Tensorflow學習筆記：CNN篇（6）——CIFAR-10數據集VGG19實現
前序
— 這是一個基於Tensorflow的VGG19模型在CIFAR-10數據集上的實現，包括圖像預處理，VGG19模型搭建和最終訓練。
VGG19模型
— VGG網絡與AlexNet類似，也是一種CNN，VGG在2014年的 ILSVRC localization and classification 兩個問題上分別取得了第一名和第二名。VGG網絡非常深，通常有16－19層，卷積核大小爲 3 x 3，16和19層的區別主要在於後面三個卷積部分卷積層的數量。可以看到VGG的前幾層爲卷積和maxpool的交替，後面緊跟三個全連接層，激活函數採用Relu，訓練採用了dropout。VGG中各模型配置如下, 其中VGG19的top-1的訓練精度可達到71.1%，top-5的訓練精度可達到89.8%。模型結構示例如下：
代碼示例
1、參數設置
# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
import time
import os
import sys
import pickle
import random
class_num = 10
image_size = 32
img_channels = 3
iterations = 200
batch_size = 250
total_epoch = 164
weight_decay = 0.0003
dropout_rate = 0.5
momentum_rate = 0.9
log_save_path = './vgg_logs'
model_save_path = './model/'2、數據準備
這一部分主要爲數據準備部分由prepare_data()以及四個子函數download_data()、unpickle()、load_data_one()、load_data()所組成，功能包括下載數據集，讀取數據集，將數據分爲訓練集與測試集兩部分並進行Shuffle。
def download_data():
dirname = 'cifar10-dataset'
origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
fname = './CAFIR-10_data/cifar-10-python.tar.gz'
fpath = './' + dirname
download = False
if os.path.exists(fpath) or os.path.isfile(fname):
download = False
print("DataSet already exist!")
else:
download = True
if download:
print('Downloading data from', origin)
import urllib.request
import tarfile
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
return
duration = time.time() - start_time
progress_size = int(count * block_size)
speed = int(progress_size / (1024 * duration))
percent = min(int(count*block_size*100/total_size),100)
sys.stdout.write("\r...%d%%, %d MB, %d KB/s, %d seconds passed" %
(percent, progress_size / (1024 * 1024), speed, duration))
sys.stdout.flush()
urllib.request.urlretrieve(origin, fname, reporthook)
print('Download finished. Start extract!', origin)
if fname.endswith("tar.gz"):
tar = tarfile.open(fname, "r:gz")
tar.extractall()
tar.close()
elif fname.endswith("tar"):
tar = tarfile.open(fname, "r:")
tar.extractall()
tar.close()
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
def load_data_one(file):
batch = unpickle(file)
data = batch[b'data']
labels = batch[b'labels']
print("Loading %s : %d." % (file, len(data)))
return data, labels
def load_data(files, data_dir, label_count):
global image_size, img_channels
data, labels = load_data_one(data_dir + '/' + files[0])
for f in files[1:]:
data_n, labels_n = load_data_one(data_dir + '/' + f)
data = np.append(data, data_n, axis=0)
labels = np.append(labels, labels_n, axis=0)
labels = np.array([[float(i == label) for i in range(label_count)] for label in labels])
data = data.reshape([-1, img_channels, image_size, image_size])
data = data.transpose([0, 2, 3, 1])
return data, labels
def prepare_data():
print("======Loading data======")
download_data()
data_dir = './cifar10-dataset'
image_dim = image_size * image_size * img_channels
meta = unpickle(data_dir + '/batches.meta')
label_names = meta[b'label_names']
label_count = len(label_names)
train_files = ['data_batch_%d' % d for d in range(1, 6)]
train_data, train_labels = load_data(train_files, data_dir, label_count)
test_data, test_labels = load_data(['test_batch'], data_dir, label_count)
print("Train data:", np.shape(train_data), np.shape(train_labels))
print("Test data :", np.shape(test_data), np.shape(test_labels))
print("======Load finished======")
print("======Shuffling data======")
indices = np.random.permutation(len(train_data))
train_data = train_data[indices]
train_labels = train_labels[indices]
