TensorFlow_mnist_CNN_LGMVIP_intern.ipynb

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    "colab": {
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "gpuClass": "standard"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "-DeP4sHeoeSQ"
      },
      "outputs": [],
      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "from tensorflow import keras\n",
        "import tensorflow as tf \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mnist=tf.keras.datasets.mnist\n",
        "(x_train,y_train),(x_test,y_test)=mnist.load_data()\n",
        "print(x_train.shape,y_train.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQmb2UIRqBB4",
        "outputId": "6e5b81b1-549c-45f8-8596-af5528fc9de5"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n",
            "(60000, 28, 28) (60000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_final=x_train.reshape((-1,784))\n",
        "x_train_final=x_train_final/255"
      ],
      "metadata": {
        "id": "3dR03BCqrW5w"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_final=x_test.reshape((-1,784))\n",
        "x_test_final=x_test_final/255"
      ],
      "metadata": {
        "id": "kuYILyX2rmUB"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential()\n",
        "model.add(keras.layers.Dense(512,input_shape=(784,),activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(256,activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(10,activation=\"softmax\"))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUlA9rrAsWVN",
        "outputId": "885a0b68-ae84-4bdc-9778-48a42873eda8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 512)               401920    \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 10)                2570      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 535,818\n",
            "Trainable params: 535,818\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='sgd',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "QoHHeUNzta01"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results=model.fit(x=x_train_final,y=y_train,shuffle=True,epochs=30,batch_size=32,validation_data=(x_test_final,y_test),verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDK6tDQ_tu55",
        "outputId": "851b2fde-e4f0-48ee-8602-d6c2fcf15a86"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "1875/1875 [==============================] - 27s 13ms/step - loss: 0.5630 - accuracy: 0.8578 - val_loss: 0.2958 - val_accuracy: 0.9140\n",
            "Epoch 2/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.2728 - accuracy: 0.9220 - val_loss: 0.2316 - val_accuracy: 0.9318\n",
            "Epoch 3/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.2208 - accuracy: 0.9376 - val_loss: 0.1953 - val_accuracy: 0.9423\n",
            "Epoch 4/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1866 - accuracy: 0.9471 - val_loss: 0.1711 - val_accuracy: 0.9486\n",
            "Epoch 5/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1615 - accuracy: 0.9545 - val_loss: 0.1514 - val_accuracy: 0.9559\n",
            "Epoch 6/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.1423 - accuracy: 0.9599 - val_loss: 0.1389 - val_accuracy: 0.9599\n",
            "Epoch 7/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.1270 - accuracy: 0.9641 - val_loss: 0.1217 - val_accuracy: 0.9642\n",
            "Epoch 8/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1142 - accuracy: 0.9678 - val_loss: 0.1166 - val_accuracy: 0.9655\n",
            "Epoch 9/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.1036 - accuracy: 0.9710 - val_loss: 0.1080 - val_accuracy: 0.9675\n",
            "Epoch 10/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0944 - accuracy: 0.9738 - val_loss: 0.1036 - val_accuracy: 0.9682\n",
            "Epoch 11/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0867 - accuracy: 0.9757 - val_loss: 0.0954 - val_accuracy: 0.9711\n",
            "Epoch 12/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0798 - accuracy: 0.9782 - val_loss: 0.0925 - val_accuracy: 0.9720\n",
            "Epoch 13/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.0740 - accuracy: 0.9799 - val_loss: 0.0869 - val_accuracy: 0.9740\n",
            "Epoch 14/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0684 - accuracy: 0.9813 - val_loss: 0.0855 - val_accuracy: 0.9737\n",
            "Epoch 15/30\n",
            "1875/1875 [==============================] - 14s 8ms/step - loss: 0.0633 - accuracy: 0.9826 - val_loss: 0.0842 - val_accuracy: 0.9736\n",
