model_code.txt

{
  "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": 1,
      "metadata": {
        "id": "kLutYXp-ecSf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd3f2061-b234-4c54-9a85-91ac3fadf6e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 1s 0us/step\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow import keras\n",
        "import keras.backend as K\n",
        "from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, Lambda, BatchNormalization, Dropout\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "x_train = x_train / 255\n",
        "x_test = x_test/ 255\n",
        "\n",
        "y_train = y_train % 2\n",
        "y_train = keras.utils.to_categorical(y_train, 10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_img = Input((28, 28))\n",
        "x = Flatten()(input_img)\n",
        "x = Dense(128, activation = 'relu')(x)\n",
        "x = Dense(256, activation = 'relu')(x)\n",
        "x = Dense(64, activation = 'relu')(x)\n",
        "classif = Dense(10, activation = 'softmax')(x)"
      ],
      "metadata": {
        "id": "Ffd2RsvUedfQ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Model(input_img, classif)"
      ],
      "metadata": {
        "id": "5aVLXHYNe5R_"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "tG0HHttBVuxs"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train, epochs = 10, batch_size = 30, shuffle = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6tEkyZdWIZy",
        "outputId": "ab46112c-85ee-4d43-eeb3-4657296ef823"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2000/2000 [==============================] - 12s 5ms/step - loss: 0.1117 - accuracy: 0.9597\n",
            "Epoch 2/10\n",
            "2000/2000 [==============================] - 11s 5ms/step - loss: 0.0523 - accuracy: 0.9825\n",
            "Epoch 3/10\n",
            "2000/2000 [==============================] - 10s 5ms/step - loss: 0.0389 - accuracy: 0.9862\n",
            "Epoch 4/10\n",
            "2000/2000 [==============================] - 9s 5ms/step - loss: 0.0304 - accuracy: 0.9895\n",
            "Epoch 5/10\n",
            "2000/2000 [==============================] - 10s 5ms/step - loss: 0.0250 - accuracy: 0.9915\n",
            "Epoch 6/10\n",
            "2000/2000 [==============================] - 10s 5ms/step - loss: 0.0203 - accuracy: 0.9929\n",
            "Epoch 7/10\n",
            "2000/2000 [==============================] - 9s 4ms/step - loss: 0.0162 - accuracy: 0.9945\n",
            "Epoch 8/10\n",
            "2000/2000 [==============================] - 11s 5ms/step - loss: 0.0148 - accuracy: 0.9947\n",
            "Epoch 9/10\n",
            "2000/2000 [==============================] - 11s 5ms/step - loss: 0.0117 - accuracy: 0.9961\n",
            "Epoch 10/10\n",
            "2000/2000 [==============================] - 9s 4ms/step - loss: 0.0114 - accuracy: 0.9960\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fb0108d3a90>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tf.keras.utils.plot_model(model, show_shapes= True, show_layer_names= True, show_layer_activations= True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "WGei66Vbdtzk",
        "outputId": "1d66ceeb-7a58-489a-ec83-6a46a3b507fa"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-668ba8cae1eb>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_shapes\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_layer_names\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_layer_activations\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('drive/MyDrive/my_model')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YkhzAnVeePCm",
        "outputId": "88492cf4-5d9d-4a4e-ca91-690740e40961"
      },
      "execution_count": 8,
      "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": [
        "model.summary()"
      ],
      "metadata": {
        "id": "H4_sMVCpvNUG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}