print("======Prepare Finished======")
return train_data, train_labels, test_data, test_labels3、數據預處理（圖像增強）
— 任何神經網絡在開始訓練數據時，都需要數據增強。什麼是數據增強呢？我們首先看一個例子，假如我們的訓練集有10萬圖片，如果直接使用這10張圖片進行訓練，是不是感覺訓練集有點小。在假如，訓練神經網絡的目的是要正確識別貓，而此時呢？你的訓練集中含有貓頭的圖片都是朝着左側傾斜，那麼當你訓練好模型之後，你的模型不能準確識別貓頭朝着右側傾斜的圖片，因爲它沒有被訓練。而假如你在訓練輸入時，將訓練集合中的所有圖片進行水平翻轉，就會得到10萬新的數據，此時你就有20萬張訓練集合了。如果在進行隨機裁剪、亮度、對比度變化，這又大大增加訓練集合的數量，最終訓練的集合更加健壯。這就是數據增強的作用：將單幅圖片增加多個副本，提高了圖片的利用率，並且防止對某一張圖片結構的學習過擬合，可以大大增加訓練集合，提供模型的健壯性。
— Tensorflow關於圖像操作的類別有：編碼/解碼、縮放、裁剪、翻轉和移位、圖像調整。
def _random_crop(batch, crop_shape, padding=None):
oshape = np.shape(batch[0])
if padding:
oshape = (oshape[0] + 2*padding, oshape[1] + 2*padding)
new_batch = []
npad = ((padding, padding), (padding, padding), (0, 0))
for i in range(len(batch)):
new_batch.append(batch[i])
if padding:
new_batch[i] = np.lib.pad(batch[i], pad_width=npad,
mode='constant', constant_values=0)
nh = random.randint(0, oshape[0] - crop_shape[0])
nw = random.randint(0, oshape[1] - crop_shape[1])
new_batch[i] = new_batch[i][nh:nh + crop_shape[0],
nw:nw + crop_shape[1]]
return new_batch
def _random_flip_leftright(batch):
for i in range(len(batch)):
if bool(random.getrandbits(1)):
batch[i] = np.fliplr(batch[i])
return batch
def data_preprocessing(x_train,x_test):
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train[:, :, :, 0] = (x_train[:, :, :, 0] - np.mean(x_train[:, :, :, 0])) / np.std(x_train[:, :, :, 0])
x_train[:, :, :, 1] = (x_train[:, :, :, 1] - np.mean(x_train[:, :, :, 1])) / np.std(x_train[:, :, :, 1])
x_train[:, :, :, 2] = (x_train[:, :, :, 2] - np.mean(x_train[:, :, :, 2])) / np.std(x_train[:, :, :, 2])
x_test[:, :, :, 0] = (x_test[:, :, :, 0] - np.mean(x_test[:, :, :, 0])) / np.std(x_test[:, :, :, 0])
x_test[:, :, :, 1] = (x_test[:, :, :, 1] - np.mean(x_test[:, :, :, 1])) / np.std(x_test[:, :, :, 1])
x_test[:, :, :, 2] = (x_test[:, :, :, 2] - np.mean(x_test[:, :, :, 2])) / np.std(x_test[:, :, :, 2])
return x_train, x_test
def data_augmentation(batch):
batch = _random_flip_leftright(batch)
batch = _random_crop(batch, [32, 32], 4)
return batch4、卷積層、池化層模塊化
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape, dtype=tf.float32)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool(input, k_size=1, stride=1, name=None):
return tf.nn.max_pool(input, ksize=[1, k_size, k_size, 1], strides=[1, stride, stride, 1],
padding='SAME', name=name)
def batch_norm(input):
return tf.contrib.layers.batch_norm(input, decay=0.9, center=True, scale=True, epsilon=1e-3,
is_training=train_flag, updates_collections=None)5、指定迭代次數，並在session執行graph
def run_testing(sess, ep):
acc = 0.0
loss = 0.0
pre_index = 0
add = 1000
for it in range(10):
batch_x = test_x[pre_index:pre_index+add]
batch_y = test_y[pre_index:pre_index+add]
pre_index = pre_index + add
loss_, acc_ = sess.run([cross_entropy, accuracy],
feed_dict={x: batch_x, y_: batch_y, keep_prob: 1.0, train_flag: False})
loss += loss_ / 10.0
acc += acc_ / 10.0
summary = tf.Summary(value=[tf.Summary.Value(tag="test_loss", simple_value=loss),
tf.Summary.Value(tag="test_accuracy", simple_value=acc)])
return acc, loss, summary6、主函數（模型搭建）
if __name__ == '__main__':
train_x, train_y, test_x, test_y = prepare_data()
train_x, test_x = data_preprocessing(train_x, test_x)
# define placeholder x, y_ , keep_prob, learning_rate
x = tf.placeholder(tf.float32,[None, image_size, image_size, 3])
y_ = tf.placeholder(tf.float32, [None, class_num])
keep_prob = tf.placeholder(tf.float32)
learning_rate = tf.placeholder(tf.float32)
