43. notmnist_cnn.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"43_notmnist_cnn.ipynb","version":"0.3.2","views":{},"default_view":{},"provenance":[],"collapsed_sections":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"}},"cells":[{"metadata":{"id":"k0uBiN-7JxWD","colab_type":"text"},"cell_type":"markdown","source":["# 43. notMNIST/FashionMNIST CNN\n","\n","<p style=\"text-align: right;\">\n","blackdew7@gmail.com<br>\n","Your name :\n","</p>\n","\n","\n","#### 선행지식\n","1. TensorFlow 다루기 기초\n","2. 모델링을 한다는 것에 대한 이해.\n","3. Supervised Learning 중 Classification에 대한 기본 지식.\n","4. CNN에 대한 기본 구조와 개념\n","\n","#### 실습목표\n","1. CNN의 구조를 Graph로 그려낼 수 있다.\n","2. 그려낸 Graph를 텐서플로우를 이용해 코딩할 수 있다.\n","\n","#### 사용데이터.\n","\n","01. Multinomial Classification : http://yann.lecun.com/exdb/mnist/"]},{"metadata":{"id":"xAWyCEf_JxWE","colab_type":"text"},"cell_type":"markdown","source":[""]},{"metadata":{"id":"Dhv50_vEJxWW","colab_type":"text"},"cell_type":"markdown","source":["## 00. 이미지 로더"]},{"metadata":{"id":"NKQnFB9VkBkO","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["from scipy.misc import imread, imresize\n","from sklearn.model_selection import train_test_split\n","\n","import os\n","import numpy as np\n","import matplotlib.pyplot as plt\n","\n","class ImageLoader:\n","    def __init__(self, path, folders):\n","        images, labels = [], []\n","\n","        for i, folder_name in enumerate(folders):\n","            directory = path + folder_name + '/'\n","            files = os.listdir(directory)\n","            label = np.array([0] * 10)\n","            label[i] = 1\n","            for file in files:\n","                try:\n","                    im = imread(directory + file)\n","                except:\n","                    print(\"Skip a corrupted file: \" + file)\n","                    continue\n","                pixels = np.array(im.astype(float))\n","                images.append(pixels / 255.0)\n","                labels.append(label)\n","\n","        train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.2, random_state=0)\n","        \n","        class train:\n","            def __init__(self):\n","                self.images = []\n","                self.labels = []\n","                self.batch_counter = 0\n","                self.num_examples = 0\n","\n","            def get_num_examples(self):\n","                    return len(self.images)\n","\n","            def next_batch(self, num):\n","                if self.batch_counter + num >= len(self.labels):\n","                    batch_images = self.images[self.batch_counter:]\n","                    batch_labels = self.labels[self.batch_counter:]\n","                    left = num - len(batch_labels)\n","                    batch_images.extend(self.images[:left])\n","                    batch_labels.extend(self.labels[:left])\n","                    self.batch_counter = left\n","                else:\n","                    batch_images = self.images[self.batch_counter:self.batch_counter+num]\n","                    batch_labels = self.labels[self.batch_counter:self.batch_counter+num]                  \n","                    self.batch_counter += num\n","                    \n","                return (batch_images, batch_labels)\n","                    \n","        class test:\n","            def __init__(self):\n","                self.images = []\n","                self.labels = []\n","                \n","        self.train = train()\n","        self.test = test()\n","\n","        self.train.images = train_images\n","        self.train.labels = train_labels\n","        self.test.images = test_images\n","        self.test.labels = test_labels"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6YU7m5FInET_","colab_type":"text"},"cell_type":"markdown","source":["#### 파일 업로드 \n","notMNIST_small.tar.gz"]},{"metadata":{"id":"1EWSPOQ4mwmo","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# file update\n","from google.colab import files\n","uploaded = files.upload() # then browse, select the files. It's then