NaivePruningFashionNoDropout.ipynb - machinelearning

NaivePruningFashionNoDropout.ipynb (158289B)
      1 {
      2  "cells": [
      3   {
      4    "cell_type": "markdown",
      5    "metadata": {},
      6    "source": [
      7     "* 89.38% Accuracy on test set before pruning\n",
      8     "* 88.13% Accuracy on test set after pruning arbitrary neurons\n",
      9     "\n",
     10     "The degradation in accuracy here was 1.25% when pruning ~2.4% of neurons compared to the .23% when using dropout."
     11    ]
     12   },
     13   {
     14    "cell_type": "code",
     15    "execution_count": 27,
     16    "metadata": {},
     17    "outputs": [
     18     {
     19      "data": {
     20       "text/plain": [
     21        "1.25"
     22       ]
     23      },
     24      "execution_count": 27,
     25      "metadata": {},
     26      "output_type": "execute_result"
     27     }
     28    ],
     29    "source": [
     30     "89.38 - 88.13"
     31    ]
     32   },
     33   {
     34    "cell_type": "code",
     35    "execution_count": 1,
     36    "metadata": {},
     37    "outputs": [],
     38    "source": [
     39     "import pandas as pd"
     40    ]
     41   },
     42   {
     43    "cell_type": "code",
     44    "execution_count": 2,
     45    "metadata": {},
     46    "outputs": [],
     47    "source": [
     48     "train_set = pd.read_csv('../datasets/fashion/fashion-mnist_train.csv')\n",
     49     "test_set = pd.read_csv('../datasets/fashion/fashion-mnist_test.csv')"
     50    ]
     51   },
     52   {
     53    "cell_type": "code",
     54    "execution_count": 3,
     55    "metadata": {},
     56    "outputs": [],
     57    "source": [
     58     "y_train = train_set['label']\n",
     59     "X_train = train_set.drop(axis=1, labels=['label'])\n",
     60     "\n",
     61     "y_test = test_set['label']\n",
     62     "X_test = test_set.drop(axis=1, labels=['label'])"
     63    ]
     64   },
     65   {
     66    "cell_type": "code",
     67    "execution_count": 4,
     68    "metadata": {},
     69    "outputs": [],
     70    "source": [
     71     "from sklearn.preprocessing import OneHotEncoder\n",
     72     "import numpy as np\n",
     73     "\n",
     74     "ohec = OneHotEncoder(sparse_output=False)\n",
     75     "\n",
     76     "y_train = np.array(y_train)\n",
     77     "y_test = np.array(y_test)\n",
     78     "\n",
     79     "y_train = y_train.reshape(-1,1)\n",
     80     "y_test = y_test.reshape(-1,1)\n",
     81     "\n",
     82     "\n",
     83     "y_train = ohec.fit_transform(y_train)\n",
     84     "y_test = ohec.fit_transform(y_test)"
     85    ]
     86   },
     87   {
     88    "cell_type": "code",
     89    "execution_count": 5,
     90    "metadata": {},
     91    "outputs": [
     92     {
     93      "name": "stderr",
     94      "output_type": "stream",
     95      "text": [
     96       "2024-10-23 16:21:47.399066: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
     97       "2024-10-23 16:21:47.402499: I external/local_tsl/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
     98       "2024-10-23 16:21:47.437251: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
     99       "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
    100       "2024-10-23 16:21:48.269808: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
    101       "2024-10-23 16:21:48.798517: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
    102       "2024-10-23 16:21:48.799002: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
    103       "Skipping registering GPU devices...\n"
    104      ]
    105     },
    106     {
    107      "data": {
    108       "text/html": [
    109        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
    110        "</pre>\n"
    111       ],
    112       "text/plain": [
    113        "\u001b[1mModel: \"sequential\"\u001b[0m\n"
    114       ]
    115      },
    116      "metadata": {},
    117      "output_type": "display_data"
    118     },
    119     {
    120      "data": {
    121       "text/html": [
    122        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
    123        "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
    124        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
    125        "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">401,920</span> │\n",
    126        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    127        "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">262,656</span> │\n",
    128        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    129        "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
    130        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
    131        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    132        "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,328</span> │\n",
    133        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    134        "│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
    135        "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
    136        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    137        "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</span> │\n",
