cloathes_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Imports"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "# type: ignore on\r\n",
    "!pip install -q tensorflow\r\n",
    "!pip install -q matplotlib\r\n",
    "# type: ignore off\r\n",
    "import tensorflow\r\n",
    "from tensorflow import keras\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import numpy as np"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Load data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "dataset = keras.datasets.fashion_mnist\r\n",
    "((x_training, y_training), (x_test, y_test)) = dataset.load_data()\r\n",
    "print(x_training.shape)\r\n",
    "y_training.min(), y_training.max()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(60000, 28, 28)\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0, 9)"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Show data"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "classification = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\r\n",
    "\r\n",
    "for image in range(10):\r\n",
    "    plt.subplot(2, 5, image + 1)\r\n",
    "    plt.imshow(x_training[image])\r\n",
    "    plt.title(classification[y_training[image]])"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ],
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "plt.imshow(x_training[0])\r\n",
    "plt.colorbar()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x12b47fc2730>"
      ]
     },
     "metadata": {},
     "execution_count": 4
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ],
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data normalization"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "x_training = train_images = x_training / float(255)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Creating, training and normalizing the model"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "model = keras.Sequential([\r\n",
    "    keras.layers.Flatten(input_shape = (28, 28)),\r\n",
    "    keras.layers.Dense(256, activation = 'relu'),\r\n",
    "    keras.layers.Dropout(0.2),\r\n",
    "    keras.layers.Dense(len(classification), activation = 'softmax'),\r\n",
    "])\r\n",
    "\r\n",
    "model.compile(\r\n",
    "    optimizer = 'adam',\r\n",
    "    loss = 'sparse_categorical_crossentropy',\r\n",
    "    metrics = ['accuracy'],\r\n",
    ")\r\n",
    "\r\n",
    "model_history = model.fit(x_training, y_training, epochs = 5, validation_split = 0.2)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/5\n",
      "1500/1500 [==============================] - 3s 2ms/step - loss: 0.5324 - accuracy: 0.8104 - val_loss: 0.4367 - val_accuracy: 0.8405\n",
      "Epoch 2/5\n",
      "1500/1500 [==============================] - 2s 2ms/step - loss: 0.3984 - accuracy: 0.8548 - val_loss: 0.3695 - val_accuracy: 0.8638\n",
      "Epoch 3/5\n",
      "1500/1500 [==============================] - 2s 2ms/step - loss: 0.3649 - accuracy: 0.8670 - val_loss: 0.3719 - val_accuracy: 0.8683\n",
      "Epoch 4/5\n",
      "1500/1500 [==============================] - 2s 1ms/step - loss: 0.3404 - accuracy: 0.8762 - val_loss: 0.3457 - val_accuracy: 0.8756\n",
      "Epoch 5/5\n",
      "1500/1500 [==============================] - 2s 1ms/step - loss: 0.3235 - accuracy: 0.8807 - val_loss: 0.3362 - val_accuracy: 0.8797\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Saving/loading model"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "model.save('cloath_classification.h5')\r\n",
    "saved_model = keras.models.load_model('cloath_classification.h5')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Ploting accuracy by epoch"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "plt.plot(model_history.history['accuracy'])\r\n",
    "plt.plot(model_history.history['val_accuracy'])\r\n",
    "plt.title('Accuracy by epoch')\r\n",
    "plt.xlabel('Epoch')\r\n",
    "plt.ylabel('Accuracy')\r\n",
    "plt.legend(['Training', 'Validation'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12ba2df4a30>"
      ]
     },
     "metadata": {},
     "execution_count": 8
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Ploting loss by epoch"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "plt.plot(model_history.history['loss'])\r\n",
    "plt.plot(model_history.history['val_loss'])\r\n",
    "plt.title('Loss by epoch')\r\n",
    "plt.xlabel('Epoch')\r\n",
    "plt.ylabel('Loss')\r\n",
    "plt.legend(['Training', 'Validation'])"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12ba2e4c040>"
      ]
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model testing "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "tests = model.predict(x_test)\r\n",
    "result_test = classification[np.argmax(tests[10])]\r\n",
    "validation_test = classification[y_test[10]]\r\n",
    "print('Result test:', result_test)\r\n",
    "print('Validation test:', validation_test)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Result test: Coat\n",
      "Validation test: Coat\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "saved_model_tests = saved_model.predict(x_test)\r\n",
    "saved_model_result_test = classification[np.argmax(saved_model_tests[10])]\r\n",
    "saved_model_validation_test = classification[y_test[10]]\r\n",
    "print('Result test:', saved_model_result_test)\r\n",
    "print('Validation test:', saved_model_validation_test)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Result test: Coat\n",
      "Validation test: Coat\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Model validation"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "source": [
    "test_loss, accuracy_loss = model.evaluate(x_test, y_test, batch_size = 1)\r\n",
    "print('Test loss:', test_loss)\r\n",
    "print('Test accuracy:', accuracy_loss)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "10000/10000 [==============================] - 9s 899us/step - loss: 997.6274 - accuracy: 0.7472\n",
      "Test loss: 997.6273803710938\n",
      "Test accuracy: 0.7472000122070312\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "saved_model_test_loss, saved_model_accuracy_loss = saved_model.evaluate(x_test, y_test, batch_size = 1)\r\n",
    "print('Test loss:', saved_model_test_loss)\r\n",
    "print('Test accuracy:', saved_model_accuracy_loss)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "10000/10000 [==============================] - 6s 574us/step - loss: 59.9363 - accuracy: 0.8539\n",
      "Test loss: 59.93633270263672\n",
      "Test accuracy: 0.8539000153541565\n"
     ]
    }
   ],
   "metadata": {}
  }
 ],
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  "language_info": {
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   "version": "3.9.6",
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