train_flag = tf.placeholder(tf.bool)
# build_network
W_conv1_1 = tf.get_variable('conv1_1', shape=[3, 3, 3, 64], initializer=tf.contrib.keras.initializers.he_normal())
b_conv1_1 = bias_variable([64])
output = tf.nn.relu(batch_norm(conv2d(x, W_conv1_1) + b_conv1_1))
W_conv1_2 = tf.get_variable('conv1_2', shape=[3, 3, 64, 64], initializer=tf.contrib.keras.initializers.he_normal())
b_conv1_2 = bias_variable([64])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv1_2) + b_conv1_2))
output = max_pool(output, 2, 2, "pool1")
W_conv2_1 = tf.get_variable('conv2_1', shape=[3, 3, 64, 128], initializer=tf.contrib.keras.initializers.he_normal())
b_conv2_1 = bias_variable([128])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv2_1) + b_conv2_1))
W_conv2_2 = tf.get_variable('conv2_2', shape=[3, 3, 128, 128], initializer=tf.contrib.keras.initializers.he_normal())
b_conv2_2 = bias_variable([128])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv2_2) + b_conv2_2))
output = max_pool(output, 2, 2, "pool2")
W_conv3_1 = tf.get_variable('conv3_1', shape=[3, 3, 128, 256], initializer=tf.contrib.keras.initializers.he_normal())
b_conv3_1 = bias_variable([256])
output = tf.nn.relu( batch_norm(conv2d(output,W_conv3_1) + b_conv3_1))
W_conv3_2 = tf.get_variable('conv3_2', shape=[3, 3, 256, 256], initializer=tf.contrib.keras.initializers.he_normal())
b_conv3_2 = bias_variable([256])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv3_2) + b_conv3_2))
W_conv3_3 = tf.get_variable('conv3_3', shape=[3, 3, 256, 256], initializer=tf.contrib.keras.initializers.he_normal())
b_conv3_3 = bias_variable([256])
output = tf.nn.relu( batch_norm(conv2d(output, W_conv3_3) + b_conv3_3))
W_conv3_4 = tf.get_variable('conv3_4', shape=[3, 3, 256, 256], initializer=tf.contrib.keras.initializers.he_normal())
b_conv3_4 = bias_variable([256])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv3_4) + b_conv3_4))
output = max_pool(output, 2, 2, "pool3")
W_conv4_1 = tf.get_variable('conv4_1', shape=[3, 3, 256, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv4_1 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv4_1) + b_conv4_1))
W_conv4_2 = tf.get_variable('conv4_2', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv4_2 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv4_2) + b_conv4_2))
W_conv4_3 = tf.get_variable('conv4_3', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv4_3 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv4_3) + b_conv4_3))
W_conv4_4 = tf.get_variable('conv4_4', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv4_4 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv4_4)) + b_conv4_4)
output = max_pool(output, 2, 2)
W_conv5_1 = tf.get_variable('conv5_1', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv5_1 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv5_1) + b_conv5_1))
W_conv5_2 = tf.get_variable('conv5_2', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv5_2 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv5_2) + b_conv5_2))
W_conv5_3 = tf.get_variable('conv5_3', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv5_3 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv5_3) + b_conv5_3))
W_conv5_4 = tf.get_variable('conv5_4', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv5_4 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv5_4) + b_conv5_4))
# output = tf.contrib.layers.flatten(output)
output = tf.reshape(output, [-1, 2*2*512])
W_fc1 = tf.get_variable('fc1', shape=[2048, 4096], initializer=tf.contrib.keras.initializers.he_normal())
