Upload Зачётное_задание.ipynb

Зачётное_задание.ipynb

@@ -0,0 +1,453 @@
+{
+  "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": 114,
+      "metadata": {
+        "id": "hkKhol5xInsc"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "import tensorflow.keras\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tensorflow.keras import layers\n",
+        "from tensorflow.keras.layers import Dense, Flatten\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.optimizers import Adam\n",
+        "from tensorflow.keras.datasets import mnist"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+        "print(len(X_train), len(y_train), len(X_test), len(y_train))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "A-G0qmnLJehX",
+        "outputId": "4107021a-1175-4c30-c9b6-ca93745365eb"
+      },
+      "execution_count": 115,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "60000 60000 10000 60000\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(X_train[7].shape,X_train[7].dtype)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "dnoM30yEJtN5",
+        "outputId": "29517086-722a-416b-8c49-0dae07a060b6"
+      },
+      "execution_count": 116,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(28, 28) uint8\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(X_train[7])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WWIdtdAaJ-It",
+        "outputId": "12cdae37-c5d7-4617-dca5-f745a102fc34"
+      },
+      "execution_count": 117,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0  38  43 105 255 253 253 253\n",
+            "  253 253 174   6   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0  43 139 224 226 252 253 252 252 252\n",
+            "  252 252 252 158  14   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0 178 252 252 252 252 253 252 252 252\n",
+            "  252 252 252 252  59   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0 109 252 252 230 132 133 132 132 189\n",
+            "  252 252 252 252  59   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   4  29  29  24   0   0   0   0  14\n",
+            "  226 252 252 172   7   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  85\n",
+            "  243 252 252 144   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  88 189\n",
+            "  252 252 252  14   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0  91 212 247 252\n",
+            "  252 252 204   9   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0  32 125 193 193 193 253 252 252 252\n",
+            "  238 102  28   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0  45 222 252 252 252 252 253 252 252 252\n",
+            "  177   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0  45 223 253 253 253 253 255 253 253 253\n",
+            "  253  74   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0  31 123  52  44  44  44  44 143 252\n",
+            "  252  74   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  15 252\n",
+            "  252  74   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0  86 252\n",
+            "  252  74   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   5  75   9   0   0   0   0   0   0  98 242 252\n",
+            "  252  74   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0  61 183 252  29   0   0   0   0  18  92 239 252 252\n",
+            "  243  65   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0 208 252 252 147 134 134 134 134 203 253 252 252 188\n",
+            "   83   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0 208 252 252 252 252 252 252 252 252 253 230 153   8\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0  49 157 252 252 252 252 252 217 207 146  45   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   7 103 235 252 172 103  24   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]\n",
+            " [  0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
+            "    0   0   0   0   0   0   0   0   0   0]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(y_train[7])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n8HxUXEkKDGW",
+        "outputId": "78688ff3-39cf-427e-f74e-d88ba738e95c"
+      },
+      "execution_count": 118,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(X_train[7], cmap='binary')\n",
+        "plt.axis('off')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "sl8UqNi3KJR5",
+        "outputId": "d718d031-9875-46d6-b59f-f625ff9ad1e7"
+      },
+      "execution_count": 119,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(-0.5, 27.5, 27.5, -0.5)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 119
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train = X_train/X_train.max()\n",
+        "X_test = X_test/X_test.max()\n",
+        "y_train = tensorflow.keras.utils.to_categorical(y_train, 10)\n",
+        "y_test = tensorflow.keras.utils.to_categorical(y_test, 10)"
+      ],
+      "metadata": {
+        "id": "8LiC_R1lKNWw"
+      },
+      "execution_count": 120,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = Sequential([\n",
+        "    layers.Dense(32, activation='relu', input_shape=(X_train[7].shape)),\n",
+        "    layers.Dense(64, activation='relu'),\n",
+        "    layers.Dense(128, activation='relu'),\n",
