CNN2_flower.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled7.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "oUTIvVPUypJ8",
        "colab_type": "code",
        "outputId": "8b4a9b4d-29d6-401a-f120-c7b34543386d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('dataset')\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at dataset\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MdG7LzAyxpU",
        "colab_type": "code",
        "outputId": "2b1e6181-b41e-4a5f-df0e-0fc402a165c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "import os\n",
        "path = 'dataset/My Drive/Colab Notebooks/flowers'\n",
        "folders = os.listdir(path)\n",
        "print(folders)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['tulip', 'forsythia', 'cherryblossom', 'roseofsharon', 'sunflower', 'lily', 'korearose', 'buckwheat', 'cosmos', 'rose']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwVVMp34zze9",
        "colab_type": "code",
        "outputId": "331fd4c0-5b18-4f40-c76a-5ac7027e8704",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84073
        }
      },
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import cv2\n",
        "\n",
        "from random import shuffle\n",
        "from tqdm import tqdm\n",
        "\n",
        "train_dir = '/content/gdrive/My Drive/Colab Notebooks/flowers'\n",
        "\n",
        "\n",
        "img_size = 28\n",
        "\n",
        "def label_folder(folders):\n",
        "    if folders == 'tulip': return [1,0,0,0,0,0,0,0,0,0]\n",
        "    elif folders == 'rose': return [0,1,0,0,0,0,0,0,0,0]\n",
        "    elif folders == 'cosmos': return [0,0,1,0,0,0,0,0,0,0]\n",
        "    elif folders == 'cherryblossom': return [0,0,0,1,0,0,0,0,0,0]\n",
        "    elif folders == 'sunflower': return [0,0,0,0,1,0,0,0,0,0]\n",
        "    elif folders == 'koreaflower': return [0,0,0,0,0,1,0,0,0,0]\n",
        "    elif folders == 'lily': return [0,0,0,0,0,0,1,0,0,0]\n",
        "    elif folders == 'buckwheat': return [0,0,0,0,0,0,0,1,0,0]       #메밀꽃\n",
        "    elif folders == 'korearosebay': return [0,0,0,0,0,0,0,0,1,0]       #진달래\n",
        "    elif folders == 'forsythia': return [0,0,0,0,0,0,0,0,0,1]   #개나리\n",
        "\n",
        "def create_train_data():\n",
        "    training_data = []\n",
        "    dirs = os.listdir(train_dir)\n",
        "    for folders in dirs:\n",
        "        label = label_folder(folders)\n",
        "        req_train_dir = os.path.join(train_dir,folders)\n",
        "        for img in tqdm(os.listdir(req_train_dir)):\n",
        "            path = os.path.join(req_train_dir,img)\n",
        "            img = cv2.imread(path,cv2.IMREAD_GRAYSCALE)\n",
        "            img = cv2.resize(img, (img_size,img_size))\n",
        "            training_data.append([np.array(img),np.array(label)])\n",
        "    shuffle(training_data)\n",
        "    np.save('flower_train_data.npy', training_data)\n",
        "    return training_data\n",
        "\n",
        "train_data = create_train_data()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[A"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y2uQVngkzA7X",
        "colab_type": "code",
        "outputId": "d4e005b1-6ae5-47ac-9b6a-1fc32c2b7267",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "source": [
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Convolutional Layer 1.\n",
        "filter_size1 = 4 \n",
        "num_filters1 = 32\n",
        "# Convolutional Layer 2.\n",
        "filter_size2 = 4\n",
        "num_filters2 = 64\n",
        "# Convolutional Layer 3.\n",
        "filter_size3 = 4\n",
        "num_filters3 = 128\n",
        "# Convolutional Layer 4\n",
        "filter_size4 = 4\n",
        "num_filters4 = 256\n",
        "# Convolutional Layer 5\n",
        "filter_size5 = 4\n",
        "num_filters5 = 128\n",
        "# Fully-connected layer.\n",
        "fc_size = 1024         \n",
        "# Number of color channels for the images: 1 channel for gray-scale.  ########################우선 train컬러 흑백으로 1개...?\n",
        "num_channels = 1\n",
        "# image dimensions (only squares for now)   #################\n",
        "img_size = 28\n",
        "# Size of image when flattened to a single dimension\n",
        "img_size_flat = img_size * img_size * num_channels\n",
        "# Tuple with height and width of images used to reshape arrays.\n",
        "img_shape = (img_size, img_size)\n",
        "# class 위에 순서 맞게 써줌\n",
        "classes = ['tulip', 'rose','cosmos', 'cherryblossom','sunflower','koreaflower','lily', 'buckwheat', 'korearosebay','forsythia']\n",
        "num_classes = len(classes)\n",
        "# batch size\n",
        "batch_size = 50\n",
        "\n",
        "\n",
        "#data 불러옴\n",
        "\n",
        "train_data = np.load('flower_train_data.npy',allow_pickle=True)\n",
        "\n",
        "def new_weights(shape):\n",
        "    return tf.Variable(tf.truncated_normal(shape, stddev=0.05))\n",
        "def new_biases(length):\n",
        "    return tf.Variable(tf.constant(0.05, shape=[length]))\n",
        "\n",
        "def new_conv_layer(input,num_input_channels,filter_size,num_filters,use_pooling=True):  \n",
        "\n",
        "    shape = [filter_size, filter_size, num_input_channels, num_filters]\n",
        "    weights = new_weights(shape=shape)\n",
        "    biases = new_biases(length=num_filters)\n",
        "\n",