uploaded"],"execution_count":0,"outputs":[]},{"metadata":{"id":"daTDztKCmw83","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# notMNIST_small.tar.gz 압축해제\n","import sys\n","import tarfile\n","\n","tar = tarfile.open('./notMNIST_small.tar.gz')\n","sys.stdout.flush()\n","tar.extractall('./')\n","tar.close()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"gymTtEqkniXR","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["!ls -al"],"execution_count":0,"outputs":[]},{"metadata":{"id":"N0CuvZw6nQgs","colab_type":"text"},"cell_type":"markdown","source":["#### 이미지 로딩"]},{"metadata":{"id":"a6jxeK4TJxWa","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["mnist = ImageLoader('./notMNIST_small/', ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'])"],"execution_count":0,"outputs":[]},{"metadata":{"id":"z-9671g7JxWf","colab_type":"text"},"cell_type":"markdown","source":["#### 이미지 로딩 테스트"]},{"metadata":{"id":"53LSsY7gJxWf","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["print(len(mnist.train.images))   # Images in the training set.\n","print(len(mnist.train.labels))   # Labels in the training set.\n","print(len(mnist.test.images))    # Images in the test set.\n","print(len(mnist.test.labels))    # Labels in the test set.\n","\n","train, label = mnist.train.next_batch(100) # To retrieve specified number of data in a batch\n","\n","print(np.array(train).shape)\n","print(np.array(label).shape)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"rP-cs-Mdm3mB","colab_type":"text"},"cell_type":"markdown","source":["# notMNIST CNN 실습"]},{"metadata":{"id":"uYWwkLSQvGwC","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["'''\n","mnist 시각화 함수\n","'''\n","def mnist_plot(i):\n","    pixels = mnist.test.images[i].reshape((28, 28))\n","    a = mnist.test.labels\n","    plt.title('Label : {}'.format(a[i]))\n","    plt.imshow(pixels, cmap='gray')\n","    return plt.show()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"UKYnF2lmvJNq","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["'''\n","반복실행해보자\n","'''\n","mnist_plot(np.random.randint(0, high=9999))"],"execution_count":0,"outputs":[]},{"metadata":{"id":"HV2TUR1ym42v","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# 데이터 셋을 로딩하여 train, test 데이터를 만들자 \n","\n","\n"],"execution_count":0,"outputs":[]},{"metadata":{"id":"P2DqgMoDogtq","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# 텐서플로우 그래프를 작성해보자\n","\n","\n"],"execution_count":0,"outputs":[]},{"metadata":{"id":"ew1xdIVpoqgf","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# 만든 CNN 모델을 학습시키자\n","\n","\n"],"execution_count":0,"outputs":[]},{"metadata":{"id":"31PQUlObownv","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"base_uri":"https://localhost:8080/","height":37},"outputId":"4a328b15-556a-4f6e-ac3d-3e89218d7951","executionInfo":{"status":"ok","timestamp":1530025698116,"user_tz":-540,"elapsed":948,"user":{"displayName":"이숙번","photoUrl":"//lh5.googleusercontent.com/-WSqlBEMdoyA/AAAAAAAAAAI/AAAAAAAAn4c/ubBeIeyrnb4/s50-c-k-no/photo.jpg","userId":"104266208940946892796"}}},"cell_type":"code","source":["# 완성된 모델을 검증하는 코드를 작성하자\n","\n","\n"],"execution_count":27,"outputs":[]},{"metadata":{"id":"lphOglXjJxXd","colab_type":"text"},"cell_type":"markdown","source":["## 대체 뭘 학습한 것인가?"]},{"metadata":{"id":"98sQRxNCJxXd","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","import math\n","import numpy as np\n","\n","def plot_conv_weights(weights, input_channel=0):\n","    \n","    with tf.Session() as sess:\n","        sess.run(tf.global_variables_initializer())\n","\n","        w = sess.run(weights)\n","\n","        w_min = np.min(w)\n","        w_max = np.max(w)\n","\n","        num_filters = w.shape[3]\n","        num_grids = math.ceil(math.sqrt(num_filters))\n","        fig, axes = plt.subplots(num_grids, num_grids)\n","\n","        for i, ax in enumerate(axes.flat):\n","            if i < num_filters:\n","                img = w[:, :, input_channel, i]\n","                ax.imshow(img, vmin=w_min, vmax=w_max, interpolation='nearest', cmap='seismic')\n","\n","            ax.set_xticks([])\n","            ax.set_yticks([])\n","\n","        