Exercise2_model_method.ipynb

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      "name": "Exercise2_model_method.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/soumyadiptapete/Coursera_TF_specialization/blob/master/Exercise2_model_method.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "oNC3gKoK-z-z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential,Model\n",
        "from keras.layers import Dense, Activation,Flatten,Input\n",
        "from keras import regularizers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kjgbbf0D_Cvs",
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      "source": [
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs={}):\n",
        "    if(logs.get('acc')>0.99):\n",
        "      print(\"\\nReached 99% accuracy so cancelling training!\")\n",
        "      self.model.stop_training = True"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dKHun9S_Ibt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "callbacks = myCallback()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2xpzh9K_Kt0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist=tf.keras.datasets.mnist\n",
        "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XemspGg__bTO",
        "colab_type": "code",
        "outputId": "57a6f790-4f0d-4401-d831-2ae12f85c259",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train=tf.keras.utils.to_categorical(y_train,num_classes=10)\n",
        "y_test=tf.keras.utils.to_categorical(y_test,num_classes=10)\n",
        "y_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ozYysC7Z_g3R",
        "colab_type": "code",
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      },
      "source": [
        "inputs=Input(shape=(28,28))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEN0whz7BCOG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=Flatten()(inputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTbOx43lBqlH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X=Dense(1024,activation='relu')(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WkAG6mCoCGuV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred=Dense(10,activation='softmax')(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZdABhMxCLab",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=Model(inputs=inputs,outputs=pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lfN2rIwCd1y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sfAvBz9Cko3",
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        "outputId": "eca7f39d-6ab7-4b1b-aad1-04aa54125a40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 309
        }
      },
      "source": [
        "model.fit(x_train,y_train,epochs=10,callbacks=[callbacks])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.cast instead.\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 19s 320us/step - loss: 0.1862 - acc: 0.9439\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 19s 322us/step - loss: 0.0732 - acc: 0.9773\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 19s 309us/step - loss: 0.0485 - acc: 0.9848\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 20s 329us/step - loss: 0.0334 - acc: 0.9888\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 18s 301us/step - loss: 0.0266 - acc: 0.9915\n",
            "\n",
            "Reached 60% accuracy so cancelling training!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f0dda48a1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "yWbg2ZZLCtkl",
        "colab_type": "code",
        "outputId": "96cf9a7e-3bb9-4600-c42c-28d347497be8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "model.evaluate(x_test,y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 73us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.05973226695196063, 0.9828]"
            ]
          },
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          "execution_count": 31
        }
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      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
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