    138        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    139        "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
    140        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
    141        "</pre>\n"
    142       ],
    143       "text/plain": [
    144        "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
    145        "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
    146        "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
    147        "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m401,920\u001b[0m │\n",
    148        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    149        "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m262,656\u001b[0m │\n",
    150        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    151        "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │         \u001b[38;5;34m2,048\u001b[0m │\n",
    152        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
    153        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    154        "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m131,328\u001b[0m │\n",
    155        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    156        "│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m1,024\u001b[0m │\n",
    157        "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
    158        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    159        "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\u001b[0m │\n",
    160        "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
    161        "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,290\u001b[0m │\n",
    162        "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
    163       ]
    164      },
    165      "metadata": {},
    166      "output_type": "display_data"
    167     },
    168     {
    169      "data": {
    170       "text/html": [
    171        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">833,162</span> (3.18 MB)\n",
    172        "</pre>\n"
    173       ],
    174       "text/plain": [
    175        "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m833,162\u001b[0m (3.18 MB)\n"
    176       ]
    177      },
    178      "metadata": {},
    179      "output_type": "display_data"
    180     },
    181     {
    182      "data": {
    183       "text/html": [
    184        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">831,626</span> (3.17 MB)\n",
    185        "</pre>\n"
    186       ],
    187       "text/plain": [
    188        "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m831,626\u001b[0m (3.17 MB)\n"
    189       ]
    190      },
    191      "metadata": {},
    192      "output_type": "display_data"
    193     },
    194     {
    195      "data": {
    196       "text/html": [
    197        "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,536</span> (6.00 KB)\n",
    198        "</pre>\n"
    199       ],
    200       "text/plain": [
    201        "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,536\u001b[0m (6.00 KB)\n"
    202       ]
    203      },
    204      "metadata": {},
    205      "output_type": "display_data"
    206     }
    207    ],
    208    "source": [
    209     "import keras\n",
    210     "import tensorflow as tf\n",
    211     "\n",
    212     "model = keras.Sequential(layers=[\n",
    213     "    keras.layers.Input(shape=(784,)),\n",
    214     "    keras.layers.Dense(512, activation='relu'),\n",
    215     "    keras.layers.Dense(512, activation='relu'),\n",
    216     "    keras.layers.BatchNormalization(),\n",
    217     "    keras.layers.Dense(256, activation='relu'),\n",
    218     "    keras.layers.BatchNormalization(),\n",
    219     "    keras.layers.Dense(128, activation='relu'),\n",
    220     "    keras.layers.Dense(10, activation='softmax')\n",
    221     "    ]\n",
    222     ")\n",
    223     "model.summary()"
    224    ]
    225   },
    226   {
    227    "cell_type": "code",
    228    "execution_count": 6,
    229    "metadata": {},
    230    "outputs": [],
    231    "source": [
    232     "model.compile(loss=keras.losses.CategoricalCrossentropy, optimizer='adam', metrics=['accuracy'])"
    233    ]
    234   },
    235   {
    236    "cell_type": "code",
    237    "execution_count": 7,
    238    "metadata": {},
    239    "outputs": [
    240     {
    241      "name": "stdout",
    242      "output_type": "stream",
    243      "text": [
    244       "Epoch 1/50\n",
    245       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 34ms/step - accuracy: 0.7436 - loss: 0.7600\n",
    246       "Epoch 2/50\n",
    247       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 0.8742 - loss: 0.3466\n",
    248       "Epoch 3/50\n",
    249       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 31ms/step - accuracy: 0.8841 - loss: 0.3191\n",
    250       "Epoch 4/50\n",
    251       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 31ms/step - accuracy: 0.8898 - loss: 0.3002\n",
    252       "Epoch 5/50\n",
    253       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 30ms/step - accuracy: 0.8963 - loss: 0.2792\n",
    254       "Epoch 6/50\n",
    255       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 30ms/step - accuracy: 0.9011 - loss: 0.2654\n",
    256       "Epoch 7/50\n",