b_fc1 = bias_variable([4096])
output = tf.nn.relu(batch_norm(tf.matmul(output, W_fc1) + b_fc1) )
output = tf.nn.dropout(output, keep_prob)
W_fc2 = tf.get_variable('fc7', shape=[4096, 4096], initializer=tf.contrib.keras.initializers.he_normal())
b_fc2 = bias_variable([4096])
output = tf.nn.relu(batch_norm(tf.matmul(output, W_fc2) + b_fc2))
output = tf.nn.dropout(output, keep_prob)
W_fc3 = tf.get_variable('fc3', shape=[4096, 10], initializer=tf.contrib.keras.initializers.he_normal())
b_fc3 = bias_variable([10])
output = tf.nn.relu(batch_norm(tf.matmul(output, W_fc3) + b_fc3))
# output = tf.reshape(output,[-1,10])
# loss function: cross_entropy
# train_step: training operation
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=output))
l2 = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
train_step = tf.train.MomentumOptimizer(learning_rate, momentum_rate, use_nesterov=True).\
minimize(cross_entropy + l2 * weight_decay)
correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# initial an saver to save model
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(log_save_path,sess.graph)
# epoch = 164
# make sure [bath_size * iteration = data_set_number]
for ep in range(1, total_epoch+1):
lr = learning_rate_schedule(ep)
pre_index = 0
train_acc = 0.0
train_loss = 0.0
start_time = time.time()
print("\n epoch %d/%d:" % (ep, total_epoch))
for it in range(1, iterations+1):
batch_x = train_x[pre_index:pre_index+batch_size]
batch_y = train_y[pre_index:pre_index+batch_size]
batch_x = data_augmentation(batch_x)
_, batch_loss = sess.run([train_step, cross_entropy],
feed_dict={x: batch_x, y_: batch_y, keep_prob: dropout_rate,
learning_rate: lr, train_flag: True})
batch_acc = accuracy.eval(feed_dict={x: batch_x, y_: batch_y, keep_prob: 1.0, train_flag: True})
train_loss += batch_loss
train_acc += batch_acc
pre_index += batch_size
if it == iterations:
train_loss /= iterations
train_acc /= iterations
loss_, acc_ = sess.run([cross_entropy, accuracy],
feed_dict={x: batch_x, y_: batch_y, keep_prob: 1.0, train_flag: True})
train_summary = tf.Summary(value=[tf.Summary.Value(tag="train_loss", simple_value=train_loss),
tf.Summary.Value(tag="train_accuracy", simple_value=train_acc)])
val_acc, val_loss, test_summary = run_testing(sess, ep)
summary_writer.add_summary(train_summary, ep)
summary_writer.add_summary(test_summary, ep)
summary_writer.flush()
print("iteration: %d/%d, cost_time: %ds, train_loss: %.4f, "
"train_acc: %.4f, test_loss: %.4f, test_acc: %.4f"
% (it, iterations, int(time.time()-start_time), train_loss, train_acc, val_loss, val_acc))
else:
print("iteration: %d/%d, train_loss: %.4f, train_acc: %.4f"
% (it, iterations, train_loss / it, train_acc / it), end='\r')
save_path = saver.save(sess, model_save_path)
print("Model saved in file: %s" % save_path) 完整代碼
# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
import time
import os
import sys
import pickle
import random
class_num = 10
image_size = 32
img_channels = 3
iterations = 200
batch_size = 250
total_epoch = 164
weight_decay = 0.0003
dropout_rate = 0.5
momentum_rate = 0.9
log_save_path = './vgg_logs'
model_save_path = './model/'
def download_data():
dirname = 'cifar10-dataset'
origin = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
fname = './CAFIR-10_data/cifar-10-python.tar.gz'
fpath = './' + dirname
download = False
if os.path.exists(fpath) or os.path.isfile(fname):
download = False
print("DataSet already exist!")
