退会済みユーザー
2017/10/10 22:08 投稿
| TensorFlowを用いた画像認識（CNN法）で用いる画像のサイズが分からない． |
| 以下のコードでCNNを用いたニューラルネットワークを構築する際，データセットの画像サイズを統一しなければならないか知りたいです． |
| どなたか詳しい方教えて頂けませんでしょうか． |
| ``` |
| #!/usr/bin/env python |
| # -*- coding: utf-8 -*- |
| import sys |
| import cv2 |
| import numpy as np |
| import tensorflow as tf |
| import tensorflow.python.platform |
| NUM_CLASSES = 12 |
| IMAGE_SIZE = 28 |
| IMAGE_PIXELS = IMAGE_SIZE*IMAGE_SIZE*3 |
| flags = tf.app.flags |
| FLAGS = flags.FLAGS |
| flags.DEFINE_string('train', 'train.txt', 'File name of train data') |
| flags.DEFINE_string('test', 'test.txt', 'File name of train data') |
| flags.DEFINE_string('train_dir', '/tmp/data', 'Directory to put the training data.') |
| flags.DEFINE_integer('max_steps', 200, 'Number of steps to run trainer.') |
| flags.DEFINE_integer('batch_size', 10, 'Batch size' |
| 'Must divide evenly into the dataset sizes.') |
| flags.DEFINE_float('learning_rate', 1e-4, 'Initial learning rate.') |
| def inference(images_placeholder, keep_prob): |
| """ 予測モデルを作成する関数 |
| 引数: |
| images_placeholder: 画像のplaceholder |
| keep_prob: dropout率のplace_holder |
| 返り値: |
| y_conv: 各クラスの確率(のようなもの) |
| """ |
| # 重みを標準偏差0.1の正規分布で初期化 |
| def weight_variable(shape): |
| initial = tf.truncated_normal(shape, stddev=0.1) |
| return tf.Variable(initial) |
| # バイアスを標準偏差0.1の正規分布で初期化 |
| def bias_variable(shape): |
| initial = tf.constant(0.1, shape=shape) |
| return tf.Variable(initial) |
| # 畳み込み層の作成 |
| def conv2d(x, W): |
| return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') |
| # プーリング層の作成 |
| def max_pool_2x2(x): |
| return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], |
| strides=[1, 2, 2, 1], padding='SAME') |
| # 入力を28x28x3に変形 |
| x_image = tf.reshape(images_placeholder, [-1, 28, 28, 3]) |
| # 畳み込み層1の作成 |
| with tf.name_scope('conv1') as scope: |
| W_conv1 = weight_variable([5, 5, 3, 32]) |
| b_conv1 = bias_variable([32]) |
| h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) |
| # プーリング層1の作成 |
| with tf.name_scope('pool1') as scope: |
| h_pool1 = max_pool_2x2(h_conv1) |
| # 畳み込み層2の作成 |
| with tf.name_scope('conv2') as scope: |
| W_conv2 = weight_variable([5, 5, 32, 64]) |
| b_conv2 = bias_variable([64]) |
| h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) |
| # プーリング層2の作成 |
| with tf.name_scope('pool2') as scope: |
| h_pool2 = max_pool_2x2(h_conv2) |
| # 全結合層1の作成 |
| with tf.name_scope('fc1') as scope: |
| W_fc1 = weight_variable([7*7*64, 1024]) |
| b_fc1 = bias_variable([1024]) |
| h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) |
| h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) |
| # dropoutの設定 |
| h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) |
| # 全結合層2の作成 |
| with tf.name_scope('fc2') as scope: |
| W_fc2 = weight_variable([1024, NUM_CLASSES]) |
| b_fc2 = bias_variable([NUM_CLASSES]) |
| # ソフトマックス関数による正規化 |
| with tf.name_scope('softmax') as scope: |
| y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) |
| # 各ラベルの確率のようなものを返す |
| return y_conv |
| def loss(logits, labels): |
| """ lossを計算する関数 |
| 引数: |
| logits: ロジットのtensor, float - [batch_size, NUM_CLASSES] |
| labels: ラベルのtensor, int32 - [batch_size, NUM_CLASSES] |
| 返り値: |
| cross_entropy: 交差エントロピーのtensor, float |
| """ |
| # 交差エントロピーの計算 |
| cross_entropy = -tf.reduce_sum(labels*tf.log(logits)) |
| # TensorBoardで表示するよう指定 |
| tf.scalar_summary("cross_entropy", cross_entropy) |
| return cross_entropy |
| def training(loss, learning_rate): |
| """ 訓練のOpを定義する関数 |
| 引数: |
| loss: 損失のtensor, loss()の結果 |
| learning_rate: 学習係数 |
| 返り値: |