    257       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 36ms/step - accuracy: 0.9096 - loss: 0.2427\n",
    258       "Epoch 8/50\n",
    259       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 36ms/step - accuracy: 0.9152 - loss: 0.2296\n",
    260       "Epoch 9/50\n",
    261       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 36ms/step - accuracy: 0.9202 - loss: 0.2136\n",
    262       "Epoch 10/50\n",
    263       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 38ms/step - accuracy: 0.9195 - loss: 0.2145\n",
    264       "Epoch 11/50\n",
    265       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 0.9051 - loss: 0.2516\n",
    266       "Epoch 12/50\n",
    267       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 0.9167 - loss: 0.2240\n",
    268       "Epoch 13/50\n",
    269       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 0.9199 - loss: 0.2136\n",
    270       "Epoch 14/50\n",
    271       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 0.9212 - loss: 0.2095\n",
    272       "Epoch 15/50\n",
    273       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 30ms/step - accuracy: 0.9244 - loss: 0.2002\n",
    274       "Epoch 16/50\n",
    275       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.9306 - loss: 0.1820\n",
    276       "Epoch 17/50\n",
    277       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 0.9370 - loss: 0.1666\n",
    278       "Epoch 18/50\n",
    279       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9393 - loss: 0.1610\n",
    280       "Epoch 19/50\n",
    281       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 29ms/step - accuracy: 0.9433 - loss: 0.1514\n",
    282       "Epoch 20/50\n",
    283       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 39ms/step - accuracy: 0.9451 - loss: 0.1455\n",
    284       "Epoch 21/50\n",
    285       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 0.9469 - loss: 0.1426\n",
    286       "Epoch 22/50\n",
    287       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 31ms/step - accuracy: 0.9520 - loss: 0.1276\n",
    288       "Epoch 23/50\n",
    289       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 30ms/step - accuracy: 0.9542 - loss: 0.1229\n",
    290       "Epoch 24/50\n",
    291       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 0.9575 - loss: 0.1158\n",
    292       "Epoch 25/50\n",
    293       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 41ms/step - accuracy: 0.9605 - loss: 0.1082\n",
    294       "Epoch 26/50\n",
    295       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 0.9628 - loss: 0.1022\n",
    296       "Epoch 27/50\n",
    297       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9627 - loss: 0.1009\n",
    298       "Epoch 28/50\n",
    299       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 33ms/step - accuracy: 0.9649 - loss: 0.0921\n",
    300       "Epoch 29/50\n",
    301       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - accuracy: 0.9661 - loss: 0.0931\n",
    302       "Epoch 30/50\n",
    303       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 0.9677 - loss: 0.0866\n",
    304       "Epoch 31/50\n",
    305       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 29ms/step - accuracy: 0.9709 - loss: 0.0800\n",
    306       "Epoch 32/50\n",
    307       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9671 - loss: 0.0872\n",
    308       "Epoch 33/50\n",
    309       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 0.9708 - loss: 0.0770\n",
    310       "Epoch 34/50\n",
    311       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9742 - loss: 0.0705\n",
    312       "Epoch 35/50\n",
    313       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 32ms/step - accuracy: 0.9739 - loss: 0.0672\n",
    314       "Epoch 36/50\n",
    315       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 0.9783 - loss: 0.0586\n",
    316       "Epoch 37/50\n",
    317       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 0.9752 - loss: 0.0657\n",
    318       "Epoch 38/50\n",
    319       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 0.9815 - loss: 0.0523\n",
    320       "Epoch 39/50\n",
    321       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 0.9785 - loss: 0.0556\n",
    322       "Epoch 40/50\n",
    323       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 33ms/step - accuracy: 0.9809 - loss: 0.0521\n",
    324       "Epoch 41/50\n",
    325       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 34ms/step - accuracy: 0.9812 - loss: 0.0519\n",
    326       "Epoch 42/50\n",
    327       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9805 - loss: 0.0522\n",
    328       "Epoch 43/50\n",
    329       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - accuracy: 0.9803 - loss: 0.0527\n",
    330       "Epoch 44/50\n",
    331       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9816 - loss: 0.0484\n",
    332       "Epoch 45/50\n",
    333       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 0.9838 - loss: 0.0462\n",
    334       "Epoch 46/50\n",
    335       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 35ms/step - accuracy: 0.9857 - loss: 0.0394\n",
    336       "Epoch 47/50\n",