else:
download = True
if download:
print('Downloading data from', origin)
import urllib.request
import tarfile
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
return
duration = time.time() - start_time
progress_size = int(count * block_size)
speed = int(progress_size / (1024 * duration))
percent = min(int(count*block_size*100/total_size),100)
sys.stdout.write("\r...%d%%, %d MB, %d KB/s, %d seconds passed" %
(percent, progress_size / (1024 * 1024), speed, duration))
sys.stdout.flush()
urllib.request.urlretrieve(origin, fname, reporthook)
print('Download finished. Start extract!', origin)
if fname.endswith("tar.gz"):
tar = tarfile.open(fname, "r:gz")
tar.extractall()
tar.close()
elif fname.endswith("tar"):
tar = tarfile.open(fname, "r:")
tar.extractall()
tar.close()
def unpickle(file):
with open(file, 'rb') as fo:
dict = pickle.load(fo, encoding='bytes')
return dict
def load_data_one(file):
batch = unpickle(file)
data = batch[b'data']
labels = batch[b'labels']
print("Loading %s : %d." % (file, len(data)))
return data, labels
def load_data(files, data_dir, label_count):
global image_size, img_channels
data, labels = load_data_one(data_dir + '/' + files[0])
for f in files[1:]:
data_n, labels_n = load_data_one(data_dir + '/' + f)
data = np.append(data, data_n, axis=0)
labels = np.append(labels, labels_n, axis=0)
labels = np.array([[float(i == label) for i in range(label_count)] for label in labels])
data = data.reshape([-1, img_channels, image_size, image_size])
data = data.transpose([0, 2, 3, 1])
return data, labels
def prepare_data():
print("======Loading data======")
download_data()
data_dir = './cifar10-dataset'
image_dim = image_size * image_size * img_channels
meta = unpickle(data_dir + '/batches.meta')
label_names = meta[b'label_names']
label_count = len(label_names)
train_files = ['data_batch_%d' % d for d in range(1, 6)]
train_data, train_labels = load_data(train_files, data_dir, label_count)
test_data, test_labels = load_data(['test_batch'], data_dir, label_count)
print("Train data:", np.shape(train_data), np.shape(train_labels))
print("Test data :", np.shape(test_data), np.shape(test_labels))
print("======Load finished======")
print("======Shuffling data======")
indices = np.random.permutation(len(train_data))
train_data = train_data[indices]
train_labels = train_labels[indices]
print("======Prepare Finished======")
return train_data, train_labels, test_data, test_labels
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape, dtype=tf.float32)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool(input, k_size=1, stride=1, name=None):
return tf.nn.max_pool(input, ksize=[1, k_size, k_size, 1], strides=[1, stride, stride, 1],
padding='SAME', name=name)
def batch_norm(input):
return tf.contrib.layers.batch_norm(input, decay=0.9, center=True, scale=True, epsilon=1e-3,
is_training=train_flag, updates_collections=None)
def _random_crop(batch, crop_shape, padding=None):
oshape = np.shape(batch[0])
if padding:
oshape = (oshape[0] + 2*padding, oshape[1] + 2*padding)
new_batch = []
npad = ((padding, padding), (padding, padding), (0, 0))
for i in range(len(batch)):
new_batch.append(batch[i])
if padding:
new_batch[i] = np.lib.pad(batch[i], pad_width=npad,
mode='constant', constant_values=0)
nh = random.randint(0, oshape[0] - crop_shape[0])
nw = random.randint(0, oshape[1] - crop_shape[1])
new_batch[i] = new_batch[i][nh:nh + crop_shape[0],
nw:nw + crop_shape[1]]
return new_batch
def _random_flip_leftright(batch):
for i in range(len(batch)):
if bool(random.getrandbits(1)):
batch[i] = np.fliplr(batch[i])
return batch
def data_preprocessing(x_train,x_test):
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train[:, :, :, 0] = (x_train[:, :, :, 0] - np.mean(x_train[:, :, :, 0])) / np.std(x_train[:, :, :, 0])
x_train[:, :, :, 1] = (x_train[:, :, :, 1] - np.mean(x_train[:, :, :, 1])) / np.std(x_train[:, :, :, 1])
x_train[:, :, :, 2] = (x_train[:, :, :, 2] - np.mean(x_train[:, :, :, 2])) / np.std(x_train[:, :, :, 2])
