Deep_Learning_Using_Tensorflow,Keras.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Deep Learning Using Tensorflow,Keras.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "o1WbtZqRZ_dH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Image recognition using Deep Learning - TensorFlow and Keras**"
      ]
    },
    {
      "metadata": {
        "id": "BYOyFMkgLuDf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9f9b1bf4-d810-466d-f719-c55ad123ed53"
      },
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "tf.__version__"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.13.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "V3Wf6dJbM4Wu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mnist = tf.keras.datasets.mnist   ## 28x28 images of hand-written digits"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j34EEudpNE2d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "59fe3d8e-4c38-4ff1-f11b-2aa18ade5b96"
      },
      "cell_type": "code",
      "source": [
        "(X_train,y_train),(X_test,y_test) = mnist.load_data()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1Qe_jJBSNGGg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "1aa9b3b7-ab38-4467-d25b-ad7d86c9ac31"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(X_train[0])\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Duru7tXLlSn311Vdqbm5WQkKCJGnFihWaO3duOPsEAEf1G5ZHjhzR8ePH5fP51NHRoUWL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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "liuz8fswOyuZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "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": []
    },
    {
      "metadata": {
        "id": "e3--0QP_PUCZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "dcd912ed-ef54-4406-e4a1-fb2d048632bc"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.imshow(X_train[0])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OqaWlRUuXLtWJEydUXV2t5ORkSdKSJUs0derUaPYJAI7qNCxPnz6t2tpa+Xw+NTY2au7c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\nAGBAWAKAAWEJAAaEJQAYEJYAYPBfTLggPBvtq64AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "JQZfXvziPVxO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "2a22357f-4d9e-4204-89a7-eb1993bdd855"
      },
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential()    ## a basic feed-forward model\n",
        "model.add(tf.keras.layers.Flatten())    ## faltten 28x28 into 1x784 input layer\n",
        "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))  ## add hidden layer\n",
        "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
        "model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))  ## output layer\n",
        "\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "model.fit(X_train,y_train, epochs=3)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "60000/60000 [==============================] - 8s 129us/sample - loss: 0.2617 - acc: 0.9223\n",
            "Epoch 2/3\n",
            "60000/60000 [==============================] - 8s 127us/sample - loss: 0.1073 - acc: 0.9674\n",
            "Epoch 3/3\n",
            "60000/60000 [==============================] - 8s 127us/sample - loss: 0.0737 - acc: 0.9765\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f5d7f35feb8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "3LnDIh5DW4Qv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "417ddcf7-11dd-40d9-e9d5-2fd183c662b9"
      },
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 57us/sample - loss: 0.0963 - acc: 0.9708\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.09631686116755009, 0.9708]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "6NNnaSyJYnjr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "29ae90ae-17e1-465a-d622-c7289e88a2f0"
      },
      "cell_type": "code",
      "source": [
        "val_loss, val_acc = model.evaluate(X_test, y_test)\n",
        "print(val_loss)\n",
        "print(val_acc)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 53us/sample - loss: 0.0963 - acc: 0.9708\n",
            "0.09631686116755009\n",
            "0.9708\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "avGHxPJlYrLT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "model.save('number_reader.model')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d--ZqBwaZHw5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "7fd05aab-6ab9-4886-9fed-845738061fbd"
      },
      "cell_type": "code",
      "source": [
        "new_model = tf.keras.models.load_model('number_reader.model')\n",
        "predictions = new_model.predict(X_test)   ##make predictions\n",
        "print(predictions)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "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",
            "[[1.2778778e-09 2.7186660e-08 5.3303215e-06 ... 9.9999273e-01\n",
            "  2.5705651e-09 1.4483085e-07]\n",
            " [5.0711968e-08 3.2686078e-04 9.9967194e-01 ... 1.3571420e-09\n",
            "  2.3471820e-07 1.1197756e-12]\n",
            " [4.9142665e-07 9.9985135e-01 1.7403847e-05 ... 7.6653945e-05\n",
            "  2.0052330e-05 7.6656278e-07]\n",
            " ...\n",
            " [2.4890846e-07 4.5650418e-06 1.6692345e-06 ... 1.3914693e-04\n",
            "  2.4736898e-06 6.9152913e-05]\n",
            " [2.5786894e-05 1.7351973e-05 6.6066212e-07 ... 7.9879690e-05\n",
            "  2.4501006e-03 3.2315413e-08]\n",
            " [4.3923651e-06 5.0182571e-08 2.1424548e-06 ... 6.4134580e-11\n",
            "  1.3331011e-08 3.9726826e-09]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "KwnyiOIvZQoq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ea442231-1377-4eb0-df34-84734d73a81e"
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "print(np.argmax(predictions[6]))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9w0OpFwmZrKy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "outputId": "c3791dc2-e5b0-4609-bcfc-d03e5e53bea6"
      },
      "cell_type": "code",
      "source": [
        "plt.imshow(X_test[6],cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mjRITEyM5JwA4yhzLw4cPa9euXdqzZ4+Ki4uVkJCg1NRU5eTk6MaNG1q7dm2kZwUAx5i+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\nWAKAAbEEAANiCQAGxBIADP4L69q4QZg8reMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "R1MtE4heZvnb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}