    337       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 36ms/step - accuracy: 0.9832 - loss: 0.0448\n",
    338       "Epoch 48/50\n",
    339       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - accuracy: 0.9876 - loss: 0.0357\n",
    340       "Epoch 49/50\n",
    341       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9842 - loss: 0.0421\n",
    342       "Epoch 50/50\n",
    343       "\u001b[1m60/60\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 32ms/step - accuracy: 0.9821 - loss: 0.0478\n"
    344      ]
    345     },
    346     {
    347      "data": {
    348       "text/plain": [
    349        "<keras.src.callbacks.history.History at 0x7fee5a966bd0>"
    350       ]
    351      },
    352      "execution_count": 7,
    353      "metadata": {},
    354      "output_type": "execute_result"
    355     }
    356    ],
    357    "source": [
    358     "model.fit(x=X_train, y=y_train, batch_size=1000, epochs=50)"
    359    ]
    360   },
    361   {
    362    "cell_type": "code",
    363    "execution_count": 8,
    364    "metadata": {},
    365    "outputs": [
    366     {
    367      "name": "stdout",
    368      "output_type": "stream",
    369      "text": [
    370       "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"
    371      ]
    372     }
    373    ],
    374    "source": [
    375     "y_test_pred = model.predict(x=X_test)"
    376    ]
    377   },
    378   {
    379    "cell_type": "code",
    380    "execution_count": 9,
    381    "metadata": {},
    382    "outputs": [
    383     {
    384      "data": {
    385       "text/plain": [
    386        "array([9.9859405e-01, 7.9753104e-10, 6.4784399e-07, 9.1048644e-09,\n",
    387        "       1.3203351e-05, 6.1672427e-09, 1.3919529e-03, 3.4279146e-10,\n",
    388        "       1.6498035e-07, 1.3554866e-10], dtype=float32)"
    389       ]
    390      },
    391      "execution_count": 9,
    392      "metadata": {},
    393      "output_type": "execute_result"
    394     }
    395    ],
    396    "source": [
    397     "y_test_pred[0]"
    398    ]
    399   },
    400   {
    401    "cell_type": "code",
    402    "execution_count": 10,
    403    "metadata": {},
    404    "outputs": [
    405     {
    406      "data": {
    407       "text/plain": [
    408        "array([1., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
    409       ]
    410      },
    411      "execution_count": 10,
    412      "metadata": {},
    413      "output_type": "execute_result"
    414     }
    415    ],
    416    "source": [
    417     "y_test[0]"
    418    ]
    419   },
    420   {
    421    "cell_type": "code",
    422    "execution_count": 11,
    423    "metadata": {},
    424    "outputs": [],
    425    "source": [
    426     "y_test_pred_proper = []\n",
    427     "\n",
    428     "for i in y_test_pred:\n",
    429     "    y_test_pred_proper.append(i.argmax())"
    430    ]
    431   },
    432   {
    433    "cell_type": "code",
    434    "execution_count": 12,
    435    "metadata": {},
    436    "outputs": [],
    437    "source": [
    438     "trueY = []\n",
    439     "for i in y_test:\n",
    440     "    trueY.append(i.argmax())"
    441    ]
    442   },
    443   {
    444    "cell_type": "code",
    445    "execution_count": 13,
    446    "metadata": {},
    447    "outputs": [
    448     {
    449      "data": {
    450       "text/plain": [
    451        "[0, 1, 2, 2, 3, 6, 8, 6, 5, 0]"
    452       ]
    453      },
    454      "execution_count": 13,
    455      "metadata": {},
    456      "output_type": "execute_result"
    457     }
    458    ],
    459    "source": [
    460     "y_test_pred_proper[:10]"
    461    ]
    462   },
    463   {
    464    "cell_type": "code",
    465    "execution_count": 14,
    466    "metadata": {},
    467    "outputs": [
    468     {
    469      "data": {
    470       "text/plain": [
    471        "[0, 1, 2, 2, 3, 2, 8, 6, 5, 0]"
    472       ]
    473      },
    474      "execution_count": 14,
    475      "metadata": {},
    476      "output_type": "execute_result"
    477     }
    478    ],
    479    "source": [
    480     "trueY[:10]"
    481    ]
    482   },
    483   {
    484    "cell_type": "code",
    485    "execution_count": 15,
    486    "metadata": {},
    487    "outputs": [
    488     {
    489      "data": {
    490       "text/plain": [
    491        "0.8938"
    492       ]
    493      },
    494      "execution_count": 15,
    495      "metadata": {},
    496      "output_type": "execute_result"
    497     }
    498    ],
    499    "source": [
    500     "from sklearn.metrics import accuracy_score\n",
    501     "\n",
    502     "accuracy_score(y_true=trueY, y_pred=y_test_pred_proper)"
    503    ]
    504   },
    505   {
    506    "cell_type": "code",
    507    "execution_count": 16,
    508    "metadata": {},
    509    "outputs": [
    510     {
    511      "name": "stdout",
    512      "output_type": "stream",
    513      "text": [
    514       "[[839   0  19  23   5   2 107   0   5   0]\n",
    515       " [  2 993   1   4   0   0   0   0   0   0]\n",
    516       " [ 13   1 768  15 123   0  79   0   1   0]\n",
    517       " [ 12  24   6 885  47   0  23   1   2   0]\n",
    518       " [  1   1  47  22 886   0  42   0   1   0]\n",