plt.show()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"pNu2aNtXJxXi","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["plot_conv_weights(weights=W1)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"oxtUEIxFJxXl","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["%matplotlib inline\n","import matplotlib.pyplot as plt\n","import math\n","import numpy as np\n","\n","def plot_conv_layer(layer, image):\n","\n","    with tf.Session() as sess:\n","\n","        sess.run(tf.global_variables_initializer())\n","\n","        feed_dict = {X: [image]}\n","        values = sess.run(layer, feed_dict=feed_dict)\n","\n","        num_filters = values.shape[3]\n","        num_grids = math.ceil(math.sqrt(num_filters))\n","        fig, axes = plt.subplots(num_grids, num_grids)\n","\n","        for i, ax in enumerate(axes.flat):\n","            if i < num_filters:\n","                img = values[0, :, :, i]\n","                ax.imshow(img, interpolation='nearest', cmap='binary')\n","                \n","            ax.set_xticks([])\n","            ax.set_yticks([])\n","\n","        plt.show()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"_sWilzHbJxXp","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["img_shape = (28, 28)\n","\n","def plot_image(image):\n","    plt.imshow(image.reshape(img_shape),\n","               interpolation='nearest',\n","               cmap='binary')\n","\n","    plt.show()\n","    \n","image1 = mnist.test.images[0]\n","plot_image(image1)\n","\n","image2 = mnist.test.images[1]\n","plot_image(image2)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"zCkMVCs6JxXs","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["plot_conv_layer(layer=pool1, image=image1)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"r1_xLgOko8xT","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["plot_conv_layer(layer=pool2, image=image1)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"3qXVyQIXuohz","colab_type":"text"},"cell_type":"markdown","source":["# Fashion MNIST CNN 실습"]},{"metadata":{"id":"cONfpmnvupII","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"base_uri":"https://localhost:8080/","height":233},"outputId":"b2b5631f-b099-4e23-bcf6-691115d6d189","executionInfo":{"status":"ok","timestamp":1530160831193,"user_tz":-540,"elapsed":15504,"user":{"displayName":"이숙번","photoUrl":"//lh5.googleusercontent.com/-WSqlBEMdoyA/AAAAAAAAAAI/AAAAAAAAn4c/ubBeIeyrnb4/s50-c-k-no/photo.jpg","userId":"104266208940946892796"}}},"cell_type":"code","source":["'''\n","트레이닝 데이터로 활용할 60000개 이미지, 테스트용 10000개 이미지\n","28*28 사이즈의 이미지가 그냥 주욱 784칸 짜리 어레이에 담겨있다.\n","test, traing 데이터가 분리되어 있다.\n","레이블은 one-hot encoding을 해주어야 한다. \n","'''\n","import tensorflow as tf\n","import numpy as np\n","\n","(train_x, train_y), (test_x, test_y) = tf.keras.datasets.fashion_mnist.load_data()\n","\n","print(train_x.shape)\n","print(train_y.shape)\n","print(test_x.shape)\n","print(test_y.shape)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n","32768/29515 [=================================] - 0s 3us/step\n","Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n","26427392/26421880 [==============================] - 8s 0us/step\n","Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n","8192/5148 [===============================================] - 0s 0us/step\n","Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n","4423680/4422102 [==============================] - 4s 1us/step\n","(60000, 28, 28)\n","(60000,)\n","(10000, 28, 28)\n","(10000,)\n"],"name":"stdout"}]},{"metadata":{"id":"rC5pp17hux0j","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["'''\n","Fashion mnist 시각화 함수\n","'''\n","def fashion_plot(i):\n","    pixels = test_x[i].reshape((28, 28))\n","    a = test_y.tolist()\n","    plt.title('Label : {}'.format(a[i]))\n","    plt.imshow(pixels, cmap='gray')\n","    return plt.show()"],"execution_count":0,"outputs":[]},{"metadata":{"id":"IBOb_zFJuyX2","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["'''\n","반복실행해보자\n","'''\n","fashion_plot(np.random.randint(0, high=9999))"],"execution_count":0,"outputs":[]},{"metadata":{"id":"lNmfjhMiu0PV","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# 