x_test[:, :, :, 0] = (x_test[:, :, :, 0] - np.mean(x_test[:, :, :, 0])) / np.std(x_test[:, :, :, 0])
x_test[:, :, :, 1] = (x_test[:, :, :, 1] - np.mean(x_test[:, :, :, 1])) / np.std(x_test[:, :, :, 1])
x_test[:, :, :, 2] = (x_test[:, :, :, 2] - np.mean(x_test[:, :, :, 2])) / np.std(x_test[:, :, :, 2])
return x_train, x_test
def data_augmentation(batch):
batch = _random_flip_leftright(batch)
batch = _random_crop(batch, [32, 32], 4)
return batch
def learning_rate_schedule(epoch_num):
if epoch_num < 81:
return 0.1
elif epoch_num < 121:
return 0.01
else:
return 0.001
def run_testing(sess, ep):
acc = 0.0
loss = 0.0
pre_index = 0
add = 1000
for it in range(10):
batch_x = test_x[pre_index:pre_index+add]
batch_y = test_y[pre_index:pre_index+add]
pre_index = pre_index + add
loss_, acc_ = sess.run([cross_entropy, accuracy],
feed_dict={x: batch_x, y_: batch_y, keep_prob: 1.0, train_flag: False})
loss += loss_ / 10.0
acc += acc_ / 10.0
summary = tf.Summary(value=[tf.Summary.Value(tag="test_loss", simple_value=loss),
tf.Summary.Value(tag="test_accuracy", simple_value=acc)])
return acc, loss, summary
if __name__ == '__main__':
train_x, train_y, test_x, test_y = prepare_data()
train_x, test_x = data_preprocessing(train_x, test_x)
# define placeholder x, y_ , keep_prob, learning_rate
x = tf.placeholder(tf.float32,[None, image_size, image_size, 3])
y_ = tf.placeholder(tf.float32, [None, class_num])
keep_prob = tf.placeholder(tf.float32)
learning_rate = tf.placeholder(tf.float32)
train_flag = tf.placeholder(tf.bool)
# build_network
W_conv1_1 = tf.get_variable('conv1_1', shape=[3, 3, 3, 64], initializer=tf.contrib.keras.initializers.he_normal())
b_conv1_1 = bias_variable([64])
output = tf.nn.relu(batch_norm(conv2d(x, W_conv1_1) + b_conv1_1))
W_conv1_2 = tf.get_variable('conv1_2', shape=[3, 3, 64, 64], initializer=tf.contrib.keras.initializers.he_normal())
b_conv1_2 = bias_variable([64])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv1_2) + b_conv1_2))
output = max_pool(output, 2, 2, "pool1")
W_conv2_1 = tf.get_variable('conv2_1', shape=[3, 3, 64, 128], initializer=tf.contrib.keras.initializers.he_normal())
b_conv2_1 = bias_variable([128])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv2_1) + b_conv2_1))
W_conv2_2 = tf.get_variable('conv2_2', shape=[3, 3, 128, 128], initializer=tf.contrib.keras.initializers.he_normal())
b_conv2_2 = bias_variable([128])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv2_2) + b_conv2_2))
output = max_pool(output, 2, 2, "pool2")
W_conv3_1 = tf.get_variable('conv3_1', shape=[3, 3, 128, 256], initializer=tf.contrib.keras.initializers.he_normal())
b_conv3_1 = bias_variable([256])
output = tf.nn.relu( batch_norm(conv2d(output,W_conv3_1) + b_conv3_1))
W_conv3_2 = tf.get_variable('conv3_2', shape=[3, 3, 256, 256], initializer=tf.contrib.keras.initializers.he_normal())
b_conv3_2 = bias_variable([256])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv3_2) + b_conv3_2))
W_conv3_3 = tf.get_variable('conv3_3', shape=[3, 3, 256, 256], initializer=tf.contrib.keras.initializers.he_normal())
b_conv3_3 = bias_variable([256])
output = tf.nn.relu( batch_norm(conv2d(output, W_conv3_3) + b_conv3_3))
W_conv3_4 = tf.get_variable('conv3_4', shape=[3, 3, 256, 256], initializer=tf.contrib.keras.initializers.he_normal())
b_conv3_4 = bias_variable([256])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv3_4) + b_conv3_4))
output = max_pool(output, 2, 2, "pool3")
W_conv4_1 = tf.get_variable('conv4_1', shape=[3, 3, 256, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv4_1 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv4_1) + b_conv4_1))
W_conv4_2 = tf.get_variable('conv4_2', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv4_2 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv4_2) + b_conv4_2))
W_conv4_3 = tf.get_variable('conv4_3', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv4_3 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv4_3) + b_conv4_3))