    519       " [  1   0   0   1   0 956   1  16   3  22]\n",
    520       " [ 96   5  62  26  75   0 732   0   4   0]\n",
    521       " [  0   0   0   0   0  17   0 941   1  41]\n",
    522       " [  3   0   4   3   2   2   9   1 976   0]\n",
    523       " [  0   0   0   0   0   6   1  28   3 962]]\n"
    524      ]
    525     }
    526    ],
    527    "source": [
    528     "from sklearn.metrics import confusion_matrix\n",
    529     "\n",
    530     "cm = confusion_matrix(y_true=trueY, y_pred=y_test_pred_proper)\n",
    531     "print(cm)"
    532    ]
    533   },
    534   {
    535    "cell_type": "code",
    536    "execution_count": 17,
    537    "metadata": {},
    538    "outputs": [
    539     {
    540      "data": {
    541       "image/png": "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",
    542       "text/plain": [
    543        "<Figure size 640x480 with 2 Axes>"
    544       ]
    545      },
    546      "metadata": {},
    547      "output_type": "display_data"
    548     }
    549    ],
    550    "source": [
    551     "import matplotlib.pyplot as plt\n",
    552     "import seaborn as sns\n",
    553     "\n",
    554     "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    555     "plt.xlabel('Predicted')\n",
    556     "plt.ylabel('Actual')\n",
    557     "plt.title('Confusion Matrix')\n",
    558     "plt.show()\n"
    559    ]
    560   },
    561   {
    562    "cell_type": "markdown",
    563    "metadata": {},
    564    "source": [
    565     "* 0 T-shirt/top\n",
    566     "* 1 Trouser\n",
    567     "* 2 Pullover\n",
    568     "* 3 Dress\n",
    569     "* 4 Coat\n",
    570     "* 5 Sandal\n",
    571     "* 6 Shirt\n",
    572     "* 7 Sneaker\n",
    573     "* 8 Bag\n",
    574     "* 9 Ankle boot"
    575    ]
    576   },
    577   {
    578    "cell_type": "markdown",
    579    "metadata": {},
    580    "source": [
    581     "1408 neurons * .024 percent ~= 34 neurons"
    582    ]
    583   },
    584   {
    585    "cell_type": "code",
    586    "execution_count": 18,
    587    "metadata": {},
    588    "outputs": [
    589     {
    590      "data": {
    591       "text/plain": [
    592        "[array([[ 0.10132942, -0.09994747,  0.0544187 , ..., -0.01199088,\n",
    593        "         -0.06292131,  0.01987181],\n",
    594        "        [-0.03616156, -0.07451397,  0.01944981, ...,  0.01166514,\n",
    595        "          0.02126038, -0.0748473 ],\n",
    596        "        [ 0.01550752,  0.0909953 ,  0.04380812, ..., -0.07962617,\n",
    597        "          0.01898236,  0.07236466],\n",
    598        "        ...,\n",
    599        "        [ 0.0108054 ,  0.05861497,  0.17087871, ...,  0.03839699,\n",
    600        "          0.09123042, -0.06567019],\n",
    601        "        [ 0.00497013, -0.08016764, -0.06656981, ..., -0.00515878,\n",
    602        "          0.00221761,  0.05097136],\n",
    603        "        [-0.02290533, -0.03354137,  0.04545495, ...,  0.01482573,\n",
    604        "          0.00098484, -0.04111394]], dtype=float32),\n",
    605        " array([ 3.48618440e-02, -5.41638955e-03,  1.31103210e-02,  7.22463280e-02,\n",
    606        "        -2.43876502e-02, -1.06870309e-02,  2.91081183e-02,  2.90576406e-02,\n",
    607        "         2.62515862e-02,  1.45024937e-02, -1.57963522e-02,  1.49091119e-02,\n",
    608        "         8.93487409e-03,  4.12793346e-02,  2.95282118e-02,  5.19331312e-03,\n",
    609        "         1.32530916e-03, -3.14759538e-02,  4.28747246e-03,  7.10726976e-02,\n",
    610        "        -2.24813707e-02,  2.92180516e-02, -5.23487013e-03,  8.60791206e-02,\n",
    611        "        -2.18736026e-02,  1.45187313e-02, -1.80636141e-02,  6.63637221e-02,\n",
    612        "         1.71002783e-02, -1.99741162e-02,  6.77409815e-03, -2.29119249e-02,\n",
    613        "        -2.11065151e-02, -4.21732739e-02,  5.66550791e-02, -7.87500665e-03,\n",
    614        "         4.96934354e-02, -2.62451507e-02, -3.14179659e-02,  6.30049482e-02,\n",
    615        "         8.19519460e-02,  1.55785831e-03,  4.29074429e-02,  3.24573219e-02,\n",
    616        "         1.79940443e-02,  1.36072130e-03,  5.86371124e-02,  3.61156873e-02,\n",
    617        "        -1.09881293e-02,  4.32750061e-02, -2.25960705e-02, -8.04545358e-03,\n",
    618        "        -1.53334010e-02, -3.28587070e-02,  3.13267857e-03, -1.86315607e-02,\n",
    619        "        -4.37643602e-02,  3.81392762e-02,  3.66224125e-02,  1.48072718e-02,\n",
    620        "         3.44659165e-02, -1.13196466e-02, -2.27169134e-02,  8.48719776e-02,\n",
    621        "         3.10068321e-03,  1.39202345e-02,  9.27911773e-02,  7.53409788e-02,\n",
    622        "        -2.16330979e-02, -2.20414903e-02, -3.15212552e-03,  1.59236938e-02,\n",
    623        "        -2.42054686e-02, -1.75593980e-02, -2.21934468e-02, -3.70963216e-02,\n",
    624        "         6.72085881e-02,  3.21805626e-02,  2.90451963e-02, -1.29152099e-02,\n",
    625        "        -1.29320752e-02,  4.36900444e-02, -2.44683400e-02,  3.23325917e-02,\n",
    626        "        -1.76756456e-02, -5.36627956e-02,  4.35329936e-02,  2.40565222e-02,\n",
    627        "         4.36999649e-02, -2.26819180e-02, -8.69834423e-03,  6.07701577e-02,\n",
    628        "        -2.18553673e-02, -3.17634009e-02,  2.49652676e-02, -4.14558090e-02,\n",