데이터 셋을 로딩하여 train, test 데이터를 만들자 \n","\n","import tensorflow as tf\n","import numpy as np\n","\n"],"execution_count":0,"outputs":[]},{"metadata":{"id":"zoHWmYDku0bP","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# 텐서플로우 그래프를 작성해보자\n","# Graph Clear\n","tf.reset_default_graph()\n","tf.set_random_seed(2017) # random seeding - reproduct\n","\n","############################\n","# Place Holders\n","\n","X = tf.placeholder(tf.float32, [None, 28, 28, 1])\n","Y = tf.placeholder(tf.float32, [None, 10])\n","\n","lr = tf.placeholder(tf.float32)\n","kp = tf.placeholder(tf.float32)\n","\n","# for Conv Layer 01 filter - shape=(3, 3, 1)\n","w1 = tf.Variable(tf.random_normal([3, 3, 1, 64], stddev=0.01))\n","b1 = tf.Variable(tf.random_normal([64], stddev=0.01))\n","conv1 = tf.nn.conv2d(X, w1, strides=[1, 1, 1, 1], padding='SAME')\n","conv1 = tf.add(conv1, b1)\n","conv1 = tf.nn.relu(conv1)\n","pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n","\n","# for Conv Layer 02 filter - shape=(3, 3, 32)\n","w2 = tf.Variable(tf.random_normal([3, 3, 64, 128], stddev=0.01))\n","b2 = tf.Variable(tf.random_normal([128], stddev=0.01))\n","conv2 = tf.nn.conv2d(pool1, w2, strides=[1, 1, 1, 1], padding='SAME')\n","conv2 = tf.add(conv2, b2)\n","conv2 = tf.nn.relu(conv2)\n","pool2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n","\n","# pool2 => (?, 7, 7, 128)\n","\n","# fully connected layer 1\n","flat = tf.reshape(pool2, [-1, 7 * 7 * 128])\n","wfc1 = tf.Variable(tf.random_normal([7 * 7 * 128, 128], stddev=0.01))\n","bfc1 = tf.Variable(tf.random_normal([128], stddev=0.01))\n","fc1 = tf.add(tf.matmul(flat, wfc1), bfc1)\n","fc1 = tf.nn.relu(fc1, name=\"L1_fc_relu\")\n","\n","# dropout\n","fc_drop = tf.nn.dropout(fc1, keep_prob=kp)\n","\n","# fully connected layer 2\n","wfc2 = tf.Variable(tf.random_normal([128, 10], stddev=0.01))\n","bfc2 = tf.Variable(tf.random_normal([10], stddev=0.01))\n","logits = tf.add(tf.matmul(fc_drop, wfc2), bfc2)\n","\n","\n","# Cost(loss) function & Optimizer\n","cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y))\n","optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(cost)"],"execution_count":0,"outputs":[]},{"metadata":{"id":"VBoCCT-5u3nD","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0},"base_uri":"https://localhost:8080/","height":1181},"outputId":"b71118cd-cd9a-492a-e717-4a1320a06ce8","executionInfo":{"status":"error","timestamp":1530161642759,"user_tz":-540,"elapsed":493413,"user":{"displayName":"이숙번","photoUrl":"//lh5.googleusercontent.com/-WSqlBEMdoyA/AAAAAAAAAAI/AAAAAAAAn4c/ubBeIeyrnb4/s50-c-k-no/photo.jpg","userId":"104266208940946892796"}}},"cell_type":"code","source":["# 만든 CNN 모델을 학습시키자\n","\n","###########################\n","# Initialize\n","sess = tf.Session()\n","sess.run(tf.global_variables_initializer())\n","\n","###########################\n","# Training\n","\n","print('Learning started. It takes sometime.')\n","\n","\n","# 총 갯수: 55000개를 500개씩 (batch_size) 나누어 훈련 \n","#        => 110번 (n_of_batches) 훈련하게 된다. \n","epochs = 3\n","batch_size = 512\n","n_of_batches = int(len(train_x) / batch_size)\n","\n","for epoch in range(epochs):\n","    print(\"%dth epoch\" % (epoch + 1))\n","    \n","    for i in range(n_of_batches):\n","        X_batch = train_x[batch_size * i: batch_size * (i + 1)].reshape(-1, 28, 28, 1)\n","        Y_batch = train_y[batch_size * i: batch_size * (i + 1)]\n","        # 학습 진행\n","        sess.run(optimizer, feed_dict={X: X_batch, Y: np.eye(10)[Y_batch], kp: 0.7, lr: 0.01})\n","        \n","        if ((i + 1) % 10 == 0):\n","            # 학습 상황 디스플레이\n","            loss = sess.run(cost, feed_dict={X: X_batch, Y: np.eye(10)[Y_batch], \n","                                             kp: 1.0})\n","            print(\"%dth records, training cost: %.3f\" % (((i + 1) * batch_size), loss))\n","            \n","###########################\n","# Evaluation\n","\n","# Test model and check accuracy\n","correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))\n","acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n","accuracy = sess.run(acc, feed_dict={X: test_x.reshape(-1, 28, 28, 1), \n","                                    Y: np.eye(10)[test_y], kp: 1.0})\n","\n","print('Accuracy: %.2f' % (accuracy * 100))\n","\n"],"execution_count":11,"outputs":[{"output_type":"stream","text":["Learning started. It takes sometime.