| train_step: 訓練のOp |
| """ |
| train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss) |
| return train_step |
| def accuracy(logits, labels): |
| """ 正解率(accuracy)を計算する関数 |
| 引数: |
| logits: inference()の結果 |
| labels: ラベルのtensor, int32 - [batch_size, NUM_CLASSES] |
| 返り値: |
| accuracy: 正解率(float) |
| """ |
| correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) |
| accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) |
| tf.scalar_summary("accuracy", accuracy) |
| return accuracy |
| if __name__ == '__main__': |
| # ファイルを開く |
| f = open(FLAGS.train, 'r') |
| # データを入れる配列 |
| train_image = [] |
| train_label = [] |
| for line in f: |
| # 改行を除いてスペース区切りにする |
| line = line.rstrip() |
| l = line.split() |
| # データを読み込んで28x28に縮小 |
| img = cv2.imread(l[0]) |
| img = cv2.resize(img, (28, 28)) |
| # 一列にした後、0-1のfloat値にする |
| train_image.append(img.flatten().astype(np.float32)/255.0) |
| # ラベルを1-of-k方式で用意する |
| tmp = np.zeros(NUM_CLASSES) |
| tmp[int(l[1])] = 1 |
| train_label.append(tmp) |
| # numpy形式に変換 |
| train_image = np.asarray(train_image) |
| train_label = np.asarray(train_label) |
| f.close() |
| f = open(FLAGS.test, 'r') |
| test_image = [] |
| test_label = [] |
| for line in f: |
| line = line.rstrip() |
| l = line.split() |
| img = cv2.imread(l[0]) |
| img = cv2.resize(img, (28, 28)) |
| test_image.append(img.flatten().astype(np.float32)/255.0) |
| tmp = np.zeros(NUM_CLASSES) |
| tmp[int(l[1])] = 1 |
| test_label.append(tmp) |
| test_image = np.asarray(test_image) |
| test_label = np.asarray(test_label) |
| f.close() |
| with tf.Graph().as_default(): |
| # 画像を入れる仮のTensor |
| images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS)) |
| # ラベルを入れる仮のTensor |
| labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES)) |
| # dropout率を入れる仮のTensor |
| keep_prob = tf.placeholder("float") |
| # inference()を呼び出してモデルを作る |
| logits = inference(images_placeholder, keep_prob) |
| # loss()を呼び出して損失を計算 |
| loss_value = loss(logits, labels_placeholder) |
| # training()を呼び出して訓練 |
| train_op = training(loss_value, FLAGS.learning_rate) |
| # 精度の計算 |
| acc = accuracy(logits, labels_placeholder) |
| # 保存の準備 |
| saver = tf.train.Saver() |
| # Sessionの作成 |
| sess = tf.Session() |
| # 変数の初期化 |
| sess.run(tf.initialize_all_variables()) |
| # TensorBoardで表示する値の設定 |
| summary_op = tf.merge_all_summaries() |
| summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph_def) |
| # 訓練の実行 |
| for step in range(FLAGS.max_steps): |
| for i in range(len(train_image)/FLAGS.batch_size): |
| # batch_size分の画像に対して訓練の実行 |
| batch = FLAGS.batch_size*i |
| # feed_dictでplaceholderに入れるデータを指定する |
| sess.run(train_op, feed_dict={ |
| images_placeholder: train_image[batch:batch+FLAGS.batch_size], |
| labels_placeholder: train_label[batch:batch+FLAGS.batch_size], |
| keep_prob: 0.5}) |
| # 1 step終わるたびに精度を計算する |
| train_accuracy = sess.run(acc, feed_dict={ |
| images_placeholder: train_image, |
| labels_placeholder: train_label, |
| keep_prob: 1.0}) |
| print "step %d, training accuracy %g"%(step, train_accuracy) |
| # 1 step終わるたびにTensorBoardに表示する値を追加する |
| summary_str = sess.run(summary_op, feed_dict={ |
| images_placeholder: train_image, |
| labels_placeholder: train_label, |
| keep_prob: 1.0}) |
| summary_writer.add_summary(summary_str, step) |
| # 訓練が終了したらテストデータに対する精度を表示 |
| print "test accuracy %g"%sess.run(acc, feed_dict={ |
| images_placeholder: test_image, |
| labels_placeholder: test_label, |
| keep_prob: 1.0}) |
| # 最終的なモデルを保存 |
| save_path = saver.save(sess, "model.ckpt") |
| ``` |
退会済みユーザー
2017/10/10 22:08 投稿
