keras1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "794ey8wxyIkR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYmBCqdKySk6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow.keras as keras\n",
        "import tensorflow as tf\n",
        "import cv2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6uCgjlyyr6r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mnist=tf.keras.datasets.mnist\n",
        "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n",
        "#print(x_train[0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "28Y-5h21vnOS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "039aed82-bb2c-4609-b1e5-fcb4830e5302"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.imshow(x_train[0],cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GSpqt9luKepukRdntRZJeL3CWH2iXZbyrLTOugh+7wpc/d/eWf0i6W32vyH8maVURM1SZ\n65eS/jf7OFD0bJI2qe9p3Xfqe23jYUn/JmmnpEOS3pI0po1m+29JH0rqUV+xOgua7Rb1PUXvkbQv\n+7i76McuMVdLHjd+XRYIghfogCAoOxAEZQeCoOxAEJQdCIKyA0FQdiCI/wfvpjt5Q0mdXQAAAABJ\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vesUwurp2D17",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "e0728038-342b-4e9f-dae3-16a168c180ac"
      },
      "source": [
        "x_train=tf.keras.utils.normalize(x_train,axis=1)\n",
        "x_test = tf.keras.utils.normalize(x_test, axis=1)\n",
        "#print(x_train[0])\n",
        "\n",
        "plt.imshow(x_train[0],cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9fEO7TOOdN824Kn7vKp/+3N1b/idpjcaOyH8o6d+q6CGnr3+Q9E72917VvUl6SWMf677S\n2LGNxyTdImmnpEOSXpc0u416e1HSPknvaixY8yrq7V6NfUR/V9Le7G9N1e9doq+WvG/8XBYIggN0\nQBCEHQiCsANBEHYgCMIOBEHYgSAIOxDE/wN2tzSxIQ/OWQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V8At4LdC2eXB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "outputId": "b0e614f1-6d22-4ca9-dc27-ea740d1ff5b5"
      },
      "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.relu))\n",
        "model.add(tf.keras.layers.Dense(10,activation=tf.nn.softmax))\n",
        "model.compile(\n",
        "    optimizer=\"adam\",\n",
        "    loss=\"sparse_categorical_crossentropy\",\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "model.fit(x_train,y_train,epochs=10)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "Train on 60000 samples\n",
            "Epoch 1/10\n",
            "60000/60000 [==============================] - 5s 83us/sample - loss: 0.2671 - acc: 0.9208\n",
            "Epoch 2/10\n",
            "60000/60000 [==============================] - 5s 87us/sample - loss: 0.1075 - acc: 0.9669\n",
            "Epoch 3/10\n",
            "60000/60000 [==============================] - 5s 89us/sample - loss: 0.0714 - acc: 0.9773\n",
            "Epoch 4/10\n",
            "60000/60000 [==============================] - 5s 82us/sample - loss: 0.0536 - acc: 0.9830\n",
            "Epoch 5/10\n",
            "60000/60000 [==============================] - 5s 80us/sample - loss: 0.0406 - acc: 0.9866\n",
            "Epoch 6/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0327 - acc: 0.9893\n",
            "Epoch 7/10\n",
            "60000/60000 [==============================] - 5s 77us/sample - loss: 0.0250 - acc: 0.9917\n",
            "Epoch 8/10\n",
            "60000/60000 [==============================] - 5s 79us/sample - loss: 0.0200 - acc: 0.9933\n",
            "Epoch 9/10\n",
            "60000/60000 [==============================] - 5s 80us/sample - loss: 0.0190 - acc: 0.9937\n",
            "Epoch 10/10\n",
            "60000/60000 [==============================] - 5s 79us/sample - loss: 0.0140 - acc: 0.9953\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fb391b2ee48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6YjMHIN19kqX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "98a02e3d-2243-4984-f5f9-2f1054a9b640"
      },
      "source": [
        "val_loss,val_acc=model.evaluate(x_test,y_test)\n",
        "#print(x_test,y_test)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 0s 33us/sample - loss: 0.0972 - acc: 0.9767\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBqKCbjP-RZN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "a1f7ad36-aa4c-4775-d2c9-fa90ea1ad61e"
      },
      "source": [
        "model.save('abc.model')\n",
        "new_model=tf.keras.models.load_model('abc.model')\n",
        "prediction=new_model.predict(x_test)\n",
        "print(numpy.argmax(prediction[0]))"
      ],
      "execution_count": 38,
      "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",
            "7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPrquxLh_cm5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2bbdee00-3b2b-4200-9d5e-0bafe4c9c613"
      },
      "source": [
        "plt.imshow(x_test[0],cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kc4FgRi2AU5K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}