tensorflow vgg基于cifar-10进行训练
2018-11-27 本文已影响339人
GOGOYAO
最近接触tf，想在cifar-10数据集上训练下vgg网络。最开始想先跑vgg16，搜了一大圈，没有一个可以直接跑的（我参考 【深度学习系列】用PaddlePaddle和Tensorflow实现经典CNN网络Vgg 跑出来的精度就10%），要么是代码是针对1000种分类的，要么是预训练好的。最后在Tensorflow学习笔记：CNN篇（6）——CIFAR-10数据集VGG19实现 找到了一个vgg19的，可以直接跑，而且加了tensorboard，然后再在这个代码基础上改了一个vgg16的，终于可以跑了。
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vgg19 可训练代码
vgg19的代码可以见Tensorflow学习笔记：CNN篇（6）——CIFAR-10数据集VGG19实现，注意python2跑的话，需要根据错误改一些地方。改好的代码如下：
# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
import time
import os
import sys
import random
import cPickle as pickle
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_19_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)
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))
save_path = saver.save(sess, model_save_path)
print("Model saved in file: %s" % save_path)
vgg16 可训练代码
vgg16的代码如下，python2亲测跑过：
# -*- coding:utf-8 -*-
import tensorflow as tf
import numpy as np
import time
import os
import sys
import random
import cPickle as pickle
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_16_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)
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')
print(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))
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))
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))
#output = max_pool(output, 2, 2)
# 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))
save_path = saver.save(sess, model_save_path)
print("Model saved in file: %s" % save_path)
运行结果
运行环境是2 gpu。
自己改出来的vgg16，需要大约100分钟，可以到93%左右的精度，CNN篇（6）——CIFAR-10数据集VGG19实现的vgg19，需要大约分钟，可以到93%左右的精度。
vgg16的曲线
-
train:
这里写图片描述
-
test:
这里写图片描述
vgg19的曲线
-
train:
这里写图片描述
- test:
这里写图片描述
