{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "CnOuuUHQXQ13"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(train_images, train_labels), (test_images, test_labels) = keras.datasets.fashion_mnist.load_data()\n",
        "\n",
        "train_images = train_images.reshape(-1, 28, 28, 1).astype(\"float32\") / 255.0\n",
        "test_images = test_images.reshape(-1, 28, 28, 1).astype(\"float32\") / 255.0\n",
        "\n",
        "testY = []\n",
        "trainY = []\n",
        "\n",
        "for i in range(len(train_labels)):\n",
        "  if( 0 <= train_labels[i] <= 4 or train_labels[i] == 6):\n",
        "    trainY.append(0)\n",
        "  elif(train_labels[i] == 5 or train_labels[i] == 7 or train_labels[i] == 9):\n",
        "    trainY.append(1)\n",
        "  else:\n",
        "    trainY.append(2)\n",
        "\n",
        "for i in range(len(test_labels)):\n",
        "  if( 0 <= test_labels[i] <= 4 or test_labels[i] == 6):\n",
        "    testY.append(0)\n",
        "  elif(test_labels[i] == 5 or test_labels[i] == 7 or test_labels[i] == 9):\n",
        "    testY.append(1)\n",
        "  else:\n",
        "    testY.append(2)\n",
        "\n",
        "\n",
        "\n",
        "trainY = to_categorical(trainY, 3)\n",
        "testY = to_categorical(testY, 3)"
      ],
      "metadata": {
        "id": "MWwHW6KMYBwl"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train_images),\n",
        "len(test_images),\n",
        "len(trainY),\n",
        "len(testY))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E74SVCUAuEcb",
        "outputId": "00af836a-4d32-4cc0-f5b9-efb02ece5e29"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "60000 10000 60000 10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential(\n",
        "    [\n",
        "        keras.Input(shape=(28, 28, 1)),\n",
        "        layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Flatten(),\n",
        "        layers.Dropout(0.5),\n",
        "        layers.Dense(128, activation=\"relu\"),\n",
        "        layers.Dense(3, activation=\"softmax\"),\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "T9Yre6tDZRiy"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "9P6Wz-O-Z3Gh"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(train_images, trainY, epochs=10, validation_data=(test_images, testY), batch_size=128)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-pxzXbbZ4qS",
        "outputId": "c0b1c10d-a870-4fc5-c815-f0510b7933b8"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 66s 139ms/step - loss: 0.0690 - accuracy: 0.9783 - val_loss: 0.0313 - val_accuracy: 0.9907\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 62s 133ms/step - loss: 0.0287 - accuracy: 0.9920 - val_loss: 0.0251 - val_accuracy: 0.9923\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 62s 132ms/step - loss: 0.0222 - accuracy: 0.9936 - val_loss: 0.0198 - val_accuracy: 0.9937\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 62s 132ms/step - loss: 0.0176 - accuracy: 0.9948 - val_loss: 0.0192 - val_accuracy: 0.9947\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 61s 130ms/step - loss: 0.0156 - accuracy: 0.9954 - val_loss: 0.0186 - val_accuracy: 0.9947\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 62s 132ms/step - loss: 0.0126 - accuracy: 0.9963 - val_loss: 0.0184 - val_accuracy: 0.9943\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 66s 142ms/step - loss: 0.0124 - accuracy: 0.9963 - val_loss: 0.0177 - val_accuracy: 0.9952\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 61s 131ms/step - loss: 0.0109 - accuracy: 0.9966 - val_loss: 0.0151 - val_accuracy: 0.9959\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 63s 134ms/step - loss: 0.0092 - accuracy: 0.9974 - val_loss: 0.0155 - val_accuracy: 0.9958\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 61s 130ms/step - loss: 0.0085 - accuracy: 0.9972 - val_loss: 0.0135 - val_accuracy: 0.9963\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fd2327fefb0>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "keras.utils.plot_model(model, \"model.png\", show_shapes=True, show_layer_activations=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        },
        "id": "way9gmt-Z5_K",
        "outputId": "d5dd7b59-c614-484a-dd7b-da9a8c9f9272"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sr3wN_PCfJGJ",
        "outputId": "434ea6d5-ccd4-41c3-96b5-e256267b405b"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_14 (Conv2D)          (None, 26, 26, 32)        320       \n",
            "                                                                 \n",
            " max_pooling2d_14 (MaxPoolin  (None, 13, 13, 32)       0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_15 (Conv2D)          (None, 11, 11, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_15 (MaxPoolin  (None, 5, 5, 64)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_7 (Flatten)         (None, 1600)              0         \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 1600)              0         \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 128)               204928    \n",
            "                                                                 \n",
            " dense_15 (Dense)            (None, 3)                 387       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 224,131\n",
            "Trainable params: 224,131\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(test_images, testY)\n",
        "train_loss, train_acc= model.evaluate(train_images, trainY)\n",
        "val_loss, val_acc = model.evaluate(test_images, testY)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lD42yWpihbZR",
        "outputId": "3705434b-2e37-403c-ba3f-dfdc44feaada"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 4s 11ms/step - loss: 0.0135 - accuracy: 0.9963\n",
            "1875/1875 [==============================] - 21s 11ms/step - loss: 0.0041 - accuracy: 0.9988\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 0.0135 - accuracy: 0.9963\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicts = model.predict(test_images)\n",
        "predicted_lbs = [np.argmax(prediction) for prediction in predicts]\n",
        "\n",
        "fig, axes = plt.subplots(5, 5, figsize=(24, 12))\n",
        "axes = axes.ravel()\n",
        "\n",
        "for i in range(25):\n",
        "    axes[i].imshow(test_images[i].reshape(28, 28), cmap='gray')\n",
        "    if(predicted_lbs[i] == 0):\n",
        "      axes[i].set_title(\"Одежда\")\n",
        "    elif(predicted_lbs[i] == 1):\n",
        "      axes[i].set_title(\"Обувь\")\n",
        "    else:\n",
        "      axes[i].set_title(\"Сумка\")\n",
        "    axes[i].axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "id": "D95WvS-9poHX",
        "outputId": "97a35fda-cdf6-4d71-8b2e-cb4575552475"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 3s 10ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x1200 with 25 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save(\"/content/drive/MyDrive/my_model2\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8TlDhYesoJU",
        "outputId": "de36b0e6-0c8c-4b58-b84e-d6390aa6395f"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _update_step_xla while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
          ]
        }
      ]
    }
  ]
}