1144\n",
+ "Epoch 355/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6426 - loss: 1.1202\n",
+ "Epoch 356/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6474 - loss: 1.1053\n",
+ "Epoch 357/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6456 - loss: 1.1053\n",
+ "Epoch 358/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6454 - loss: 1.1138\n",
+ "Epoch 359/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6427 - loss: 1.1216\n",
+ "Epoch 360/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6542 - loss: 1.0857\n",
+ "Epoch 361/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6460 - loss: 1.1031\n",
+ "Epoch 362/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6458 - loss: 1.1155\n",
+ "Epoch 363/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6521 - loss: 1.0922\n",
+ "Epoch 364/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6505 - loss: 1.0964\n",
+ "Epoch 365/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6484 - loss: 1.1014\n",
+ "Epoch 366/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6525 - loss: 1.0874\n",
+ "Epoch 367/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6501 - loss: 1.0971\n",
+ "Epoch 368/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6517 - loss: 1.0884\n",
+ "Epoch 369/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6484 - loss: 1.0971\n",
+ "Epoch 370/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6531 - loss: 1.0874\n",
+ "Epoch 371/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6562 - loss: 1.0832\n",
+ "Epoch 372/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6537 - loss: 1.0845\n",
+ "Epoch 373/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6542 - loss: 1.0843\n",
+ "Epoch 374/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6560 - loss: 1.0765\n",
+ "Epoch 375/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6577 - loss: 1.0708\n",
+ "Epoch 376/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6536 - loss: 1.0832\n",
+ "Epoch 377/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6560 - loss: 1.0738\n",
+ "Epoch 378/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6506 - loss: 1.0883\n",
+ "Epoch 379/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6565 - loss: 1.0723\n",
+ "Epoch 380/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6559 - loss: 1.0846\n",
+ "Epoch 381/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6612 - loss: 1.0565\n",
+ "Epoch 382/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6618 - loss: 1.0568\n",
+ "Epoch 383/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6541 - loss: 1.0839\n",
+ "Epoch 384/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6581 - loss: 1.0675\n",
+ "Epoch 385/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6604 - loss: 1.0604\n",
+ "Epoch 386/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6586 - loss: 1.0752\n",
+ "Epoch 387/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6602 - loss: 1.0577\n",
+ "Epoch 388/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6602 - loss: 1.0631\n",
+ "Epoch 389/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6634 - loss: 1.0541\n",
+ "Epoch 390/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6610 - loss: 1.0629\n",
+ "Epoch 391/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6649 - loss: 1.0498\n",
+ "Epoch 392/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6652 - loss: 1.0459\n",
+ "Epoch 393/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6601 - loss: 1.0539\n",
+ "Epoch 394/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6601 - loss: 1.0629\n",
+ "Epoch 395/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6632 - loss: 1.0493\n",
+ "Epoch 396/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6627 - loss: 1.0530\n",
+ "Epoch 397/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.6702 - loss: 1.0334\n",
+ "Epoch 398/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.6628 - loss: 1.0567\n",
+ "Epoch 399/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6644 - loss: 1.0454\n",
+ "Epoch 400/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6658 - loss: 1.0472\n",
+ "Epoch 401/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6667 - loss: 1.0421\n",
+ "Epoch 402/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6623 - loss: 1.0598\n",
+ "Epoch 403/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6686 - loss: 1.0356\n",
+ "Epoch 404/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6582 - loss: 1.0740\n",
+ "Epoch 405/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6641 - loss: 1.0503\n",
+ "Epoch 406/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6663 - loss: 1.0423\n",
+ "Epoch 407/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6699 - loss: 1.0323\n",
+ "Epoch 408/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6687 - loss: 1.0297\n",
+ "Epoch 409/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6687 - loss: 1.0362\n",
+ "Epoch 410/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6718 - loss: 1.0245\n",
+ "Epoch 411/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6700 - loss: 1.0238\n",
+ "Epoch 412/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6716 - loss: 1.0265\n",
+ "Epoch 413/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6691 - loss: 1.0294\n",
+ "Epoch 414/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6680 - loss: 1.0332\n",
+ "Epoch 415/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6673 - loss: 1.0357\n",
+ "Epoch 416/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6654 - loss: 1.0385\n",
+ "Epoch 417/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6763 - loss: 1.0123\n",
+ "Epoch 418/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6767 - loss: 1.0073\n",
+ "Epoch 419/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6723 - loss: 1.0272\n",
+ "Epoch 420/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6717 - loss: 1.0246\n",
+ "Epoch 421/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6684 - loss: 1.0289\n",
+ "Epoch 422/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6754 - loss: 1.0141\n",
+ "Epoch 423/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6716 - loss: 1.0239\n",
+ "Epoch 424/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6752 - loss: 1.0205\n",
+ "Epoch 425/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6715 - loss: 1.0310\n",
+ "Epoch 426/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6698 - loss: 1.0231\n",
+ "Epoch 427/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6818 - loss: 0.9868\n",
+ "Epoch 428/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6752 - loss: 1.0163\n",
+ "Epoch 429/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6717 - loss: 1.0195\n",
+ "Epoch 430/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6752 - loss: 1.0123\n",
+ "Epoch 431/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6762 - loss: 1.0073\n",
+ "Epoch 432/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6744 - loss: 1.0074\n",
+ "Epoch 433/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6759 - loss: 1.0097\n",
+ "Epoch 434/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6765 - loss: 1.0058\n",
+ "Epoch 435/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6814 - loss: 0.9907\n",
+ "Epoch 436/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6744 - loss: 1.0129\n",
+ "Epoch 437/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6788 - loss: 0.9937\n",
+ "Epoch 438/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6765 - loss: 1.0131\n",
+ "Epoch 439/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6823 - loss: 0.9893\n",
+ "Epoch 440/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6829 - loss: 0.9851\n",
+ "Epoch 441/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6774 - loss: 1.0053\n",
+ "Epoch 442/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6769 - loss: 1.0034\n",
+ "Epoch 443/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6816 - loss: 0.9948\n",
+ "Epoch 444/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6817 - loss: 0.9906\n",
+ "Epoch 445/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6845 - loss: 0.9832\n",
+ "Epoch 446/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6825 - loss: 0.9863\n",
+ "Epoch 447/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6854 - loss: 0.9793\n",
+ "Epoch 448/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6822 - loss: 0.9930\n",
+ "Epoch 449/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6752 - loss: 1.0154\n",
+ "Epoch 450/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6858 - loss: 0.9744\n",
+ "Epoch 451/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6861 - loss: 0.9765\n",
+ "Epoch 452/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6824 - loss: 0.9919\n",
+ "Epoch 453/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6809 - loss: 0.9913\n",
+ "Epoch 454/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6873 - loss: 0.9743\n",
