first_cnn.ipynb

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  "metadata": {
    "colab": {
      "name": "first_cnn.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/bum94587/Notebooks/blob/master/first_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHTJLiAGeSZk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "c9338bb0-1dba-45fd-9743-f1b184cc9633"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1.15.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjynjMFtgPCo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "b83d96e6-33b7-40f6-c515-8b1d156ab808"
      },
      "source": [
        "mnist = tf.keras.datasets.fashion_mnist\n",
        "(training_images, training_labels), (test_images, test_labels) = mnist.load_data()\n",
        "training_images = training_images.reshape(60000, 28, 28, 1)\n",
        "training_images = training_images / 255.0\n",
        "test_images = test_images.reshape(10000, 28, 28, 1)\n",
        "test_images = test_images / 255.0"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWYULm4Kg-gH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "outputId": "ebbbc3ab-b174-4982-9b9c-e2cd1f5e4ff4"
      },
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)),\n",
        "  tf.keras.layers.MaxPool2D(2,2),\n",
        "  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "  tf.keras.layers.MaxPool2D(2,2),\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')\n",
        "model.summary()\n",
        "model.fit(training_images, training_labels, epochs=5)\n",
        "test_loss = model.evaluate(test_images, test_labels)"
      ],
      "execution_count": 4,
      "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",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 26, 26, 64)        640       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 13, 13, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 11, 11, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1600)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               204928    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 243,786\n",
            "Trainable params: 243,786\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Train on 60000 samples\n",
            "Epoch 1/5\n",
            "60000/60000 [==============================] - 83s 1ms/sample - loss: 0.4388\n",
            "Epoch 2/5\n",
            "60000/60000 [==============================] - 82s 1ms/sample - loss: 0.2930\n",
            "Epoch 3/5\n",
            "60000/60000 [==============================] - 82s 1ms/sample - loss: 0.2497\n",
            "Epoch 4/5\n",
            "60000/60000 [==============================] - 82s 1ms/sample - loss: 0.2163\n",
            "Epoch 5/5\n",
            "60000/60000 [==============================] - 82s 1ms/sample - loss: 0.1911\n",
            "10000/10000 [==============================] - 4s 412us/sample - loss: 0.2628\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "km5E1figis84",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}