diff --git "a/Code_of_my_model.ipynb" "b/Code_of_my_model.ipynb"
new file mode 100644--- /dev/null
+++ "b/Code_of_my_model.ipynb"
@@ -0,0 +1,341 @@
+{
+  "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": null,
+      "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",
+        "train_labels = to_categorical(train_labels, 10)\n",
+        "test_labels = to_categorical(test_labels, 10)"
+      ],
+      "metadata": {
+        "id": "MWwHW6KMYBwl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(len(train_images),\n",
+        "len(test_images),\n",
+        "len(train_labels),\n",
+        "len(test_labels))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "E74SVCUAuEcb",
+        "outputId": "48f41ba8-89ed-426a-c68e-5c3178f8539e"
+      },
+      "execution_count": 43,
+      "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(10, activation=\"softmax\"),\n",
+        "    ]\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "T9Yre6tDZRiy"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
+      ],
+      "metadata": {
+        "id": "9P6Wz-O-Z3Gh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels), batch_size=32)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "z-pxzXbbZ4qS",
+        "outputId": "940874b7-05f0-45d2-f790-8f990fbb8bad"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "1875/1875 [==============================] - 75s 39ms/step - loss: 0.5035 - accuracy: 0.8153 - val_loss: 0.3674 - val_accuracy: 0.8669\n",
+            "Epoch 2/10\n",
+            "1875/1875 [==============================] - 71s 38ms/step - loss: 0.3554 - accuracy: 0.8697 - val_loss: 0.3113 - val_accuracy: 0.8875\n",
+            "Epoch 3/10\n",
+            "1875/1875 [==============================] - 72s 39ms/step - loss: 0.3117 - accuracy: 0.8832 - val_loss: 0.2909 - val_accuracy: 0.8906\n",
+            "Epoch 4/10\n",
+            "1875/1875 [==============================] - 74s 39ms/step - loss: 0.2817 - accuracy: 0.8941 - val_loss: 0.2907 - val_accuracy: 0.8917\n",
+            "Epoch 5/10\n",
+            "1875/1875 [==============================] - 74s 39ms/step - loss: 0.2637 - accuracy: 0.9003 - val_loss: 0.2581 - val_accuracy: 0.9058\n",
+            "Epoch 6/10\n",
+            "1875/1875 [==============================] - 75s 40ms/step - loss: 0.2494 - accuracy: 0.9060 - val_loss: 0.2616 - val_accuracy: 0.9029\n",
+            "Epoch 7/10\n",
+            "1875/1875 [==============================] - 75s 40ms/step - loss: 0.2371 - accuracy: 0.9120 - val_loss: 0.2466 - val_accuracy: 0.9099\n",
+            "Epoch 8/10\n",
+            "1875/1875 [==============================] - 74s 39ms/step - loss: 0.2264 - accuracy: 0.9134 - val_loss: 0.2435 - val_accuracy: 0.9121\n",
+            "Epoch 9/10\n",
+            "1875/1875 [==============================] - 76s 40ms/step - loss: 0.2156 - accuracy: 0.9181 - val_loss: 0.2387 - val_accuracy: 0.9119\n",
+            "Epoch 10/10\n",
+            "1875/1875 [==============================] - 75s 40ms/step - loss: 0.2102 - accuracy: 0.9199 - val_loss: 0.2376 - val_accuracy: 0.9115\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<keras.callbacks.History at 0x7fd230242950>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "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": "bf7d8588-5ca8-4cce-da24-8a5a4a9b1d6e"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sr3wN_PCfJGJ",
+        "outputId": "eaae0246-1b35-4faf-bfc5-675000114d30"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_3\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " conv2d_6 (Conv2D)           (None, 26, 26, 32)        320       \n",
+            "                                                                 \n",
+            " max_pooling2d_6 (MaxPooling  (None, 13, 13, 32)       0         \n",
+            " 2D)                                                             \n",
+            "                                                                 \n",
+            " conv2d_7 (Conv2D)           (None, 11, 11, 64)        18496     \n",
+            "                                                                 \n",
+            " max_pooling2d_7 (MaxPooling  (None, 5, 5, 64)         0         \n",
+            " 2D)                                                             \n",
+            "                                                                 \n",
+            " flatten_3 (Flatten)         (None, 1600)              0         \n",
+            "                                                                 \n",
+            " dropout_3 (Dropout)         (None, 1600)              0         \n",
+            "                                                                 \n",
+            " dense_4 (Dense)             (None, 128)               204928    \n",
+            "                                                                 \n",
+            " dense_5 (Dense)             (None, 10)                1290      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 225,034\n",
+            "Trainable params: 225,034\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
+        "train_loss, train_acc= model.evaluate(train_images, train_labels)\n",
+        "val_loss, val_acc = model.evaluate(test_images, test_labels)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lD42yWpihbZR",
+        "outputId": "31503cdd-6dab-48a8-9694-1e62f5cf2701"
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 5s 15ms/step - loss: 0.2376 - accuracy: 0.9115\n",
+            "1875/1875 [==============================] - 22s 11ms/step - loss: 0.1506 - accuracy: 0.9453\n",
+            "313/313 [==============================] - 3s 10ms/step - loss: 0.2376 - accuracy: 0.9115\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( 0 <= predicted_lbs[i] <= 4 or predicted_lbs[i] == 6):\n",
+        "      axes[i].set_title(\"Одежда\")\n",
+        "    elif(predicted_lbs[i] == 5 or predicted_lbs[i] == 7 or predicted_lbs[i] == 9):\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": "71ec59a3-5118-4a30-98ae-f2d9cc03b9c1"
+      },
+      "execution_count": 36,
+      "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": "4ba162c6-fb08-42fa-9779-6718764de705"
+      },
+      "execution_count": 37,
+      "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"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file