+ "Epoch 455/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6787 - loss: 0.9971\n",
+ "Epoch 456/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6876 - loss: 0.9781\n",
+ "Epoch 457/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6929 - loss: 0.9600\n",
+ "Epoch 458/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6900 - loss: 0.9682\n",
+ "Epoch 459/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6840 - loss: 0.9837\n",
+ "Epoch 460/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6857 - loss: 0.9780\n",
+ "Epoch 461/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6888 - loss: 0.9643\n",
+ "Epoch 462/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6913 - loss: 0.9603\n",
+ "Epoch 463/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6876 - loss: 0.9764\n",
+ "Epoch 464/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6885 - loss: 0.9661\n",
+ "Epoch 465/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6853 - loss: 0.9778\n",
+ "Epoch 466/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6909 - loss: 0.9603\n",
+ "Epoch 467/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6901 - loss: 0.9686\n",
+ "Epoch 468/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6914 - loss: 0.9621\n",
+ "Epoch 469/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6849 - loss: 0.9849\n",
+ "Epoch 470/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6947 - loss: 0.9520\n",
+ "Epoch 471/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6882 - loss: 0.9658\n",
+ "Epoch 472/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6932 - loss: 0.9592\n",
+ "Epoch 473/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6832 - loss: 0.9848\n",
+ "Epoch 474/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6897 - loss: 0.9658\n",
+ "Epoch 475/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6948 - loss: 0.9433\n",
+ "Epoch 476/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6948 - loss: 0.9474\n",
+ "Epoch 477/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6827 - loss: 0.9849\n",
+ "Epoch 478/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6958 - loss: 0.9464\n",
+ "Epoch 479/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6920 - loss: 0.9577\n",
+ "Epoch 480/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6901 - loss: 0.9676\n",
+ "Epoch 481/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6861 - loss: 0.9762\n",
+ "Epoch 482/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6873 - loss: 0.9728\n",
+ "Epoch 483/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6988 - loss: 0.9377\n",
+ "Epoch 484/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6915 - loss: 0.9645\n",
+ "Epoch 485/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6925 - loss: 0.9613\n",
+ "Epoch 486/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6960 - loss: 0.9442\n",
+ "Epoch 487/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6958 - loss: 0.9467\n",
+ "Epoch 488/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6976 - loss: 0.9346\n",
+ "Epoch 489/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6961 - loss: 0.9416\n",
+ "Epoch 490/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6950 - loss: 0.9475\n",
+ "Epoch 491/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6968 - loss: 0.9420\n",
+ "Epoch 492/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6925 - loss: 0.9575\n",
+ "Epoch 493/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6957 - loss: 0.9456\n",
+ "Epoch 494/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6967 - loss: 0.9397\n",
+ "Epoch 495/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6972 - loss: 0.9427\n",
+ "Epoch 496/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6912 - loss: 0.9572\n",
+ "Epoch 497/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6987 - loss: 0.9302\n",
+ "Epoch 498/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7005 - loss: 0.9401\n",
+ "Epoch 499/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6940 - loss: 0.9492\n",
+ "Epoch 500/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6900 - loss: 0.9648\n",
+ "Epoch 501/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6986 - loss: 0.9324\n",
+ "Epoch 502/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6956 - loss: 0.9490\n",
+ "Epoch 503/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7002 - loss: 0.9336\n",
+ "Epoch 504/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7002 - loss: 0.9278\n",
+ "Epoch 505/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6977 - loss: 0.9350\n",
+ "Epoch 506/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7011 - loss: 0.9273\n",
+ "Epoch 507/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6996 - loss: 0.9284\n",
+ "Epoch 508/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6996 - loss: 0.9318\n",
+ "Epoch 509/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6920 - loss: 0.9568\n",
+ "Epoch 510/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.6941 - loss: 0.9513\n",
+ "Epoch 511/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.6963 - loss: 0.9437\n",
+ "Epoch 512/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6995 - loss: 0.9326\n",
+ "Epoch 513/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7027 - loss: 0.9166\n",
+ "Epoch 514/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7037 - loss: 0.9189\n",
+ "Epoch 515/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 24ms/step - accuracy: 0.6965 - loss: 0.9390\n",
+ "Epoch 516/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7025 - loss: 0.9261\n",
+ "Epoch 517/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 23ms/step - accuracy: 0.7054 - loss: 0.9151\n",
+ "Epoch 518/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7034 - loss: 0.9155\n",
+ "Epoch 519/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7025 - loss: 0.9197\n",
+ "Epoch 520/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7041 - loss: 0.9233\n",
+ "Epoch 521/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7049 - loss: 0.9219\n",
+ "Epoch 522/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7021 - loss: 0.9270\n",
+ "Epoch 523/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7013 - loss: 0.9252\n",
+ "Epoch 524/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6976 - loss: 0.9385\n",
+ "Epoch 525/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.6992 - loss: 0.9291\n",
+ "Epoch 526/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7044 - loss: 0.9201\n",
+ "Epoch 527/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7050 - loss: 0.9230\n",
+ "Epoch 528/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7093 - loss: 0.9001\n",
+ "Epoch 529/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7049 - loss: 0.9162\n",
+ "Epoch 530/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7030 - loss: 0.9174\n",
+ "Epoch 531/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7034 - loss: 0.9118\n",
+ "Epoch 532/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7084 - loss: 0.9063\n",
+ "Epoch 533/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7054 - loss: 0.9109\n",
+ "Epoch 534/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7082 - loss: 0.9019\n",
+ "Epoch 535/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7052 - loss: 0.9203\n",
+ "Epoch 536/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7004 - loss: 0.9333\n",
+ "Epoch 537/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7107 - loss: 0.8972\n",
+ "Epoch 538/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7024 - loss: 0.9189\n",
+ "Epoch 539/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7075 - loss: 0.9050\n",
+ "Epoch 540/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7130 - loss: 0.8916\n",
+ "Epoch 541/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7097 - loss: 0.8983\n",
+ "Epoch 542/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.6996 - loss: 0.9320\n",
+ "Epoch 543/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7136 - loss: 0.8900\n",
+ "Epoch 544/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7078 - loss: 0.9038\n",
+ "Epoch 545/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7130 - loss: 0.8891\n",
+ "Epoch 546/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7070 - loss: 0.9087\n",
+ "Epoch 547/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7074 - loss: 0.9021\n",
+ "Epoch 548/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7083 - loss: 0.9012\n",
+ "Epoch 549/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7142 - loss: 0.8863\n",
+ "Epoch 550/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7057 - loss: 0.9185\n",
+ "Epoch 551/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7104 - loss: 0.8942\n",
+ "Epoch 552/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7110 - loss: 0.8919\n",
+ "Epoch 553/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7129 - loss: 0.8916\n",