| TensorFlowを用いた画像認識（CNN法）で用いる画像のサイズが分からない． |
| 以下のコードでCNNを用いたニューラルネットワークを構築する際，データセットの画像サイズを統一しなければならないか知りたいです． |
| どなたか詳しい方教えて頂けませんでしょうか． |
| ``` |
| #!/usr/bin/env python |
| # -*- coding: utf-8 -*- |
| import sys |
| import cv2 |
| import numpy as np |
| import tensorflow as tf |
| import tensorflow.python.platform |
| NUM_CLASSES = |
| NUM_CLASSES = 12 |
| IMAGE_SIZE = 28 |
| IMAGE_PIXELS = IMAGE_SIZE*IMAGE_SIZE*3 |
| flags = tf.app.flags |
| FLAGS = flags.FLAGS |
| flags.DEFINE_string('train', 'train.txt', 'File name of train data') |
| flags.DEFINE_string('test', 'test.txt', 'File name of train data') |
| flags.DEFINE_string('train_dir', '/tmp/data', 'Directory to put the training data.') |
| flags.DEFINE_integer('max_steps', 200, 'Number of steps to run trainer.') |
| flags.DEFINE_integer('batch_size', 10, 'Batch size' |
| 'Must divide evenly into the dataset sizes.') |
| flags.DEFINE_float('learning_rate', 1e-4, 'Initial learning rate.') |
| def inference(images_placeholder, keep_prob): |
| """ 予測モデルを作成する関数 |
| 引数: |
| images_placeholder: 画像のplaceholder |
| keep_prob: dropout率のplace_holder |
| 返り値: |
| y_conv: 各クラスの確率(のようなもの) |
| """ |
| # 重みを標準偏差0.1の正規分布で初期化 |
| def weight_variable(shape): |
| initial = tf.truncated_normal(shape, stddev=0.1) |
| return tf.Variable(initial) |
| # バイアスを標準偏差0.1の正規分布で初期化 |
| def bias_variable(shape): |
| initial = tf.constant(0.1, shape=shape) |
| return tf.Variable(initial) |
| # 畳み込み層の作成 |
| def conv2d(x, W): |
| return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') |
| # プーリング層の作成 |
| def max_pool_2x2(x): |
| return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], |
| strides=[1, 2, 2, 1], padding='SAME') |
| # 入力を28x28x3に変形 |
| x_image = tf.reshape(images_placeholder, [-1, 28, 28, 3]) |
| # 畳み込み層1の作成 |
| with tf.name_scope('conv1') as scope: |
| W_conv1 = weight_variable([5, 5, 3, 32]) |
| b_conv1 = bias_variable([32]) |
| h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) |
| # プーリング層1の作成 |
| with tf.name_scope('pool1') as scope: |
| h_pool1 = max_pool_2x2(h_conv1) |
| # 畳み込み層2の作成 |
| with tf.name_scope('conv2') as scope: |
| W_conv2 = weight_variable([5, 5, 32, 64]) |
| b_conv2 = bias_variable([64]) |
| h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2) |
| # プーリング層2の作成 |
| with tf.name_scope('pool2') as scope: |
| h_pool2 = max_pool_2x2(h_conv2) |
| # 全結合層1の作成 |
| with tf.name_scope('fc1') as scope: |
| W_fc1 = weight_variable([7*7*64, 1024]) |
| b_fc1 = bias_variable([1024]) |
| h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64]) |
| h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) |
| # dropoutの設定 |
| h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) |
| # 全結合層2の作成 |
| with tf.name_scope('fc2') as scope: |
| W_fc2 = weight_variable([1024, NUM_CLASSES]) |
| b_fc2 = bias_variable([NUM_CLASSES]) |
| # ソフトマックス関数による正規化 |
| with tf.name_scope('softmax') as scope: |
| y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2) |
| # 各ラベルの確率のようなものを返す |
| return y_conv |
| def loss(logits, labels): |
| """ lossを計算する関数 |
| 引数: |
| logits: ロジットのtensor, float - [batch_size, NUM_CLASSES] |
| labels: ラベルのtensor, int32 - [batch_size, NUM_CLASSES] |
| 返り値: |
| cross_entropy: 交差エントロピーのtensor, float |
| """ |
| # 交差エントロピーの計算 |
| cross_entropy = -tf.reduce_sum(labels*tf.log(logits)) |
| # TensorBoardで表示するよう指定 |
| tf.scalar_summary("cross_entropy", cross_entropy) |
| return cross_entropy |
| def training(loss, learning_rate): |
| """ 訓練のOpを定義する関数 |
| 引数: |
| loss: 損失のtensor, loss()の結果 |
| learning_rate: 学習係数 |
| 返り値: |