    629        "         4.81878147e-02, -3.10613662e-02,  6.24261843e-03,  4.90439422e-02,\n",
    630        "        -1.63377635e-02, -9.88943875e-03,  5.74486889e-02, -3.52662057e-02,\n",
    631        "        -3.21774445e-02,  3.78112569e-02, -3.94279137e-02, -4.42866087e-02,\n",
    632        "         9.20713786e-03, -2.36647036e-02, -1.01757804e-02, -5.47396392e-02,\n",
    633        "        -6.01586420e-03,  1.01622276e-01, -1.10045085e-02, -4.08708602e-02,\n",
    634        "        -4.97367457e-02, -7.52684176e-02,  6.09636456e-02, -1.71669777e-02,\n",
    635        "         5.91361187e-02, -2.88355686e-02,  3.53775546e-03, -3.64997461e-02,\n",
    636        "        -2.26949845e-02, -1.02284960e-02, -1.37392487e-02,  7.15957507e-02,\n",
    637        "        -2.68650427e-02,  8.53226632e-02,  5.14466465e-02,  4.93848957e-02,\n",
    638        "         8.59091058e-02, -2.62902882e-02,  1.00378595e-01, -1.65355932e-02,\n",
    639        "        -1.12617807e-02, -1.07267024e-02, -3.48392874e-02, -7.95916282e-03,\n",
    640        "         1.00653268e-01,  5.97646572e-02,  7.75867561e-03,  1.15043588e-01,\n",
    641        "         2.07628310e-02, -1.76335238e-02,  1.49275819e-02,  5.37896603e-02,\n",
    642        "         7.00431988e-02,  8.98461267e-02,  9.10779536e-02,  5.60465502e-03,\n",
    643        "         4.89570200e-02,  1.72273871e-02,  6.33122772e-02,  2.81463861e-02,\n",
    644        "        -1.87164899e-02, -4.43356372e-02, -1.96359158e-02,  6.37755021e-02,\n",
    645        "         5.42709902e-02, -8.60076584e-03, -2.25404557e-02,  3.58947404e-02,\n",
    646        "         5.20031601e-02,  1.08685963e-01, -1.22991828e-02, -1.04562687e-02,\n",
    647        "         3.02043371e-02,  3.07208095e-02,  4.24020141e-02, -1.82511788e-02,\n",
    648        "        -9.11675568e-04, -3.51112783e-02,  6.27857372e-02, -2.22748592e-02,\n",
    649        "         2.46507693e-02,  4.26435359e-02,  5.98271750e-03, -2.23289598e-02,\n",
    650        "        -2.76342370e-02,  2.70636491e-02, -1.39639527e-03, -3.58550772e-02,\n",
    651        "         5.28606214e-02,  3.11384685e-02,  5.14203608e-02,  5.45845442e-02,\n",
    652        "         1.19109713e-02,  8.52998719e-02, -4.34485935e-02,  1.28477793e-02,\n",
    653        "        -2.05492657e-02, -3.66462097e-02,  9.34293121e-03,  6.71103895e-02,\n",
    654        "         6.07372411e-02,  2.44732462e-02, -1.44067034e-02,  7.23934174e-02,\n",
    655        "        -2.92091519e-02,  4.71341871e-02,  8.01225677e-02, -1.20827099e-02,\n",
    656        "         4.47092671e-03, -1.86639261e-02,  1.82584543e-02,  4.94396463e-02,\n",
    657        "         7.74530992e-02, -3.29862945e-02, -3.13315839e-02, -7.30312197e-03,\n",
    658        "         7.01288553e-03, -2.70273201e-02, -1.52204782e-02, -2.32145302e-02,\n",
    659        "        -1.05082309e-02, -1.92536754e-05, -1.81905385e-02, -9.96621698e-03,\n",
    660        "         1.09827740e-03,  8.68956521e-02,  1.66933890e-02,  6.86416961e-03,\n",
    661        "        -1.36355804e-02,  6.13272749e-03, -4.08143923e-03,  6.13516010e-03,\n",
    662        "        -4.06875797e-02,  1.20015517e-01, -1.68200638e-02,  5.30381538e-02,\n",
    663        "        -1.80107821e-02, -8.72184616e-03, -1.38499169e-02,  4.68643233e-02,\n",
    664        "         9.19964090e-02,  1.01643559e-02,  5.90647422e-02, -4.65446375e-02,\n",
    665        "         1.23580657e-02, -1.08336322e-02, -1.94771849e-02,  1.24923378e-01,\n",
    666        "         4.38378043e-02,  4.30674516e-02,  4.88052331e-02,  1.19037889e-01,\n",
    667        "         1.41229006e-02,  4.44606319e-02, -1.27055962e-02,  9.96087026e-03,\n",
    668        "        -4.08994928e-02,  1.04974672e-01, -2.27738936e-02,  2.94191055e-02,\n",
    669        "        -7.52164470e-03,  1.45411575e-02, -3.27894352e-02, -3.44525538e-02,\n",
    670        "        -1.79617628e-02,  7.10037127e-02,  2.80745570e-02,  4.35190760e-02,\n",
    671        "        -4.07501981e-02, -5.86949894e-03,  5.25610968e-02, -2.21862085e-02,\n",
    672        "         4.24077436e-02,  5.35300560e-02,  1.01719853e-02,  5.97279631e-02,\n",
    673        "         4.16470729e-02, -2.26495452e-02,  1.12023562e-01,  1.15930304e-01,\n",
    674        "         6.27541617e-02, -1.05424039e-02, -3.84119377e-02, -4.32710610e-02,\n",
    675        "         2.88346666e-03,  4.88664694e-02,  3.65142450e-02,  3.03427354e-02,\n",
    676        "        -2.83553582e-02, -3.89167946e-03, -6.82092551e-03,  6.12990037e-02,\n",
    677        "        -2.25962736e-02,  1.20927487e-02, -1.14525799e-02,  7.77647048e-02,\n",
    678        "        -2.94107408e-03, -7.81376287e-03,  2.62177419e-02,  4.43499200e-02,\n",
    679        "         4.61728647e-02, -4.55445573e-02,  9.62291658e-02,  2.39728652e-02,\n",
    680        "         7.95497745e-03, -5.06303972e-03,  1.64592341e-02,  4.28285971e-02,\n",
    681        "         4.94796261e-02,  3.43723111e-02,  2.94336770e-02,  4.68909554e-02,\n",
    682        "         7.18486868e-03,  2.99673546e-02, -3.39506529e-02,  4.21292381e-03,\n",
    683        "         3.34585048e-02,  3.79088186e-02,  6.38425276e-02, -7.89680693e-04,\n",
    684        "         2.56242584e-02,  3.94742750e-02,  1.40315751e-02,  4.60222661e-02,\n",