+        "    layers.Dense(256, activation='relu'),\n",
+        "    layers.Dense(512, activation='relu'),\n",
+        "    layers.Flatten(),\n",
+        "    layers.Dense(10, activation='sigmoid')])\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0OyDllGeKWGG",
+        "outputId": "124bb729-235b-43b7-bd21-e9e7bb32c1af"
+      },
+      "execution_count": 121,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"sequential_6\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " dense_36 (Dense)            (None, 28, 32)            928       \n",
+            "                                                                 \n",
+            " dense_37 (Dense)            (None, 28, 64)            2112      \n",
+            "                                                                 \n",
+            " dense_38 (Dense)            (None, 28, 128)           8320      \n",
+            "                                                                 \n",
+            " dense_39 (Dense)            (None, 28, 256)           33024     \n",
+            "                                                                 \n",
+            " dense_40 (Dense)            (None, 28, 512)           131584    \n",
+            "                                                                 \n",
+            " flatten_6 (Flatten)         (None, 14336)             0         \n",
+            "                                                                 \n",
+            " dense_41 (Dense)            (None, 10)                143370    \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 319,338\n",
+            "Trainable params: 319,338\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(loss='binary_crossentropy',\n",
+        "              optimizer = Adam(lr = 0.00024),\n",
+        "              metrics = ['binary_accuracy'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SZYeosAMKdCV",
+        "outputId": "1ca25732-95c0-4e49-e0fa-f9130c6a41e3"
+      },
+      "execution_count": 122,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history = model.fit(X_train, y_train, batch_size=100, verbose=1, epochs= 5, validation_split = 0.2)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2NRQRSWfLH6I",
+        "outputId": "32f36eb6-b9ee-4910-e85a-2213b007238c"
+      },
+      "execution_count": 123,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/5\n",
+            "480/480 [==============================] - 47s 96ms/step - loss: 0.0802 - binary_accuracy: 0.9731 - val_loss: 0.0390 - val_binary_accuracy: 0.9875\n",
+            "Epoch 2/5\n",
+            "480/480 [==============================] - 49s 102ms/step - loss: 0.0334 - binary_accuracy: 0.9890 - val_loss: 0.0309 - val_binary_accuracy: 0.9902\n",
+            "Epoch 3/5\n",
+            "480/480 [==============================] - 47s 98ms/step - loss: 0.0286 - binary_accuracy: 0.9907 - val_loss: 0.0314 - val_binary_accuracy: 0.9903\n",
+            "Epoch 4/5\n",
+            "480/480 [==============================] - 46s 96ms/step - loss: 0.0262 - binary_accuracy: 0.9913 - val_loss: 0.0286 - val_binary_accuracy: 0.9913\n",
+            "Epoch 5/5\n",
+            "480/480 [==============================] - 49s 101ms/step - loss: 0.0241 - binary_accuracy: 0.9921 - val_loss: 0.0291 - val_binary_accuracy: 0.9913\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras.utils import plot_model\n",
+        "plot_model(model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 758
+        },
+        "id": "7V73Y6HOyuGV",
+        "outputId": "7192bebe-f998-4af7-c4c6-258b3752fcfc"
+      },
+      "execution_count": 124,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 124
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.save(\"Drive/Mydrive/NeueModel\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zyI0nFpnxtcA",
+        "outputId": "5b139611-35c2-4ba3-a1ad-0fd934ffeebf"
+      },
+      "execution_count": 125,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "WARNING:absl:Found untraced functions such as _update_step_xla while saving (showing 1 of 1). These functions will not be directly callable after loading.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pred = model.predict(X_test)\n",
+        "print(pred[7])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zDMHp-u1MidS",
+        "outputId": "d0acb3f2-d5be-4971-83fb-0708792de33d"
+      },
+      "execution_count": 137,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 4s 12ms/step\n",
+            "[1.7757750e-13 6.4790436e-07 9.5231037e-08 4.8262831e-03 1.0480519e-03\n",
+            " 8.7364512e-03 4.2137671e-10 2.2270392e-06 3.0571476e-09 8.6979014e-01]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for i in range(len(pred)):\n",
+        "    for j in range(10):\n",
+        "        if(pred[i][j]>0.5):\n",
+        "            pred[i][j]=1\n",
+        "        else:\n",
+        "            pred[i][j]=0\n",
+        "print(pred[7], y_test[7])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pF923X8UMn4b",
+        "outputId": "4408e564-9537-4b8e-dae6-ac266b46eba5"
+      },
+      "execution_count": 138,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.] [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n"
+          ]
+        }
+      ]
+    }
+  ]
+}