+ "Epoch 554/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7074 - loss: 0.9171\n",
+ "Epoch 555/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7123 - loss: 0.8958\n",
+ "Epoch 556/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7123 - loss: 0.8926\n",
+ "Epoch 557/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7143 - loss: 0.8844\n",
+ "Epoch 558/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7084 - loss: 0.9058\n",
+ "Epoch 559/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7131 - loss: 0.8861\n",
+ "Epoch 560/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7099 - loss: 0.8958\n",
+ "Epoch 561/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7163 - loss: 0.8783\n",
+ "Epoch 562/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7145 - loss: 0.8809\n",
+ "Epoch 563/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7145 - loss: 0.8807\n",
+ "Epoch 564/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7100 - loss: 0.8967\n",
+ "Epoch 565/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7119 - loss: 0.8920\n",
+ "Epoch 566/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7100 - loss: 0.8999\n",
+ "Epoch 567/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7195 - loss: 0.8683\n",
+ "Epoch 568/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7158 - loss: 0.8730\n",
+ "Epoch 569/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7090 - loss: 0.9014\n",
+ "Epoch 570/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7148 - loss: 0.8791\n",
+ "Epoch 571/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7165 - loss: 0.8750\n",
+ "Epoch 572/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7162 - loss: 0.8811\n",
+ "Epoch 573/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7104 - loss: 0.8942\n",
+ "Epoch 574/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7162 - loss: 0.8784\n",
+ "Epoch 575/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7187 - loss: 0.8767\n",
+ "Epoch 576/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7149 - loss: 0.8733\n",
+ "Epoch 577/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7188 - loss: 0.8720\n",
+ "Epoch 578/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7163 - loss: 0.8755\n",
+ "Epoch 579/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7145 - loss: 0.8888\n",
+ "Epoch 580/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7115 - loss: 0.8876\n",
+ "Epoch 581/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7190 - loss: 0.8715\n",
+ "Epoch 582/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7128 - loss: 0.8882\n",
+ "Epoch 583/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7191 - loss: 0.8679\n",
+ "Epoch 584/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7200 - loss: 0.8689\n",
+ "Epoch 585/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7158 - loss: 0.8814\n",
+ "Epoch 586/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7154 - loss: 0.8795\n",
+ "Epoch 587/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7222 - loss: 0.8614\n",
+ "Epoch 588/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7136 - loss: 0.8858\n",
+ "Epoch 589/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7208 - loss: 0.8627\n",
+ "Epoch 590/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7175 - loss: 0.8758\n",
+ "Epoch 591/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7252 - loss: 0.8516\n",
+ "Epoch 592/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7214 - loss: 0.8589\n",
+ "Epoch 593/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7199 - loss: 0.8641\n",
+ "Epoch 594/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7153 - loss: 0.8775\n",
+ "Epoch 595/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7201 - loss: 0.8622\n",
+ "Epoch 596/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7243 - loss: 0.8548\n",
+ "Epoch 597/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7211 - loss: 0.8616\n",
+ "Epoch 598/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7252 - loss: 0.8512\n",
+ "Epoch 599/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7153 - loss: 0.8751\n",
+ "Epoch 600/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7242 - loss: 0.8467\n",
+ "Epoch 601/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7161 - loss: 0.8755\n",
+ "Epoch 602/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7241 - loss: 0.8585\n",
+ "Epoch 603/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7212 - loss: 0.8623\n",
+ "Epoch 604/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7222 - loss: 0.8596\n",
+ "Epoch 605/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7221 - loss: 0.8589\n",
+ "Epoch 606/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7216 - loss: 0.8557\n",
+ "Epoch 607/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7256 - loss: 0.8501\n",
+ "Epoch 608/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7309 - loss: 0.8278\n",
+ "Epoch 609/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7228 - loss: 0.8519\n",
+ "Epoch 610/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7203 - loss: 0.8660\n",
+ "Epoch 611/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7266 - loss: 0.8481\n",
+ "Epoch 612/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7250 - loss: 0.8453\n",
+ "Epoch 613/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7233 - loss: 0.8560\n",
+ "Epoch 614/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7224 - loss: 0.8567\n",
+ "Epoch 615/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7243 - loss: 0.8473\n",
+ "Epoch 616/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7268 - loss: 0.8424\n",
+ "Epoch 617/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7191 - loss: 0.8722\n",
+ "Epoch 618/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7306 - loss: 0.8320\n",
+ "Epoch 619/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7292 - loss: 0.8403\n",
+ "Epoch 620/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7223 - loss: 0.8584\n",
+ "Epoch 621/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7331 - loss: 0.8295\n",
+ "Epoch 622/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7294 - loss: 0.8359\n",
+ "Epoch 623/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7275 - loss: 0.8397\n",
+ "Epoch 624/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7234 - loss: 0.8538\n",
+ "Epoch 625/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7283 - loss: 0.8356\n",
+ "Epoch 626/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7218 - loss: 0.8566\n",
+ "Epoch 627/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7259 - loss: 0.8479\n",
+ "Epoch 628/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7285 - loss: 0.8371\n",
+ "Epoch 629/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7300 - loss: 0.8334\n",
+ "Epoch 630/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7294 - loss: 0.8333\n",
+ "Epoch 631/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7305 - loss: 0.8300\n",
+ "Epoch 632/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7328 - loss: 0.8290\n",
+ "Epoch 633/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7293 - loss: 0.8388\n",
+ "Epoch 634/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7338 - loss: 0.8241\n",
+ "Epoch 635/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7304 - loss: 0.8335\n",
+ "Epoch 636/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7297 - loss: 0.8355\n",
+ "Epoch 637/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7249 - loss: 0.8417\n",
+ "Epoch 638/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7274 - loss: 0.8425\n",
+ "Epoch 639/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7277 - loss: 0.8392\n",
+ "Epoch 640/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7250 - loss: 0.8443\n",
+ "Epoch 641/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7240 - loss: 0.8533\n",
+ "Epoch 642/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7353 - loss: 0.8146\n",
+ "Epoch 643/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7350 - loss: 0.8095\n",
+ "Epoch 644/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7327 - loss: 0.8279\n",
+ "Epoch 645/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7344 - loss: 0.8176\n",
+ "Epoch 646/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7328 - loss: 0.8246\n",
+ "Epoch 647/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7296 - loss: 0.8356\n",
+ "Epoch 648/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7280 - loss: 0.8409\n",
+ "Epoch 649/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7315 - loss: 0.8268\n",
+ "Epoch 650/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7362 - loss: 0.8136\n",
+ "Epoch 651/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7347 - loss: 0.8222\n",
+ "Epoch 652/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7299 - loss: 0.8314\n",
+ "Epoch 653/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7296 - loss: 0.8342\n",