\n","1th epoch\n","5120th records, training cost: 1.003\n","10240th records, training cost: 0.719\n","15360th records, training cost: 0.591\n","20480th records, training cost: 0.523\n","25600th records, training cost: 0.413\n","30720th records, training cost: 0.379\n","35840th records, training cost: 0.460\n","40960th records, training cost: 0.410\n","46080th records, training cost: 0.381\n","51200th records, training cost: 0.327\n","56320th records, training cost: 0.338\n","2th epoch\n","5120th records, training cost: 0.295\n","10240th records, training cost: 0.332\n","15360th records, training cost: 0.301\n","20480th records, training cost: 0.330\n","25600th records, training cost: 0.284\n","30720th records, training cost: 0.286\n","35840th records, training cost: 0.318\n","40960th records, training cost: 0.353\n","46080th records, training cost: 0.313\n","51200th records, training cost: 0.265\n","56320th records, training cost: 0.278\n","3th epoch\n","5120th records, training cost: 0.252\n","10240th records, training cost: 0.270\n","15360th records, training cost: 0.262\n","20480th records, training cost: 0.280\n","25600th records, training cost: 0.265\n","30720th records, training cost: 0.257\n","35840th records, training cost: 0.275\n","40960th records, training cost: 0.306\n","46080th records, training cost: 0.285\n","51200th records, training cost: 0.233\n","56320th records, training cost: 0.256\n"],"name":"stdout"},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-11-7e8f8ee429ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0mcorrect_prediction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce_mean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcorrect_prediction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtest_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meye\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkp\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1.0\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     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy: %.2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maccuracy\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\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/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    898\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    899\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 900\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    901\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\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/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1109\u001b[0m                              \u001b[0;34m'which has shape %r'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1110\u001b[0m                              (np_val.shape, subfeed_t.name,\n\u001b[0;32m-> 1111\u001b[0;31m                               str(subfeed_t.get_shape())))\n\u001b[0m\u001b[1;32m   1112\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_feedable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1113\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Tensor %s may not be fed.'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0msubfeed_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mValueError\u001b[0m: Cannot feed value of shape (10000, 28, 28) for Tensor 'Placeholder:0', which has shape '(?, 28, 28, 1)'"]}]},{"metadata":{"id":"igovk3KAu6wO","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# 완성된 모델을 검증하는 코드를 작성하자\n","\n","\n"],"execution_count":0,"outputs":[]},{"metadata":{"id":"6jcDVvl3JxXv","colab_type":"code","colab":{"autoexec":{"startup":false,"wait_interval":0}}},"cell_type":"code","source":["# 세션을 종료하자\n","sess.close()"],"execution_count":0,"outputs":[]}]}