| train_step: 訓練のOp |
| """ |
| train_step = tf.train.AdamOptimizer(learning_rate).minimize(loss) |
| return train_step |
| def accuracy(logits, labels): |
| """ 正解率(accuracy)を計算する関数 |
| 引数: |
| logits: inference()の結果 |
| labels: ラベルのtensor, int32 - [batch_size, NUM_CLASSES] |
| 返り値: |
| accuracy: 正解率(float) |
| """ |
| correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1)) |
| accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) |
| tf.scalar_summary("accuracy", accuracy) |
| return accuracy |
| if __name__ == '__main__': |
| # ファイルを開く |
| f = open(FLAGS.train, 'r') |
| # データを入れる配列 |
| train_image = [] |
| train_label = [] |
| for line in f: |
| # 改行を除いてスペース区切りにする |
| line = line.rstrip() |
| l = line.split() |
| # データを読み込んで28x28に縮小 |
| img = cv2.imread(l[0]) |
| img = cv2.resize(img, (28, 28)) |
| # 一列にした後、0-1のfloat値にする |
| train_image.append(img.flatten().astype(np.float32)/255.0) |
| # ラベルを1-of-k方式で用意する |
| tmp = np.zeros(NUM_CLASSES) |
| tmp[int(l[1])] = 1 |
| train_label.append(tmp) |
| # numpy形式に変換 |
| train_image = np.asarray(train_image) |
| train_label = np.asarray(train_label) |
| f.close() |
| f = open(FLAGS.test, 'r') |
| test_image = [] |
| test_label = [] |
| for line in f: |
| line = line.rstrip() |
| l = line.split() |
| img = cv2.imread(l[0]) |
| img = cv2.resize(img, (28, 28)) |
| test_image.append(img.flatten().astype(np.float32)/255.0) |
| tmp = np.zeros(NUM_CLASSES) |
| tmp[int(l[1])] = 1 |
| test_label.append(tmp) |
| test_image = np.asarray(test_image) |
| test_label = np.asarray(test_label) |
| f.close() |
| with tf.Graph().as_default(): |
| # 画像を入れる仮のTensor |
| images_placeholder = tf.placeholder("float", shape=(None, IMAGE_PIXELS)) |
| # ラベルを入れる仮のTensor |
| labels_placeholder = tf.placeholder("float", shape=(None, NUM_CLASSES)) |
| # dropout率を入れる仮のTensor |
| keep_prob = tf.placeholder("float") |
| # inference()を呼び出してモデルを作る |
| logits = inference(images_placeholder, keep_prob) |
| # loss()を呼び出して損失を計算 |
| loss_value = loss(logits, labels_placeholder) |
| # training()を呼び出して訓練 |
| train_op = training(loss_value, FLAGS.learning_rate) |
| # 精度の計算 |
| acc = accuracy(logits, labels_placeholder) |
| # 保存の準備 |
| saver = tf.train.Saver() |
| # Sessionの作成 |
| sess = tf.Session() |
| # 変数の初期化 |
| sess.run(tf.initialize_all_variables()) |
| # TensorBoardで表示する値の設定 |
| summary_op = tf.merge_all_summaries() |
| summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, sess.graph_def) |
| # 訓練の実行 |
| for step in range(FLAGS.max_steps): |
| for i in range(len(train_image)/FLAGS.batch_size): |
| # batch_size分の画像に対して訓練の実行 |
| batch = FLAGS.batch_size*i |
| # feed_dictでplaceholderに入れるデータを指定する |
| sess.run(train_op, feed_dict={ |
| images_placeholder: train_image[batch:batch+FLAGS.batch_size], |
| labels_placeholder: train_label[batch:batch+FLAGS.batch_size], |
| keep_prob: 0.5}) |
| # 1 step終わるたびに精度を計算する |
| train_accuracy = sess.run(acc, feed_dict={ |
| images_placeholder: train_image, |
| labels_placeholder: train_label, |
| keep_prob: 1.0}) |
| print "step %d, training accuracy %g"%(step, train_accuracy) |
| # 1 step終わるたびにTensorBoardに表示する値を追加する |
| summary_str = sess.run(summary_op, feed_dict={ |
| images_placeholder: train_image, |
| labels_placeholder: train_label, |
| keep_prob: 1.0}) |
| summary_writer.add_summary(summary_str, step) |
| # 訓練が終了したらテストデータに対する精度を表示 |
| print "test accuracy %g"%sess.run(acc, feed_dict={ |
| images_placeholder: test_image, |
| labels_placeholder: test_label, |
| keep_prob: 1.0}) |
| # 最終的なモデルを保存 |
| save_path = saver.save(sess, "model.ckpt") |
| ``` |
