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"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "2b22b7ad-9329-477a-a8f4-583fb23a8471",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"import pandas as pd\n",
"import matplotlib\n",
"matplotlib.use(\"TkAgg\")\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "8b9896be-f135-4c8d-9c00-c6fe7e444172",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(60000, 28, 28)\n",
"uint8\n"
]
}
],
"source": [
"fashion_mnist = keras.datasets.fashion_mnist\n",
"\n",
"(x_train_full, y_train_full), (x_test, y_test) = fashion_mnist.load_data()\n",
"x_test = x_test / 255.0\n",
"\n",
"print(x_train_full.shape)\n",
"print(x_train_full.dtype)\n",
"\n",
"x_valid, x_train = x_train_full[:5000] / 255.0, x_train_full[5000:] / 255.0\n",
"y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
"\n",
"class_names = [\n",
" \"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
" \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c4514be8-3e16-4f63-bea4-adc8e2576e43",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\lacer\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\keras\\src\\layers\\reshaping\\flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(**kwargs)\n"
]
}
],
"source": [
"model = keras.models.Sequential()\n",
"model.add(keras.layers.Flatten(input_shape=(28, 28)))\n",
"model.add(keras.layers.Dense(300, activation='relu'))\n",
"model.add(keras.layers.Dense(100, activation='relu'))\n",
"model.add(keras.layers.Dense(10, activation='softmax'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "80db0e8a-cc96-4752-8bec-60315ad34bec",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:TensorFlow GPU support is not available on native Windows for TensorFlow >= 2.11. Even if CUDA/cuDNN are installed, GPU will not be used. Please use WSL2 or the TensorFlow-DirectML plugin.\n",
"Epoch 1/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.7659 - loss: 0.7165 - val_accuracy: 0.8362 - val_loss: 0.5032\n",
"Epoch 2/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8292 - loss: 0.4888 - val_accuracy: 0.8542 - val_loss: 0.4361\n",
"Epoch 3/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8441 - loss: 0.4461 - val_accuracy: 0.8642 - val_loss: 0.4142\n",
"Epoch 4/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8529 - loss: 0.4180 - val_accuracy: 0.8654 - val_loss: 0.3972\n",
"Epoch 5/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8600 - loss: 0.3981 - val_accuracy: 0.8600 - val_loss: 0.3943\n",
"Epoch 6/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8655 - loss: 0.3811 - val_accuracy: 0.8694 - val_loss: 0.3724\n",
"Epoch 7/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8690 - loss: 0.3680 - val_accuracy: 0.8668 - val_loss: 0.3823\n",
"Epoch 8/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8726 - loss: 0.3573 - val_accuracy: 0.8724 - val_loss: 0.3629\n",
"Epoch 9/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8777 - loss: 0.3461 - val_accuracy: 0.8768 - val_loss: 0.3452\n",
"Epoch 10/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8804 - loss: 0.3365 - val_accuracy: 0.8792 - val_loss: 0.3440\n",
"Epoch 11/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8837 - loss: 0.3282 - val_accuracy: 0.8698 - val_loss: 0.3532\n",
"Epoch 12/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8858 - loss: 0.3197 - val_accuracy: 0.8814 - val_loss: 0.3315\n",
"Epoch 13/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8886 - loss: 0.3127 - val_accuracy: 0.8804 - val_loss: 0.3382\n",
"Epoch 14/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8911 - loss: 0.3053 - val_accuracy: 0.8738 - val_loss: 0.3601\n",
"Epoch 15/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8923 - loss: 0.2997 - val_accuracy: 0.8816 - val_loss: 0.3278\n",
"Epoch 16/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8951 - loss: 0.2938 - val_accuracy: 0.8834 - val_loss: 0.3259\n",
"Epoch 17/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.8983 - loss: 0.2865 - val_accuracy: 0.8850 - val_loss: 0.3281\n",
"Epoch 18/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9000 - loss: 0.2809 - val_accuracy: 0.8832 - val_loss: 0.3194\n",
"Epoch 19/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9008 - loss: 0.2760 - val_accuracy: 0.8886 - val_loss: 0.3071\n",
"Epoch 20/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9016 - loss: 0.2716 - val_accuracy: 0.8820 - val_loss: 0.3401\n",
"Epoch 21/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - accuracy: 0.9035 - loss: 0.2662 - val_accuracy: 0.8890 - val_loss: 0.3063\n",
"Epoch 22/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9061 - loss: 0.2614 - val_accuracy: 0.8900 - val_loss: 0.3113\n",
"Epoch 23/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9069 - loss: 0.2565 - val_accuracy: 0.8888 - val_loss: 0.3085\n",
"Epoch 24/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9091 - loss: 0.2524 - val_accuracy: 0.8842 - val_loss: 0.3263\n",
"Epoch 25/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9102 - loss: 0.2487 - val_accuracy: 0.8894 - val_loss: 0.3044\n",
"Epoch 26/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9108 - loss: 0.2446 - val_accuracy: 0.8914 - val_loss: 0.2958\n",
"Epoch 27/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9133 - loss: 0.2392 - val_accuracy: 0.8788 - val_loss: 0.3307\n",
"Epoch 28/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9149 - loss: 0.2362 - val_accuracy: 0.8914 - val_loss: 0.2978\n",
"Epoch 29/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9171 - loss: 0.2317 - val_accuracy: 0.8886 - val_loss: 0.3139\n",
"Epoch 30/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9177 - loss: 0.2281 - val_accuracy: 0.8922 - val_loss: 0.3038\n"
]
}
],
"source": [
"model.compile(\n",
" loss=\"sparse_categorical_crossentropy\",\n",
" optimizer=\"sgd\",\n",
" metrics=[\"accuracy\"]\n",
")\n",
"\n",
"history = model.fit(\n",
" x_train,\n",
" y_train,\n",
" epochs=30 ,\n",
" validation_data=(x_valid, y_valid)\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2a081c04-338c-40e6-89d1-857ec6ca3232",
"metadata": {},
"outputs": [],
"source": [
"pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
"plt.grid(True)\n",
"plt.gca().set_ylim(0, 1)\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "7cd5d8ac-6a3e-4a92-b4ef-574923bf4b71",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8787 - loss: 0.3378\n"
]
},
{
"data": {
"text/plain": [
"[0.33777710795402527, 0.8787000179290771]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(x_test, y_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "6d401ab5-a415-4c92-98b6-01ac1c15dd47",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n",
"[[0. 0. 0. 0. 0. 0.01 0. 0.03 0. 0.95]\n",
" [0. 0. 1. 0. 0. 0. 0. 0. 0. 0. ]\n",
" [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. ]]\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n",
"[9 2 1]\n",
"['Ankle boot' 'Pullover' 'Trouser']\n"
]
}
],
"source": [
"x_new = x_test[:3]\n",
"y_proba = model.predict(x_new)\n",
"print(y_proba.round(2))\n",
"\n",
"y_pred = model.predict(x_new).argmax(axis=1)\n",
"print(y_pred)\n",
"\n",
"print(np.array(class_names)[y_pred])\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5b476174-b4af-43fb-b1f4-8b182c3af14d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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