+ "Epoch 654/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7369 - loss: 0.8168\n",
+ "Epoch 655/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7332 - loss: 0.8224\n",
+ "Epoch 656/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7248 - loss: 0.8494\n",
+ "Epoch 657/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7253 - loss: 0.8471\n",
+ "Epoch 658/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7339 - loss: 0.8283\n",
+ "Epoch 659/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7411 - loss: 0.8038\n",
+ "Epoch 660/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7333 - loss: 0.8225\n",
+ "Epoch 661/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7356 - loss: 0.8195\n",
+ "Epoch 662/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7373 - loss: 0.8106\n",
+ "Epoch 663/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7304 - loss: 0.8281\n",
+ "Epoch 664/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7313 - loss: 0.8300\n",
+ "Epoch 665/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7330 - loss: 0.8272\n",
+ "Epoch 666/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7357 - loss: 0.8195\n",
+ "Epoch 667/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7345 - loss: 0.8121\n",
+ "Epoch 668/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7359 - loss: 0.8170\n",
+ "Epoch 669/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7308 - loss: 0.8373\n",
+ "Epoch 670/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7387 - loss: 0.8052\n",
+ "Epoch 671/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7396 - loss: 0.8034\n",
+ "Epoch 672/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7344 - loss: 0.8214\n",
+ "Epoch 673/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7466 - loss: 0.7765\n",
+ "Epoch 674/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7337 - loss: 0.8227\n",
+ "Epoch 675/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7358 - loss: 0.8106\n",
+ "Epoch 676/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7444 - loss: 0.7941\n",
+ "Epoch 677/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7358 - loss: 0.8150\n",
+ "Epoch 678/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7385 - loss: 0.8124\n",
+ "Epoch 679/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7359 - loss: 0.8154\n",
+ "Epoch 680/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7409 - loss: 0.8025\n",
+ "Epoch 681/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7286 - loss: 0.8363\n",
+ "Epoch 682/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7429 - loss: 0.7931\n",
+ "Epoch 683/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7401 - loss: 0.8026\n",
+ "Epoch 684/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7378 - loss: 0.8123\n",
+ "Epoch 685/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7365 - loss: 0.8135\n",
+ "Epoch 686/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7395 - loss: 0.8079\n",
+ "Epoch 687/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7445 - loss: 0.7912\n",
+ "Epoch 688/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7405 - loss: 0.8028\n",
+ "Epoch 689/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7384 - loss: 0.8066\n",
+ "Epoch 690/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7437 - loss: 0.7902\n",
+ "Epoch 691/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7382 - loss: 0.8070\n",
+ "Epoch 692/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - accuracy: 0.7388 - loss: 0.8004\n",
+ "Epoch 693/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7417 - loss: 0.7899\n",
+ "Epoch 694/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step - accuracy: 0.7408 - loss: 0.7912\n",
+ "Epoch 695/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7406 - loss: 0.7934\n",
+ "Epoch 696/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7388 - loss: 0.8028\n",
+ "Epoch 697/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7424 - loss: 0.7933\n",
+ "Epoch 698/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7428 - loss: 0.7953\n",
+ "Epoch 699/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7456 - loss: 0.7842\n",
+ "Epoch 700/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7438 - loss: 0.7906\n",
+ "Epoch 701/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 23ms/step - accuracy: 0.7364 - loss: 0.8084\n",
+ "Epoch 702/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - accuracy: 0.7388 - loss: 0.8060\n",
+ "Epoch 703/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 23ms/step - accuracy: 0.7434 - loss: 0.7897\n",
+ "Epoch 704/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7487 - loss: 0.7720\n",
+ "Epoch 705/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7433 - loss: 0.7881\n",
+ "Epoch 706/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 26ms/step - accuracy: 0.7472 - loss: 0.7848\n",
+ "Epoch 707/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step - accuracy: 0.7399 - loss: 0.7990\n",
+ "Epoch 708/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7414 - loss: 0.8004\n",
+ "Epoch 709/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 29ms/step - accuracy: 0.7438 - loss: 0.7965\n",
+ "Epoch 710/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 35ms/step - accuracy: 0.7467 - loss: 0.7854\n",
+ "Epoch 711/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 32ms/step - accuracy: 0.7454 - loss: 0.7845\n",
+ "Epoch 712/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 23ms/step - accuracy: 0.7396 - loss: 0.8028\n",
+ "Epoch 713/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 28ms/step - accuracy: 0.7461 - loss: 0.7855\n",
+ "Epoch 714/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7405 - loss: 0.7981\n",
+ "Epoch 715/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7431 - loss: 0.7884\n",
+ "Epoch 716/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7432 - loss: 0.7943\n",
+ "Epoch 717/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7450 - loss: 0.7836\n",
+ "Epoch 718/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step - accuracy: 0.7408 - loss: 0.7974\n",
+ "Epoch 719/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7451 - loss: 0.7812\n",
+ "Epoch 720/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7452 - loss: 0.7878\n",
+ "Epoch 721/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7460 - loss: 0.7816\n",
+ "Epoch 722/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 29ms/step - accuracy: 0.7401 - loss: 0.8034\n",
+ "Epoch 723/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - accuracy: 0.7474 - loss: 0.7791\n",
+ "Epoch 724/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7490 - loss: 0.7775\n",
+ "Epoch 725/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7475 - loss: 0.7847\n",
+ "Epoch 726/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step - accuracy: 0.7529 - loss: 0.7635\n",
+ "Epoch 727/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step - accuracy: 0.7440 - loss: 0.7851\n",
+ "Epoch 728/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - accuracy: 0.7431 - loss: 0.7939\n",
+ "Epoch 729/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step - accuracy: 0.7531 - loss: 0.7602\n",
+ "Epoch 730/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 23ms/step - accuracy: 0.7536 - loss: 0.7609\n",
+ "Epoch 731/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7434 - loss: 0.7878\n",
+ "Epoch 732/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step - accuracy: 0.7421 - loss: 0.7882\n",
+ "Epoch 733/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 23ms/step - accuracy: 0.7438 - loss: 0.7858\n",
+ "Epoch 734/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - accuracy: 0.7509 - loss: 0.7706\n",
+ "Epoch 735/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 23ms/step - accuracy: 0.7444 - loss: 0.7835\n",
+ "Epoch 736/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step - accuracy: 0.7479 - loss: 0.7783\n",
+ "Epoch 737/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7441 - loss: 0.7873\n",
+ "Epoch 738/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7444 - loss: 0.7890\n",
+ "Epoch 739/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7433 - loss: 0.7862\n",
+ "Epoch 740/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step - accuracy: 0.7498 - loss: 0.7704\n",
+ "Epoch 741/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 26ms/step - accuracy: 0.7500 - loss: 0.7667\n",
+ "Epoch 742/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7499 - loss: 0.7741\n",
+ "Epoch 743/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7478 - loss: 0.7750\n",
+ "Epoch 744/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7498 - loss: 0.7788\n",
+ "Epoch 745/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7519 - loss: 0.7655\n",
+ "Epoch 746/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7445 - loss: 0.7849\n",
+ "Epoch 747/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7518 - loss: 0.7603\n",