            "Epoch 16/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0593 - accuracy: 0.9835 - val_loss: 0.0803 - val_accuracy: 0.9755\n",
            "Epoch 17/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0551 - accuracy: 0.9852 - val_loss: 0.0793 - val_accuracy: 0.9758\n",
            "Epoch 18/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0516 - accuracy: 0.9862 - val_loss: 0.0777 - val_accuracy: 0.9766\n",
            "Epoch 19/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0481 - accuracy: 0.9876 - val_loss: 0.0729 - val_accuracy: 0.9778\n",
            "Epoch 20/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0455 - accuracy: 0.9880 - val_loss: 0.0729 - val_accuracy: 0.9777\n",
            "Epoch 21/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0424 - accuracy: 0.9890 - val_loss: 0.0720 - val_accuracy: 0.9782\n",
            "Epoch 22/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0396 - accuracy: 0.9900 - val_loss: 0.0734 - val_accuracy: 0.9764\n",
            "Epoch 23/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0375 - accuracy: 0.9904 - val_loss: 0.0717 - val_accuracy: 0.9778\n",
            "Epoch 24/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0353 - accuracy: 0.9910 - val_loss: 0.0690 - val_accuracy: 0.9785\n",
            "Epoch 25/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0329 - accuracy: 0.9920 - val_loss: 0.0670 - val_accuracy: 0.9793\n",
            "Epoch 26/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0309 - accuracy: 0.9929 - val_loss: 0.0689 - val_accuracy: 0.9789\n",
            "Epoch 27/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0291 - accuracy: 0.9933 - val_loss: 0.0685 - val_accuracy: 0.9794\n",
            "Epoch 28/30\n",
            "1875/1875 [==============================] - 15s 8ms/step - loss: 0.0274 - accuracy: 0.9938 - val_loss: 0.0675 - val_accuracy: 0.9781\n",
            "Epoch 29/30\n",
            "1875/1875 [==============================] - 13s 7ms/step - loss: 0.0258 - accuracy: 0.9945 - val_loss: 0.0651 - val_accuracy: 0.9803\n",
            "Epoch 30/30\n",
            "1875/1875 [==============================] - 14s 7ms/step - loss: 0.0244 - accuracy: 0.9948 - val_loss: 0.0672 - val_accuracy: 0.9798\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predict=model.predict(x_test_final)\n",
        "predict"
      ],
      "metadata": {
        "id": "CFy30WfJWZgB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dde80a8-09a0-4a2c-dba9-06b847ea7e00"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2.2355164e-07, 1.1470512e-08, 6.2359190e-06, ..., 9.9988657e-01,\n",
              "        6.8110359e-07, 4.3435198e-06],\n",
              "       [5.6290048e-08, 6.5412851e-06, 9.9992663e-01, ..., 4.8133806e-13,\n",
              "        2.7201020e-06, 3.7119539e-12],\n",
              "       [3.6827478e-06, 9.9816960e-01, 1.7342203e-04, ..., 8.7068661e-04,\n",
              "        4.7027209e-04, 5.4970833e-06],\n",
              "       ...,\n",
              "       [9.4916429e-11, 3.8034956e-10, 1.9946480e-11, ..., 4.5034903e-06,\n",
              "        1.2578948e-07, 7.1822265e-06],\n",
              "       [1.4699810e-07, 1.3165470e-07, 4.4895101e-09, ..., 3.2181877e-08,\n",
              "        3.3421439e-04, 9.4922770e-10],\n",
              "       [1.0486214e-07, 4.1257459e-10, 2.0462420e-07, ..., 8.1753510e-12,\n",
              "        1.1456111e-09, 3.0380386e-11]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(results.history['accuracy'])\n",
        "plt.plot(results.history['val_accuracy'])\n",
        "plt.legend(['Training', 'Validation'])\n",
        "plt.title('Training and Validation losses')\n",
        "plt.xlabel('epoches')\n",
        "plt.ylabel('accuracy')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "lmjrVNQjfLqN",
        "outputId": "76401e3b-138d-4271-fb27-f3e930473445"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3NcQcu-Oyr-Z"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(results.history['loss'])\n",
        "plt.plot(results.history['val_loss'])\n",
        "plt.legend(['Training', 'Validation'])\n",
        "plt.title('Training and Validation losses')\n",
        "plt.xlabel('epoches')\n",
        "plt.ylabel('loss')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "873defbc-380d-4eb3-ed73-636d6472ce77",
        "id": "6wk1fqDTys0e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'loss')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}