MnistDigitsDnn.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MnistDigitsDnn.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SourabhG16/DeepLearning/blob/master/MnistDigitsDnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yXe8dMEvkddz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6623e159-e7f5-499f-e28a-6b67230f5597"
      },
      "source": [
        "import tensorflow as tf\n",
        "tf.__version__"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.15.0-rc3'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyPJi6VEkihh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist=tf.keras.datasets.mnist"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PEetDnikroT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "(x_train,y_train),(x_test,y_test) = mnist.load_data()\n",
        "x_train=tf.keras.utils.normalize(x_train,axis=1)\n",
        "x_test=tf.keras.utils.normalize(x_test,axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "89rYmmpek3jF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b0cd33e0-697b-4eae-c20e-2c5113f24170"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(x_train[0],cmap=plt.cm.binary)\n",
        "print(x_train[0])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
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          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nK1OkXgT91QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v6YQKAaLlGQN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=tf.keras.models.Sequential()\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(tf.keras.layers.Dense(128,activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(128,activation=tf.nn.tanh))\n",
        "model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Twfcg_vzo-Mp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ik8xy8HBr2K2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "e734da55-b195-40ab-f361-680a34205039"
      },
      "source": [
        "model.fit(x_train,y_train,epochs=20)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.2522 - acc: 0.9268\n",
            "Epoch 2/20\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.1037 - acc: 0.9676\n",
            "Epoch 3/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0708 - acc: 0.9773\n",
            "Epoch 4/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0511 - acc: 0.9835\n",
            "Epoch 5/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0398 - acc: 0.9869\n",
            "Epoch 6/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0298 - acc: 0.9901\n",
            "Epoch 7/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0251 - acc: 0.9915\n",
            "Epoch 8/20\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0189 - acc: 0.9934\n",
            "Epoch 9/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0170 - acc: 0.9943\n",
            "Epoch 10/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0141 - acc: 0.9950\n",
            "Epoch 11/20\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.0112 - acc: 0.9961\n",
            "Epoch 12/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0110 - acc: 0.9958\n",
            "Epoch 13/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0100 - acc: 0.9966\n",
            "Epoch 14/20\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0094 - acc: 0.9966\n",
            "Epoch 15/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0083 - acc: 0.9972\n",
            "Epoch 16/20\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.0093 - acc: 0.9969\n",
            "Epoch 17/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0068 - acc: 0.9978\n",
            "Epoch 18/20\n",
            "60000/60000 [==============================] - 4s 70us/sample - loss: 0.0077 - acc: 0.9973\n",
            "Epoch 19/20\n",
            "60000/60000 [==============================] - 4s 69us/sample - loss: 0.0057 - acc: 0.9980\n",
            "Epoch 20/20\n",
            "60000/60000 [==============================] - 4s 68us/sample - loss: 0.0059 - acc: 0.9979\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f033ae6ae48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nWSv9-bMuIGt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9e610147-3aa9-4956-db57-e9ee043f9393"
      },
      "source": [
        "loss,acc=model.evaluate(x_test,y_test)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 37us/sample - loss: 0.1446 - acc: 0.9759\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mz2A0IyVud1s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b0257b9c-6041-42d4-ab1a-e82e6538f5cc"
      },
      "source": [
        "loss"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.14461671838153833"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ELENqqWfu45r",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2d1f26f4-93e9-441e-9b66-4d32217e008a"
      },
      "source": [
        "acc"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9759"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FuP4KLhMu5ZI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('sourabh_20_epoch_mnist')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bmn3bo6JvgRc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        },
        "outputId": "b4096ca0-a046-435c-e8ce-bb9ec69ce82c"
      },
      "source": [
        "new_m=tf.keras.models.load_model('sourabh_20_epoch_mnist')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling GlorotUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/init_ops.py:97: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:Sequential models without an `input_shape` passed to the first layer cannot reload their optimizer state. As a result, your model isstarting with a freshly initialized optimizer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBJ7op7MvmNO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7fdd6736-5c70-4974-ac2a-eecc141871e5"
      },
      "source": [
        "new_m"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.engine.sequential.Sequential at 0x7f033e432588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kD0aEx8IvokC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predict=model.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hw1OAj5KwC4L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3f0cfb65-0f51-4844-d7fb-70605a070149"
      },
      "source": [
        "import numpy as np\n",
        "print(np.argmax(predict[1]))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A3pUdMDywXo7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "e6f4c133-d667-40f1-c2dc-526337457dcc"
      },
      "source": [
        "plt.imshow(x_test[1])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f033b244f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+67gZr9297T+SrtXkO/7/kvTNKnrI6WulpL9nP09W3ZukezT5NHBUk++N3CLpTZJ2Snpa\n0l8kLemg3n4haa+kxzUZtOUV9XaVJp/SPy5pT/ZzbdX3XaKvSu43PuEHBMUbfkBQhB8IivADQRF+\nICjCDwRF+IGgCD8QFOEHgvovaQNXcKb5BWQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMwMMDT1wae5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}