+ "Epoch 748/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7468 - loss: 0.7867\n",
+ "Epoch 749/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7395 - loss: 0.7993\n",
+ "Epoch 750/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7593 - loss: 0.7430\n",
+ "Epoch 751/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7488 - loss: 0.7705\n",
+ "Epoch 752/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7473 - loss: 0.7793\n",
+ "Epoch 753/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7523 - loss: 0.7619\n",
+ "Epoch 754/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7502 - loss: 0.7705\n",
+ "Epoch 755/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7452 - loss: 0.7856\n",
+ "Epoch 756/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7534 - loss: 0.7532\n",
+ "Epoch 757/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7555 - loss: 0.7530\n",
+ "Epoch 758/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7532 - loss: 0.7637\n",
+ "Epoch 759/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7540 - loss: 0.7589\n",
+ "Epoch 760/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7500 - loss: 0.7720\n",
+ "Epoch 761/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7482 - loss: 0.7750\n",
+ "Epoch 762/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7587 - loss: 0.7437\n",
+ "Epoch 763/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 23ms/step - accuracy: 0.7550 - loss: 0.7551\n",
+ "Epoch 764/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7520 - loss: 0.7599\n",
+ "Epoch 765/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7552 - loss: 0.7575\n",
+ "Epoch 766/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7530 - loss: 0.7626\n",
+ "Epoch 767/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7523 - loss: 0.7600\n",
+ "Epoch 768/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7536 - loss: 0.7588\n",
+ "Epoch 769/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7548 - loss: 0.7533\n",
+ "Epoch 770/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7522 - loss: 0.7598\n",
+ "Epoch 771/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7541 - loss: 0.7585\n",
+ "Epoch 772/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7539 - loss: 0.7624\n",
+ "Epoch 773/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7545 - loss: 0.7533\n",
+ "Epoch 774/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7609 - loss: 0.7365\n",
+ "Epoch 775/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 24ms/step - accuracy: 0.7505 - loss: 0.7757\n",
+ "Epoch 776/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7516 - loss: 0.7596\n",
+ "Epoch 777/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7501 - loss: 0.7695\n",
+ "Epoch 778/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 23ms/step - accuracy: 0.7509 - loss: 0.7651\n",
+ "Epoch 779/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7569 - loss: 0.7476\n",
+ "Epoch 780/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 23ms/step - accuracy: 0.7551 - loss: 0.7588\n",
+ "Epoch 781/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7542 - loss: 0.7644\n",
+ "Epoch 782/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7554 - loss: 0.7483\n",
+ "Epoch 783/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7503 - loss: 0.7644\n",
+ "Epoch 784/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7556 - loss: 0.7514\n",
+ "Epoch 785/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7548 - loss: 0.7539\n",
+ "Epoch 786/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7581 - loss: 0.7430\n",
+ "Epoch 787/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7578 - loss: 0.7485\n",
+ "Epoch 788/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7550 - loss: 0.7505\n",
+ "Epoch 789/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7594 - loss: 0.7431\n",
+ "Epoch 790/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7578 - loss: 0.7474\n",
+ "Epoch 791/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7578 - loss: 0.7396\n",
+ "Epoch 792/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7624 - loss: 0.7319\n",
+ "Epoch 793/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7502 - loss: 0.7723\n",
+ "Epoch 794/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7593 - loss: 0.7460\n",
+ "Epoch 795/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7594 - loss: 0.7391\n",
+ "Epoch 796/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7506 - loss: 0.7674\n",
+ "Epoch 797/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 24ms/step - accuracy: 0.7607 - loss: 0.7359\n",
+ "Epoch 798/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7601 - loss: 0.7393\n",
+ "Epoch 799/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7588 - loss: 0.7395\n",
+ "Epoch 800/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7531 - loss: 0.7608\n",
+ "Epoch 801/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7575 - loss: 0.7502\n",
+ "Epoch 802/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7571 - loss: 0.7444\n",
+ "Epoch 803/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7511 - loss: 0.7619\n",
+ "Epoch 804/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7611 - loss: 0.7336\n",
+ "Epoch 805/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7607 - loss: 0.7308\n",
+ "Epoch 806/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 24ms/step - accuracy: 0.7598 - loss: 0.7355\n",
+ "Epoch 807/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 21ms/step - accuracy: 0.7589 - loss: 0.7355\n",
+ "Epoch 808/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7587 - loss: 0.7388\n",
+ "Epoch 809/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7614 - loss: 0.7350\n",
+ "Epoch 810/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7585 - loss: 0.7416\n",
+ "Epoch 811/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.7552 - loss: 0.7597\n",
+ "Epoch 812/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7584 - loss: 0.7359\n",
+ "Epoch 813/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7592 - loss: 0.7422\n",
+ "Epoch 814/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.7654 - loss: 0.7236\n",
+ "Epoch 815/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7498 - loss: 0.7742\n",
+ "Epoch 816/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7601 - loss: 0.7429\n",
+ "Epoch 817/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7600 - loss: 0.7317\n",
+ "Epoch 818/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7570 - loss: 0.7481\n",
+ "Epoch 819/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7660 - loss: 0.7127\n",
+ "Epoch 820/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7624 - loss: 0.7310\n",
+ "Epoch 821/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7575 - loss: 0.7461\n",
+ "Epoch 822/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step - accuracy: 0.7560 - loss: 0.7483\n",
+ "Epoch 823/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 25ms/step - accuracy: 0.7636 - loss: 0.7262\n",
+ "Epoch 824/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.7602 - loss: 0.7312\n",
+ "Epoch 825/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7614 - loss: 0.7367\n",
+ "Epoch 826/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7661 - loss: 0.7210\n",
+ "Epoch 827/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7634 - loss: 0.7229\n",
+ "Epoch 828/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7614 - loss: 0.7308\n",
+ "Epoch 829/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7656 - loss: 0.7191\n",
+ "Epoch 830/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7613 - loss: 0.7406\n",
+ "Epoch 831/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7558 - loss: 0.7526\n",
+ "Epoch 832/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.7665 - loss: 0.7133\n",
+ "Epoch 833/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7650 - loss: 0.7221\n",
+ "Epoch 834/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7603 - loss: 0.7370\n",
+ "Epoch 835/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7610 - loss: 0.7395\n",
+ "Epoch 836/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7640 - loss: 0.7204\n",
+ "Epoch 837/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7686 - loss: 0.7115\n",
+ "Epoch 838/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7642 - loss: 0.7224\n",
+ "Epoch 839/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 20ms/step - accuracy: 0.7569 - loss: 0.7521\n",
+ "Epoch 840/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7616 - loss: 0.7324\n",
+ "Epoch 841/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7629 - loss: 0.7368\n",
+ "Epoch 842/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7607 - loss: 0.7345\n",
+ "Epoch 843/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7646 - loss: 0.7221\n",
+ "Epoch 844/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7660 - loss: 0.7214\n",