    685        "         2.42276583e-02,  4.81235385e-02,  2.12334413e-02,  3.24134156e-02,\n",
    686        "         5.22319693e-03,  1.51581809e-01, -2.08536666e-02,  5.09871505e-02,\n",
    687        "        -2.71328781e-02,  5.51932771e-03,  1.83137301e-02,  5.57270758e-02,\n",
    688        "         4.80883941e-02,  6.50618374e-02, -1.34619158e-02,  1.22141302e-01,\n",
    689        "        -1.71008296e-02,  3.81552847e-03,  7.77109936e-02,  7.29411319e-02,\n",
    690        "         3.79633270e-02, -6.00689724e-02, -9.96629149e-03,  2.48741861e-02,\n",
    691        "        -1.47205703e-02, -1.55409258e-02,  1.02969883e-02,  4.04569283e-02,\n",
    692        "         3.13799903e-02,  1.16074421e-01,  3.65990065e-02,  3.31641100e-02,\n",
    693        "        -1.01007754e-06,  7.60934576e-02, -1.75704136e-02,  1.24804899e-02,\n",
    694        "         1.18262857e-01, -3.16509157e-02,  1.05599836e-01, -3.54418345e-02,\n",
    695        "         7.37690032e-02, -4.21324465e-03, -1.89842116e-02, -5.67948520e-02,\n",
    696        "         4.46014712e-03, -2.98472904e-02,  6.52025342e-02,  9.10847858e-02,\n",
    697        "         2.15716530e-02, -2.97974572e-02,  1.73324645e-02,  3.95461395e-02,\n",
    698        "         3.12089212e-02,  4.31387834e-02,  6.34427816e-02,  3.88964042e-02,\n",
    699        "         5.78227676e-02,  3.57266329e-02,  3.58240455e-02, -5.85102215e-02,\n",
    700        "         6.58234283e-02,  4.79356572e-02, -3.79503556e-02,  9.29015651e-02,\n",
    701        "         9.08859149e-02,  3.59453931e-02, -1.46479048e-02,  2.31869463e-02,\n",
    702        "         3.59964892e-02, -2.23172419e-02, -1.54903410e-02, -1.83392055e-02,\n",
    703        "         1.03566885e-01,  4.57644612e-02, -3.77948098e-02,  3.33484635e-02,\n",
    704        "         1.28243286e-02,  2.67320629e-02,  8.60852096e-03, -4.81675379e-03,\n",
    705        "         5.97773604e-02,  4.21931632e-02, -2.67935526e-02, -2.17138021e-03,\n",
    706        "        -2.12545926e-03,  3.86224315e-02,  8.93745758e-03,  1.20874859e-01,\n",
    707        "        -2.89826393e-02, -4.28759791e-02,  4.41658124e-02,  1.68996360e-02,\n",
    708        "        -3.31505165e-02,  5.13882004e-02,  5.88960387e-02, -1.76181737e-02,\n",
    709        "         1.48313271e-03,  5.79988658e-02,  8.94222558e-02, -1.36769097e-02,\n",
    710        "         9.34284274e-03, -1.84453707e-02, -1.26828887e-02,  8.16332735e-03,\n",
    711        "         1.40198991e-02, -2.84167286e-02,  6.19943962e-02, -1.19751494e-03,\n",
    712        "        -3.54143754e-02,  2.86288578e-02,  7.49624521e-02,  2.02866849e-02,\n",
    713        "         1.08794076e-02,  4.54496685e-03,  2.41601411e-02,  4.14725691e-02,\n",
    714        "        -2.01792410e-03,  7.59529248e-02, -2.71637514e-02,  5.82897440e-02,\n",
    715        "         3.52679119e-02, -1.26215005e-02, -1.30356280e-02,  4.40822244e-02,\n",
    716        "        -1.54802725e-02,  1.93001963e-02,  2.34842468e-02,  1.62283089e-02,\n",
    717        "         2.27487423e-02,  6.47596344e-02,  1.06462287e-02, -1.79562205e-03,\n",
    718        "         2.33289460e-03,  4.68608066e-02,  6.58840733e-03,  1.15877278e-02,\n",
    719        "        -1.65016465e-02, -1.56839415e-02,  3.75386402e-02,  5.15719280e-02,\n",
    720        "         4.46450040e-02,  9.76308808e-03,  3.82444868e-03,  5.87814413e-02,\n",
    721        "         1.14726231e-01,  4.75812741e-02,  3.50318104e-02,  2.91183833e-02,\n",
    722        "        -1.01490710e-02,  2.14113072e-02,  6.38290169e-03,  5.89702800e-02,\n",
    723        "         7.55651370e-02, -4.28581722e-02,  3.85477953e-02, -2.33158339e-02,\n",
    724        "         8.83768313e-03, -1.96045693e-02,  5.51499948e-02,  2.92682145e-02,\n",
    725        "        -2.69281492e-02, -2.41599735e-02,  3.44376490e-02,  7.64978603e-02,\n",
    726        "         5.74806742e-02,  3.19192410e-02,  4.75810543e-02,  2.57085152e-02,\n",
    727        "         1.20992931e-02, -2.29395423e-02,  4.98514511e-02,  3.49956192e-02,\n",
    728        "         4.24854718e-02,  3.13469879e-02, -3.78861034e-04,  6.45802449e-03,\n",
    729        "         9.36235711e-02,  5.45675009e-02,  2.89321337e-02,  3.20924097e-03,\n",
    730        "         1.14332768e-03,  7.16858879e-02,  6.88569993e-02,  2.04472039e-02,\n",
    731        "        -4.26226849e-04, -2.12573763e-02, -2.53489371e-02, -3.60967629e-02,\n",
    732        "         4.41990867e-02,  1.84635643e-03,  2.98514590e-02,  1.83002884e-03],\n",
    733        "       dtype=float32)]"
    734       ]
    735      },
    736      "execution_count": 18,
    737      "metadata": {},
    738      "output_type": "execute_result"
    739     }
    740    ],
    741    "source": [
    742     "# get second hidden layer (input not included in layer calculation)\n",
    743     "\n",
    744     "\n",
    745     "weights = model.get_layer(index=1).get_weights()\n",
    746     "\n",
    747     "# Weights:\n",
    748     "# First dimension is spec neuron\n",
    749     "# Second dimension are the associated weights\n",
    750     "\n",
    751     "weights"
    752    ]
    753   },
    754   {
    755    "cell_type": "code",
    756    "execution_count": 19,
    757    "metadata": {},
    758    "outputs": [],