W_conv4_4 = tf.get_variable('conv4_4', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv4_4 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv4_4)) + b_conv4_4)
output = max_pool(output, 2, 2)
W_conv5_1 = tf.get_variable('conv5_1', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv5_1 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv5_1) + b_conv5_1))
W_conv5_2 = tf.get_variable('conv5_2', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv5_2 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv5_2) + b_conv5_2))
W_conv5_3 = tf.get_variable('conv5_3', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv5_3 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv5_3) + b_conv5_3))
W_conv5_4 = tf.get_variable('conv5_4', shape=[3, 3, 512, 512], initializer=tf.contrib.keras.initializers.he_normal())
b_conv5_4 = bias_variable([512])
output = tf.nn.relu(batch_norm(conv2d(output, W_conv5_4) + b_conv5_4))
# output = tf.contrib.layers.flatten(output)
output = tf.reshape(output, [-1, 2*2*512])
W_fc1 = tf.get_variable('fc1', shape=[2048, 4096], initializer=tf.contrib.keras.initializers.he_normal())
b_fc1 = bias_variable([4096])
output = tf.nn.relu(batch_norm(tf.matmul(output, W_fc1) + b_fc1) )
output = tf.nn.dropout(output, keep_prob)
W_fc2 = tf.get_variable('fc7', shape=[4096, 4096], initializer=tf.contrib.keras.initializers.he_normal())
b_fc2 = bias_variable([4096])
output = tf.nn.relu(batch_norm(tf.matmul(output, W_fc2) + b_fc2))
output = tf.nn.dropout(output, keep_prob)
W_fc3 = tf.get_variable('fc3', shape=[4096, 10], initializer=tf.contrib.keras.initializers.he_normal())
b_fc3 = bias_variable([10])
output = tf.nn.relu(batch_norm(tf.matmul(output, W_fc3) + b_fc3))
# output = tf.reshape(output,[-1,10])
# loss function: cross_entropy
# train_step: training operation
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=output))
l2 = tf.add_n([tf.nn.l2_loss(var) for var in tf.trainable_variables()])
train_step = tf.train.MomentumOptimizer(learning_rate, momentum_rate, use_nesterov=True).\
minimize(cross_entropy + l2 * weight_decay)
correct_prediction = tf.equal(tf.argmax(output, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# initial an saver to save model
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(log_save_path,sess.graph)
# epoch = 164
# make sure [bath_size * iteration = data_set_number]
for ep in range(1, total_epoch+1):
lr = learning_rate_schedule(ep)
pre_index = 0
train_acc = 0.0
train_loss = 0.0
start_time = time.time()
print("\n epoch %d/%d:" % (ep, total_epoch))
for it in range(1, iterations+1):
batch_x = train_x[pre_index:pre_index+batch_size]
batch_y = train_y[pre_index:pre_index+batch_size]
batch_x = data_augmentation(batch_x)
_, batch_loss = sess.run([train_step, cross_entropy],
feed_dict={x: batch_x, y_: batch_y, keep_prob: dropout_rate,
learning_rate: lr, train_flag: True})
batch_acc = accuracy.eval(feed_dict={x: batch_x, y_: batch_y, keep_prob: 1.0, train_flag: True})
train_loss += batch_loss
train_acc += batch_acc
pre_index += batch_size
if it == iterations:
train_loss /= iterations
train_acc /= iterations
loss_, acc_ = sess.run([cross_entropy, accuracy],
feed_dict={x: batch_x, y_: batch_y, keep_prob: 1.0, train_flag: True})
train_summary = tf.Summary(value=[tf.Summary.Value(tag="train_loss", simple_value=train_loss),
tf.Summary.Value(tag="train_accuracy", simple_value=train_acc)])
val_acc, val_loss, test_summary = run_testing(sess, ep)
summary_writer.add_summary(train_summary, ep)
summary_writer.add_summary(test_summary, ep)
summary_writer.flush()
print("iteration: %d/%d, cost_time: %ds, train_loss: %.4f, "
"train_acc: %.4f, test_loss: %.4f, test_acc: %.4f"
% (it, iterations, int(time.time()-start_time), train_loss, train_acc, val_loss, val_acc))
else:
print("iteration: %d/%d, train_loss: %.4f, train_acc: %.4f"
% (it, iterations, train_loss / it, train_acc / it), end='\r')
save_path = saver.save(sess, model_save_path)
print("Model saved in file: %s" % save_path)