+ "Epoch 845/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7704 - loss: 0.7098\n",
+ "Epoch 846/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7667 - loss: 0.7117\n",
+ "Epoch 847/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7538 - loss: 0.7598\n",
+ "Epoch 848/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7682 - loss: 0.7104\n",
+ "Epoch 849/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7664 - loss: 0.7157\n",
+ "Epoch 850/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7668 - loss: 0.7140\n",
+ "Epoch 851/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7621 - loss: 0.7282\n",
+ "Epoch 852/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7686 - loss: 0.7133\n",
+ "Epoch 853/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7644 - loss: 0.7229\n",
+ "Epoch 854/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7635 - loss: 0.7254\n",
+ "Epoch 855/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7667 - loss: 0.7163\n",
+ "Epoch 856/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7644 - loss: 0.7229\n",
+ "Epoch 857/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7657 - loss: 0.7257\n",
+ "Epoch 858/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7638 - loss: 0.7232\n",
+ "Epoch 859/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7621 - loss: 0.7298\n",
+ "Epoch 860/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7712 - loss: 0.7012\n",
+ "Epoch 861/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 20ms/step - accuracy: 0.7647 - loss: 0.7191\n",
+ "Epoch 862/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7699 - loss: 0.7087\n",
+ "Epoch 863/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7712 - loss: 0.7012\n",
+ "Epoch 864/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7676 - loss: 0.7135\n",
+ "Epoch 865/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7702 - loss: 0.7048\n",
+ "Epoch 866/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - accuracy: 0.7650 - loss: 0.7247\n",
+ "Epoch 867/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.7648 - loss: 0.7180\n",
+ "Epoch 868/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7654 - loss: 0.7147\n",
+ "Epoch 869/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7746 - loss: 0.6913\n",
+ "Epoch 870/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7609 - loss: 0.7374\n",
+ "Epoch 871/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7733 - loss: 0.6973\n",
+ "Epoch 872/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7691 - loss: 0.7080\n",
+ "Epoch 873/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7709 - loss: 0.7057\n",
+ "Epoch 874/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7684 - loss: 0.7126\n",
+ "Epoch 875/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7657 - loss: 0.7182\n",
+ "Epoch 876/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7700 - loss: 0.7019\n",
+ "Epoch 877/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7720 - loss: 0.6963\n",
+ "Epoch 878/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7716 - loss: 0.7052\n",
+ "Epoch 879/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7618 - loss: 0.7350\n",
+ "Epoch 880/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step - accuracy: 0.7660 - loss: 0.7200\n",
+ "Epoch 881/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 30ms/step - accuracy: 0.7684 - loss: 0.7079\n",
+ "Epoch 882/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 24ms/step - accuracy: 0.7751 - loss: 0.6918\n",
+ "Epoch 883/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7711 - loss: 0.7001\n",
+ "Epoch 884/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7721 - loss: 0.6984\n",
+ "Epoch 885/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7659 - loss: 0.7185\n",
+ "Epoch 886/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7714 - loss: 0.7027\n",
+ "Epoch 887/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7737 - loss: 0.6907\n",
+ "Epoch 888/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7725 - loss: 0.6960\n",
+ "Epoch 889/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7715 - loss: 0.6996\n",
+ "Epoch 890/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7687 - loss: 0.7103\n",
+ "Epoch 891/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7736 - loss: 0.6923\n",
+ "Epoch 892/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - accuracy: 0.7713 - loss: 0.7002\n",
+ "Epoch 893/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 22ms/step - accuracy: 0.7724 - loss: 0.7015\n",
+ "Epoch 894/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7700 - loss: 0.7066\n",
+ "Epoch 895/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7740 - loss: 0.6947\n",
+ "Epoch 896/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 21ms/step - accuracy: 0.7710 - loss: 0.6980\n",
+ "Epoch 897/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7727 - loss: 0.7015\n",
+ "Epoch 898/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7681 - loss: 0.7150\n",
+ "Epoch 899/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7748 - loss: 0.6898\n",
+ "Epoch 900/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7754 - loss: 0.6943\n",
+ "Epoch 901/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7748 - loss: 0.6935\n",
+ "Epoch 902/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7745 - loss: 0.6881\n",
+ "Epoch 903/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 21ms/step - accuracy: 0.7668 - loss: 0.7159\n",
+ "Epoch 904/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 26ms/step - accuracy: 0.7764 - loss: 0.6852\n",
+ "Epoch 905/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 21ms/step - accuracy: 0.7711 - loss: 0.7014\n",
+ "Epoch 906/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 24ms/step - accuracy: 0.7762 - loss: 0.6873\n",
+ "Epoch 907/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7724 - loss: 0.6949\n",
+ "Epoch 908/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7767 - loss: 0.6845\n",
+ "Epoch 909/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7721 - loss: 0.6996\n",
+ "Epoch 910/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7697 - loss: 0.7030\n",
+ "Epoch 911/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7759 - loss: 0.6891\n",
+ "Epoch 912/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7745 - loss: 0.6922\n",
+ "Epoch 913/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7758 - loss: 0.6839\n",
+ "Epoch 914/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7813 - loss: 0.6676\n",
+ "Epoch 915/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7775 - loss: 0.6826\n",
+ "Epoch 916/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7746 - loss: 0.6919\n",
+ "Epoch 917/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7734 - loss: 0.6918\n",
+ "Epoch 918/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7788 - loss: 0.6800\n",
+ "Epoch 919/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7801 - loss: 0.6750\n",
+ "Epoch 920/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7750 - loss: 0.6935\n",
+ "Epoch 921/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7833 - loss: 0.6644\n",
+ "Epoch 922/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7672 - loss: 0.7106\n",
+ "Epoch 923/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7683 - loss: 0.7097\n",
+ "Epoch 924/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7702 - loss: 0.7078\n",
+ "Epoch 925/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7716 - loss: 0.6917\n",
+ "Epoch 926/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7724 - loss: 0.6951\n",
+ "Epoch 927/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7750 - loss: 0.6893\n",
+ "Epoch 928/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7756 - loss: 0.6931\n",
+ "Epoch 929/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7804 - loss: 0.6709\n",
+ "Epoch 930/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7772 - loss: 0.6857\n",
+ "Epoch 931/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7772 - loss: 0.6870\n",
+ "Epoch 932/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7833 - loss: 0.6655\n",
+ "Epoch 933/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7796 - loss: 0.6828\n",
+ "Epoch 934/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7778 - loss: 0.6803\n",
+ "Epoch 935/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7811 - loss: 0.6652\n",
+ "Epoch 936/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7799 - loss: 0.6757\n",
+ "Epoch 937/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7685 - loss: 0.7107\n",
+ "Epoch 938/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7724 - loss: 0.7022\n",
+ "Epoch 939/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7731 - loss: 0.6956\n",
+ "Epoch 940/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7785 - loss: 0.6783\n",
+ "Epoch 941/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7796 - loss: 0.6712\n",