    759    "source": [
    760     "import random \n",
    761     "# 34/512 neurons to pick\n",
    762     "\n",
    763     "specs = []\n",
    764     "\n",
    765     "for i in range(0, 34):\n",
    766     "    specs.append(random.randint(0,511))\n",
    767     "\n",
    768     "specs.sort()"
    769    ]
    770   },
    771   {
    772    "cell_type": "code",
    773    "execution_count": 20,
    774    "metadata": {},
    775    "outputs": [],
    776    "source": [
    777     "# Zero out specified weights\n",
    778     "\n",
    779     "for i in range(0,len(weights)):    \n",
    780     "    for neuron_index in specs:\n",
    781     "        weights[i][neuron_index] = 0"
    782    ]
    783   },
    784   {
    785    "cell_type": "code",
    786    "execution_count": 21,
    787    "metadata": {},
    788    "outputs": [],
    789    "source": [
    790     "model.get_layer(index=1).set_weights(weights)"
    791    ]
    792   },
    793   {
    794    "cell_type": "markdown",
    795    "metadata": {},
    796    "source": [
    797     "* Trained model\n",
    798     "* Selected random neurons from second hidden layer (512 dense)\n",
    799     "* Zeroed all of their weights (outside of model)\n",
    800     "* Put that back into the model"
    801    ]
    802   },
    803   {
    804    "cell_type": "code",
    805    "execution_count": 22,
    806    "metadata": {},
    807    "outputs": [
    808     {
    809      "name": "stdout",
    810      "output_type": "stream",
    811      "text": [
    812       "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step\n"
    813      ]
    814     }
    815    ],
    816    "source": [
    817     "y_test_pred = model.predict(x=X_test)"
    818    ]
    819   },
    820   {
    821    "cell_type": "code",
    822    "execution_count": 23,
    823    "metadata": {},
    824    "outputs": [],
    825    "source": [
    826     "y_test_pred_proper = []\n",
    827     "\n",
    828     "for i in y_test_pred:\n",
    829     "    y_test_pred_proper.append(i.argmax())"
    830    ]
    831   },
    832   {
    833    "cell_type": "code",
    834    "execution_count": 24,
    835    "metadata": {},
    836    "outputs": [
    837     {
    838      "data": {
    839       "text/plain": [
    840        "0.8813"
    841       ]
    842      },
    843      "execution_count": 24,
    844      "metadata": {},
    845      "output_type": "execute_result"
    846     }
    847    ],
    848    "source": [
    849     "from sklearn.metrics import accuracy_score\n",
    850     "\n",
    851     "accuracy_score(y_true=trueY, y_pred=y_test_pred_proper)"
    852    ]
    853   },
    854   {
    855    "cell_type": "code",
    856    "execution_count": 25,
    857    "metadata": {},
    858    "outputs": [
    859     {
    860      "name": "stdout",
    861      "output_type": "stream",
    862      "text": [
    863       "[[805   5  48  35   6   1  92   0   8   0]\n",
    864       " [  2 992   0   5   1   0   0   0   0   0]\n",
    865       " [  9   2 768  12 127   0  79   0   3   0]\n",
    866       " [  9  24  13 878  56   0  17   0   3   0]\n",
    867       " [  1   1  61  26 863   0  48   0   0   0]\n",
    868       " [  1   1   1   2   0 972   2   7   5   9]\n",
    869       " [ 99   7  77  40  67   0 706   0   4   0]\n",
    870       " [  0   0   3   0   1  41   3 893  10  49]\n",
    871       " [  3   0   5   0   5   1   9   1 976   0]\n",
    872       " [  0   0   0   0   0  16   3  17   4 960]]\n"
    873      ]
    874     }
    875    ],
    876    "source": [
    877     "from sklearn.metrics import confusion_matrix\n",
    878     "\n",
    879     "cm = confusion_matrix(y_true=trueY, y_pred=y_test_pred_proper)\n",
    880     "print(cm)"
    881    ]
    882   },
    883   {
    884    "cell_type": "code",
    885    "execution_count": 26,
    886    "metadata": {},
    887    "outputs": [
    888     {
    889      "data": {
    890       "image/png": "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",
    891       "text/plain": [
    892        "<Figure size 640x480 with 2 Axes>"
    893       ]
    894      },
    895      "metadata": {},
    896      "output_type": "display_data"
    897     }
    898    ],
    899    "source": [
    900     "import matplotlib.pyplot as plt\n",
    901     "import seaborn as sns\n",
    902     "\n",
    903     "sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    904     "plt.xlabel('Predicted')\n",
    905     "plt.ylabel('Actual')\n",
    906     "plt.title('Confusion Matrix')\n",
    907     "plt.show()"
    908    ]
    909   }
    910  ],
    911  "metadata": {
    912   "kernelspec": {
    913    "display_name": ".venv",
    914    "language": "python",
    915    "name": "python3"
    916   },
    917   "language_info": {
    918    "codemirror_mode": {
    919     "name": "ipython",
    920     "version": 3
    921    },
    922    "file_extension": ".py",
    923    "mimetype": "text/x-python",
    924    "name": "python",
    925    "nbconvert_exporter": "python",
    926    "pygments_lexer": "ipython3",
    927    "version": "3.11.2"
    928   }
    929  },
    930  "nbformat": 4,
    931  "nbformat_minor": 2
    932 }
	machinelearning Machine learning code
	git clone git://git.laack.co/machinelearning.git
	Log \| Files \| Refs