        "    layer = tf.nn.conv2d(input=input,filter=weights,strides=[1, 1, 1, 1],padding='SAME')\n",
        "    layer += biases\n",
        "\n",
        "    if use_pooling:\n",
        "        layer = tf.nn.max_pool(value=layer,ksize=[1, 2, 2, 1],strides=[1, 2, 2, 1],padding='SAME')\n",
        "\n",
        "    layer = tf.nn.relu(layer)\n",
        "\n",
        "    return layer, weights\n",
        "\n",
        "def new_fc_layer(input,num_inputs,num_outputs,use_relu=True): \n",
        "\n",
        "    weights = new_weights(shape=[num_inputs, num_outputs])\n",
        "    biases = new_biases(length=num_outputs)\n",
        "\n",
        "    layer = tf.matmul(input, weights) + biases\n",
        "    \n",
        "    if use_relu:\n",
        "        layer = tf.nn.relu(layer)\n",
        "\n",
        "    return layer\n",
        "\n",
        "def flatten_layer(layer):\n",
        "    \n",
        "    layer_shape = layer.get_shape()\n",
        "    num_features = layer_shape[1:4].num_elements()\n",
        "    layer_flat = tf.reshape(layer, [-1, num_features])\n",
        "\n",
        "    return layer_flat, num_features\n",
        "\n",
        "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')\n",
        "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])\n",
        "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')\n",
        "y_true_cls = tf.argmax(y_true, axis=1)\n",
        "\n",
        "layer_conv1, weights_conv1 = new_conv_layer(input=x_image,num_input_channels=num_channels,filter_size=filter_size1,\n",
        "                                            num_filters=num_filters1,use_pooling=True)\n",
        "layer_conv2, weights_conv2 = new_conv_layer(input=layer_conv1,num_input_channels=num_filters1,filter_size=filter_size2,\n",
        "                                            num_filters=num_filters2,use_pooling=True)\n",
        "layer_conv3, weights_conv3 = new_conv_layer(input=layer_conv2,num_input_channels=num_filters2,filter_size=filter_size3,\n",
        "                                            num_filters=num_filters3,use_pooling=True)\n",
        "layer_conv4, weights_conv4 = new_conv_layer(input=layer_conv3,num_input_channels=num_filters3,filter_size=filter_size4,\n",
        "                                            num_filters=num_filters4,use_pooling=True)\n",
        "layer_conv5, weights_conv5 = new_conv_layer(input=layer_conv4,num_input_channels=num_filters4,filter_size=filter_size5,\n",
        "                                            num_filters=num_filters5,use_pooling=True)\n",
        "\n",
        "layer_flat, num_features = flatten_layer(layer_conv5)\n",
        "layer_fc1 = new_fc_layer(input=layer_flat,num_inputs=num_features,num_outputs=fc_size,use_relu=True)\n",
        "layer_fc2 = new_fc_layer(input=layer_fc1,num_inputs=fc_size,num_outputs=num_classes,use_relu=False)\n",
        "\n",
        "y_pred = tf.nn.softmax(layer_fc2)\n",
        "y_pred_cls = tf.argmax(y_pred, axis=1)\n",
        "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,labels=y_true)\n",
        "cost = tf.reduce_mean(cross_entropy)\n",
        "\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(cost)\n",
        "correct_prediction = tf.equal(y_pred_cls, y_true_cls)\n",
        "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
        "\n",
        "train = train_data[0:-500]\n",
        "\n",
        "x_batch = np.array([i[0] for i in train]).reshape(len(train),img_size_flat)\n",
        "y_true_batch = [i[1] for i in train]\n",
        "\n",
        "session = tf.Session()\n",
        "session.run(tf.global_variables_initializer())\n",
        "total_iterations = 0\n",
        "\n",
        "def optimize(num_iterations):\n",
        "    \n",
        "    global total_iterations\n",
        "\n",
        "    for i in range(total_iterations,total_iterations + num_iterations):\n",
        "    \ta = 0\n",
        "    \tfor __ in range(int(len(train)/batch_size)):\n",
        "        \t#feed_dict_train = {x: x_batch[a:a+batch_size,:],y_true: y_true_batch[a:a+batch_size]}\n",
        "        \tsession.run(optimizer, feed_dict={x: x_batch[a:a+batch_size,:],y_true: y_true_batch[a:a+batch_size]})\n",
        "        \ta = a + batch_size\n",
        " \t\t\n",
        "    \tif i % 2 == 0:\n",
        "    \t\tprint(\"Iteration = \", i, \"Loss = \", session.run(cost, feed_dict={x: x_batch[a:a+batch_size,:],y_true: y_true_batch[a:a+batch_size]}),\n",
        "                      \"Train Accuracy = \", session.run(accuracy, feed_dict={x: x_batch[a:a+batch_size,:],y_true: y_true_batch[a:a+batch_size]}), \n",
        "                      )\n",
        "\n",
        "optimize(num_iterations=50)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-4411181267b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0mcheckpoint_dir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"models/\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0mtrain_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'flower_train_data.npy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnew_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    420\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 422\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    423\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'flower_train_data.npy'"
          ]
        }
      ]
    }
  ]
}