+ "Epoch 942/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7843 - loss: 0.6646\n",
+ "Epoch 943/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7794 - loss: 0.6741\n",
+ "Epoch 944/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7792 - loss: 0.6757\n",
+ "Epoch 945/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7812 - loss: 0.6713\n",
+ "Epoch 946/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7748 - loss: 0.6963\n",
+ "Epoch 947/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7767 - loss: 0.6819\n",
+ "Epoch 948/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7797 - loss: 0.6793\n",
+ "Epoch 949/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7837 - loss: 0.6565\n",
+ "Epoch 950/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7710 - loss: 0.7045\n",
+ "Epoch 951/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7826 - loss: 0.6664\n",
+ "Epoch 952/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7758 - loss: 0.6852\n",
+ "Epoch 953/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7840 - loss: 0.6677\n",
+ "Epoch 954/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7797 - loss: 0.6766\n",
+ "Epoch 955/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7816 - loss: 0.6709\n",
+ "Epoch 956/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7796 - loss: 0.6724\n",
+ "Epoch 957/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7850 - loss: 0.6615\n",
+ "Epoch 958/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7800 - loss: 0.6703\n",
+ "Epoch 959/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7842 - loss: 0.6584\n",
+ "Epoch 960/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7786 - loss: 0.6773\n",
+ "Epoch 961/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7824 - loss: 0.6675\n",
+ "Epoch 962/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7788 - loss: 0.6769\n",
+ "Epoch 963/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7830 - loss: 0.6668\n",
+ "Epoch 964/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7815 - loss: 0.6684\n",
+ "Epoch 965/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7756 - loss: 0.6929\n",
+ "Epoch 966/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7826 - loss: 0.6697\n",
+ "Epoch 967/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7847 - loss: 0.6611\n",
+ "Epoch 968/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7822 - loss: 0.6655\n",
+ "Epoch 969/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7764 - loss: 0.6854\n",
+ "Epoch 970/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7691 - loss: 0.7100\n",
+ "Epoch 971/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7823 - loss: 0.6683\n",
+ "Epoch 972/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7813 - loss: 0.6715\n",
+ "Epoch 973/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7831 - loss: 0.6638\n",
+ "Epoch 974/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7827 - loss: 0.6603\n",
+ "Epoch 975/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7763 - loss: 0.6917\n",
+ "Epoch 976/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7801 - loss: 0.6695\n",
+ "Epoch 977/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7829 - loss: 0.6704\n",
+ "Epoch 978/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7833 - loss: 0.6633\n",
+ "Epoch 979/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7861 - loss: 0.6590\n",
+ "Epoch 980/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7880 - loss: 0.6484\n",
+ "Epoch 981/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7803 - loss: 0.6711\n",
+ "Epoch 982/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7858 - loss: 0.6576\n",
+ "Epoch 983/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7811 - loss: 0.6707\n",
+ "Epoch 984/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7910 - loss: 0.6406\n",
+ "Epoch 985/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7826 - loss: 0.6669\n",
+ "Epoch 986/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7858 - loss: 0.6515\n",
+ "Epoch 987/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7903 - loss: 0.6440\n",
+ "Epoch 988/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7868 - loss: 0.6517\n",
+ "Epoch 989/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7840 - loss: 0.6643\n",
+ "Epoch 990/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7826 - loss: 0.6614\n",
+ "Epoch 991/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 22ms/step - accuracy: 0.7856 - loss: 0.6599\n",
+ "Epoch 992/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7857 - loss: 0.6560\n",
+ "Epoch 993/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7872 - loss: 0.6465\n",
+ "Epoch 994/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7809 - loss: 0.6715\n",
+ "Epoch 995/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7823 - loss: 0.6643\n",
+ "Epoch 996/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7853 - loss: 0.6553\n",
+ "Epoch 997/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 23ms/step - accuracy: 0.7831 - loss: 0.6673\n",
+ "Epoch 998/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 27ms/step - accuracy: 0.7848 - loss: 0.6628\n",
+ "Epoch 999/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7887 - loss: 0.6525\n",
+ "Epoch 1000/1000\n",
+ "\u001b[1m213/213\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 24ms/step - accuracy: 0.7818 - loss: 0.6688\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<keras.src.callbacks.history.History at 0x7faab8a29990>"
+ ]
+ },
+ "execution_count": 66,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model.fit(X,y,epochs=1000, batch_size=512)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 67,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def convert(inputStr):\n",
+ " nt = np.array([label_encoder.transform(list(inputStr))])\n",
+ " nextCharArray = model.predict(nt)\n",
+ " char = np.argmax(nextCharArray)\n",
+ " print(char)\n",
+ " character = label_encoder.inverse_transform([char])\n",
+ " return character[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%capture\n",
+ "def generate_text(initial_string, length):\n",
+ " text = initial_string\n",
+ " \n",
+ " for _ in range(length):\n",
+ " predicted_char = convert(text[-49:]); # Predict next character based on last 49 characters\n",
+ " text += predicted_char # Append predicted character to text\n",
+ " \n",
+ " return text # Return text with first character chopped off\n",
+ "\n",
+ "# Example usage:\n",
+ "initial_string = \"Love looks not with the eyes, but with the mind; \"\n",
+ "generated_text = generate_text(initial_string, 10000);"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Looks like it picked up some formatting stuff. This would have turned out better with tokenized words instead of characters / byte pair encoding, but not terrible. Happy with this. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Initial string:\n",
+ "Love looks not with the eyes, but with the mind; \n",
+ "\n",
+ "Generated text:\n",
+ "Love looks not with the eyes, but with the mind; be hor but Rut I madam, so and the nort!plagr ver\n",
+ " fr Atwiting thal haste but meadive yord,\n",
+ " Aete'droe our be his foorer and dis the louseles,\n",
+ " CUKEOLKO. The be wo prdell and head in your thate conse mears conses raid leep\n",
+ " Are boark shat he there the kis oust wond;\n",
+ " KORTEN wo conkinis, your donth. Shou you ray blanger of that ever haily me my\n",
+ " And hiver gor you did vent for shat tom.\n",
+ " The flse. [s H gumde Nyeeldss Tranher and plakous follent of enbopne greaks,\n",
+ " Qet I,\n",
+ " eou whom food H rleade.\n",
+ " fo meady sear whou his but our him him mead of 'nrarpy; I lade your 'tint kifhr the him gream,\n",
+ " You\n",
+ "your on me lieelint me greed leaven 'tweeled.\n",
+ " AUKILODSUS GOYIN. Sir will oppple for place qur laver had glow.\n",
+ " And be then lead, and hood that me oneak thall\n",
+ " Eres did then that your worl him them his shall the poth fefomd floor fore holy is meart nore\n",
+ " BUCBE. In in be when arte lore me pld vo seves of ouch speak'd Eor. Suchbudin for lanc, and we ly lounte\n",
+ " RUINC. I arese Dorther thank to his shall ment the camnow? that H Uhe hone not the I'll comf and we rhee mour\n",
+ " Wo Alad, and when rould coneal, where and that, the gromany\n",
+ " he hart that your ae enthers enth ind.\n",
+ "almkon'straingte.\n",
+ " Whe caus rhall 'pl good the all they our to fall is hore\n",
+ " shat Arche henute whichers bute the hone\n",
+ " That in Hemilow keart and you morth.lifece\n",
+ " Bllole but rhght. the judde. inow shas, phe leed yould cause you theme foores your fore them thus leave; dond enooury.\n",
+ "\n",
+ " Or I ll your our willance willing andde the lavifous and The.\n",
+ " Gle evertant;\n",
+ " [ouse CLINA. MAGVD Cngek.nis entn the othend. Butloy it.\n",
+ " TICE will be that nord the dids me last the toote hs that world nore force the the he\n",
+ " js mus yrete the will. Wo thee in wo my bonclve own lan the good ouret. Wo the yould!the he salk.\n",
+ " So afink Rupm will gor pot our full ordje\n",
+ " Drew his ind loow Crac, onow meady,\n",
+ " Whent Wheir shat ne dond Ie'noree will mord his lawfh.\n",
+ " For all en lord our lead vell I then are\n",
+ "dist heart gor Lartaint be worl thme the\n",
+ " she Kod, whereinioless love, and deaute your hiscer noun goly le commk himbes.\n",
+ " Exeorip 'Uo, HIRTNE. ol likl erose owndl.\n",
+ " Rhat bour mady and wort place his thes thous forture the for veeker the glow the conk\n",
+ " rpeaud that the inve my pur own did Rhrmad but forth.\n",
+ " And out; I bo, soon enoole.\n",
+ " DHAYERDHUS. Ieadure but be that worived had nore\n",
+ " Therest the thes and lov long and furin,\n",
+ " Shat vill thall un the the Ely lelaud lepple,\n",
+ " The nore it did did and soeard the cut grait.\n",
+ " heat Nenres gind the mores world mevens\n",
+ " Inttre shank gor and gready me.\n",
+ " Flsheoress me the flow. wire hive thus are mounting shale forw weres foorp]n, I'll fid,\n",
+ " puhce the mead met heaue his theer wilt my shghr me le' inu'stres,\n",
+ " IUCEOTDUD. Shis mear. The I wilt innour not dome oure,\n",
+ " Exeunn, 2 VF ouo s, No, cope, anun Iing 'toorec,\n",
+ " you likend he his a Liros his the Iingu.way\n",
+ " thand you the vidir forwer le thus aloughte\n",
+ " That in nore, I dauge our the worl as soul\n",
+ " lad- Now,\n",
+ " Bould be ourier; tou, tir, qlat oure fore gor they your than\n",
+ " Br out hive Iol nore angling to Roune\n",
+ " Aut thall me inds of that le he head willef!by assuage ente that op gr the po that hinq me.\n",
+ " Er awack thant shank honour. I'lave good fol.\n",
+ " In hntr shghr partaane and pl a eo meadiarabl hive the hold his Geroos worl,\n",
+ " Cumide is eveed own a commg,\n",
+ " PIAMNAL. You wour the to she the molyarious the coood.\n",
+ " The veepe in my the morth.\n",
+ " And voldier ares conde I ever me denule.\n",
+ " And that your bud lany our your than kint lasuatity\n",
+ " rhank histue ind in reass in thall shere.\n",
+ " Thee vell northers soun his hivers anded,\n",
+ " So shane and spet world our yord, mee, and Emtert indee no gis shers fore hor ae,\n",
+ " Nf un lontherse lov a at gr plase'righp\n",
+ " Why G but Iind!kin grouels,\n",
+ " Anr. It th'line but his yords ott him.\n",
+ " KATSUAMLO. I toore with vere dand thank rould but he veem ginnnwar'dis ind,\n",
+ " Ker Iing 'toough I nonkoy a will!deed, I -To gor a qere they thall youre noun lake honour'd\n",
+ " Are that even ind fls his away yourier and meas thme given owlt. anddd, wherefort.\n",
+ " A would rakes sould sould the these shght.\n",
+ " Aut wort lad at honeomous of thall wise,\n",
+ " Bnd will of Edwe shre of ae leaven sould I\n",
+ "HRTIUS\n",
+ " Nov heave but the woo rureome.\n",
+ " All inow shall gone your in under.\n",
+ " All hiven; whek flsend will in thall, Aly ruick hiven freakl the that will 'pr live.\n",
+ " EUCT. Ifs shghr 'to youldr thate her he honjus meepnd gre ralt be furn him. I';N. Uhe meed word, will undert werent the to gonoury.tkeant.\n",
+ " WORKLA. But wolance and shat shall that the fulbe gore\n",
+ " THRWAR. I qead qatury have confec chere'd\n",
+ "fulnce his no histros.\n",
+ " TIIREY. ind lear a shance gorther, Uho somes your haith'day, I\n",
+ "prous me away head enjen your heave the rature his rhat wile hatures,\n",
+ " For shgir Nenmow nore not conkoled.\n",
+ " Whe Drporer ousther, al Ieroos and his dist,\n",
+ " Ft is Fribey dpate le parry can wrgck will wogce.\n",
+ " AGTPW. I se nore you Ty chediar of shgh hivet of the she it.\n",
+ " thshar'd his they thall ye mord his bloow to heave;\n",
+ " and inddr.\n",
+ " Enthhn Linkles K'larks\n",
+ " AHRENTER.\n",
+ "Vho wese,\n",
+ " IINUNSHY. Ie klm likes to thall but any shght his groms the themed.\n",
+ " Asince you Jeout of the paee entrel you;\n",
+ " Ay find moretuing do that well shath,\n",
+ " Ee wou co rene alongt.\n",
+ " FXEYARA. Shoeade. it you thght a bome, and grow his shey'drounn. \" And I, Sur the somend that a my thall.\n",
+ "that shan woo thint heart.\n",
+ " Fomdue qale roore deed meave wooan meture\n",
+ " STEIO. Yhis the lin'd wo sherefores Nile deent fore heave\n",
+ " his him her leavefo?\n",
+ " QPDOD. H haither Sardores, by your your of your your fore nore the Mrace,\n",
+ " Eo undak and therefort hivert H ever they lonshonnne\n",
+ " Uhat land they libe KAO. There qentled lolyant hive wogecere.\n",
+ " Cull thou your indeio'd,\n",
+ " MENIARA. N, that I weat dall hivert thatt Ptcing his word.\n",
+ " Harded leadiar, and lany and your you and nore.\n",
+ " WALEUIUE. There Drains Iall met away they corwer'stranity\n",
+ " IUKEO. B SHII. As noreed uhe mear mears leard he hiven\n",
+ " Fan in in deart of conder with the glow.\n",
+ " Dore H,hair call concer that that your me ouchte\n",
+ " Ooly, that life the mawfht.all thather him.\n",
+ " BIOGAEAS. A aehr nore the shat my that you you there.\n",
+ " SLSTAEA. She bomse me and I be oure. ke lord;\n",
+ " PECODARIOLE. Cein I thank would shat enor, for holy I woldct that shan shn the kind;\n",
+ " CHRD. The hath lore I will lover thank his your the anyar'redo meave:\n",
+ " With oay rhghr sowers of ourt that aroos.\n",
+ " Ee wou coret le s runrone. qhek'stoues.\n",
+ " Now ousuer thackevent shath, wheinady his throre and hor of she nore fonee!nore forthers.\n",
+ " So pr you dond othell J were, let flikuer,\n",
+ " FINGNIUS. gor J thre how Hrages that they the hinne?\n",
+ " Dego, vhere the the shall let of doneated yould!northe nore nore thallons.\n",
+ " urut and world me be they unto lord of hentle hiver, I knig meast bed soun 'tin kead himdnd hind; Po, gead and kind othn my gr at leart of him.\n",
+ " Way, alth lear vent phght lovert hndeo\n",
+ " Foreed? that donved will woo grew his ther,\n",
+ " Onlear our Entpe at vo thene will your forwer and meft, and he beat by hiver,\n",
+ " [AAOUOIAUS,\n",
+ "\n",
+ " POCDRS OROIB, FNMEET ORLVONNA.\n",
+ "be hullo plack'd JUTIBS a thank saids in Palber. shat and nore buteled learge of lore\n",
+ " But every wourest the that he is veee.\n",
+ " Hf is wond inow of the me therefous le nore.\n",
+ " SCABSHHEEAT, BX xouralle. Bom will our upeee of hivence meav,\n",
+ " And with of woother, head but gulbe but love.\n",
+ " Bends nwn opdeed any leav, and nort and gre]k'd in the mountry\n",
+ " Ay hindorent of hir nove. whose meft E;ERT. I LLRD. Gatdnd 'gaughts fo will in of hight I wise world lovng.\n",
+ " The Evken that indeding brt fore thant nore nord of bed hour'd, oument\n",
+ " AUis be that fulnce I 'Uhere'stite and anuld; Tarriers the in the noun of meavon.\n",
+ " AUCKIA. Well is thall. Sho'stat world enodrence hone bomma, and his the his the gloour, and glong long his hone him conk bream, I a wour shall will of heard the and mord, andasity\n",
+ " Ere bond meaven and here leave the eve lous leavioury. I'hathers and the bre the and Iing ind.\n",
+ " Bllole and Aattert world five therefoe hold him more.\n",
+ " SPEIARD. My leave a gor thght conse they le the thall thent in thout en mear world as Ooow of heaverend of elponishorn. Shou ' Ae Sow reou my the conspink love eay, in than hear and word the bud Wheice well and gloughte honour'd,droughted?\n",
+ " VALINOPW\n",
+ "\n",
+ " rwn hivenus nord Iing sould you head 'pet grincer anged nount. I havfht. Cunmay and ind.\n",
+ " Wo lll me my mavill 'tweet d loul a an they of they than noven Rrancest. meegce.likev. the bud but counte of\n",
+ " Eareed, ro his your your thall, wourer word,\n",
+ " must woo you. so hnol me the at pur the Bblent thuse in the lan move mead meavorf.\n",
+ " FIOTT OF H'lad, I havil''\n",
+ "Bll hive hath of morroink? How lor the or ly flsthers lead is more\n",
+ " And the find veem for 'toorent that his in liness wooves,\n",
+ " Tpree arpee I oake as shght welcer in ouree.\n",
+ " rould le and and otst donth. I wou hivent nord, thereforess they cond for oust ente thuless.\n",
+ " you!racey me bll wither youre plut thee?\n",
+ " Aeauge Vilapp veee, there he nord him;\n",
+ " [FRNDUDUS. uhe THRNISICTE SOLEILE OORHIS and old sould we North of Proild where forther,\n",
+ " hole cond then wishad- Or hear worm not,\n",
+ " Winu nothered pothort in louse vere for plalce,\n",
+ " Aut our eyent than you my ounour ralk kin leanions you;\n",
+ " And Iese, I pash love, the nor boun lan b.\n",
+ " Fady, Kere's me hales le the will for place.\n",
+ "de every upeakev they thall\n",
+ "\n",
+ " shrv in that thall the youry. and einse lour wear of dier lan uhen all woone, a\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Initial string:\")\n",
+ "print(initial_string)\n",
+ "print(\"\\nGenerated text:\")\n",
+ "print(generated_text)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#model.save('../models/TextGenDeepNeuralNetwork.keras')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": ".venv",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
