用于 for loop， just like range() function
'''
if len(samples) != len(labels):
raise Exception('Length of samples and labels must equal')
stepStart = 0 # initial step
i = 0
while stepStart < len(samples):
stepEnd = stepStart + chunkSize
if stepEnd < len(samples):
yield i, samples[stepStart:stepEnd], labels[stepStart:stepEnd]
i += 1
stepStart = stepEnd
net = Network(train_batch_size=128,
test_batch_size=100,
pooling_scale=2,
dropout_rate=0.90,
base_learning_rate=0.008,
decay_rate=0.99)
net.define_inputs(
train_samples_shape=(128, 48, 48, 1),
train_labels_shape=(128, 7),
test_samples_shape=(100, 48, 48, 1),
)
#
net.add_conv(patch_size=3,
in_depth=1,
out_depth=32,
activation='relu',
pooling=True,
name='conv1')
net.add_conv(patch_size=3,
Iterator/Generator: get a batch of data
这个函数是一个迭代器/生成器，用于每一次只得到 chunkSize 这么多的数据
用于 for loop， just like range() function
'''
if len(samples) != len(labels):
raise Exception('Length of samples and labels must equal')
stepStart = 0 # initial step
i = 0
while stepStart < len(samples):
stepEnd = stepStart + chunkSize
if stepEnd < len(samples):
yield i, samples[stepStart:stepEnd], labels[stepStart:stepEnd]
i += 1
stepStart = stepEnd
net = Network(train_batch_size=64, test_batch_size=500, pooling_scale=2)
net.define_inputs(train_samples_shape=(64, image_size, image_size,
num_channels),
train_labels_shape=(64, num_labels),
test_samples_shape=(500, image_size, image_size,
num_channels))
#
net.add_conv(patch_size=3,
in_depth=num_channels,
out_depth=16,
activation='relu',
pooling=False,
name='conv1')
net.add_conv(patch_size=3,
in_depth=16,
out_depth=16,
用于 for loop， just like range() function
"""
if len(samples) != len(labels):
raise Exception('Length of samples and labels must equal')
stepStart = 0 # initial step
i = 0
while stepStart < len(samples):
stepEnd = stepStart + chunkSize
if stepEnd < len(samples):
yield i, samples[stepStart:stepEnd], labels[stepStart:stepEnd]
i += 1
stepStart = stepEnd
net = Network(
train_batch_size=64, test_batch_size=500, pooling_scale=2,
dropout_rate=0.9,
base_learning_rate=0.001, decay_rate=0.99)
net.define_inputs(
train_samples_shape=(64, image_size, image_size, num_channels),
train_labels_shape=(64, num_labels),
test_samples_shape=(500, image_size, image_size, num_channels),
)
#
net.add_conv(patch_size=3, in_depth=num_channels, out_depth=32, activation='relu', pooling=False, name='conv1')
net.add_conv(patch_size=3, in_depth=32, out_depth=32, activation='relu', pooling=True, name='conv2')
net.add_conv(patch_size=3, in_depth=32, out_depth=32, activation='relu', pooling=False, name='conv3')
net.add_conv(patch_size=3, in_depth=32, out_depth=32, activation='relu', pooling=True, name='conv4')
# 4 = 两次 pooling, 每一次缩小为 1/2
# 32 = conv4 out_depth
net.add_fc(in_num_nodes=(image_size // 4) * (image_size // 4) * 32, out_num_nodes=128, activation='relu',
用于 for loop， just like range() function
"""
if len(samples) != len(labels):
raise Exception('Length of samples and labels must equal')
stepStart = 0 # initial step
i = 0
while stepStart < len(samples):
stepEnd = stepStart + chunkSize
if stepEnd < len(samples):
yield i, samples[stepStart:stepEnd], labels[stepStart:stepEnd]
i += 1
stepStart = stepEnd
net = Network(train_batch_size=64,
test_batch_size=500,
pooling_scale=2,
dropout_rate=0.9,
base_learning_rate=0.001,
decay_rate=0.99)
net.define_inputs(
train_samples_shape=(64, image_size, image_size, num_channels),
train_labels_shape=(64, num_labels),
test_samples_shape=(500, image_size, image_size, num_channels),
)
#
net.add_conv(patch_size=3,
in_depth=num_channels,
out_depth=32,
activation='relu',
pooling=False,
name='conv1')
net.add_conv(patch_size=3,
这个函数是一个迭代器/生成器，用于每一次只得到 chunk_size 这么多的数据
用于 for loop， just like range() function
"""
if len(samples) != len(labels):
raise Exception('Length of samples and labels must equal')
step_start = 0 # initial step
i = 0
while step_start < len(samples):
step_end = step_start + chunk_size
if step_end < len(samples):
yield i, samples[step_start:step_end], labels[step_start:step_end]
i += 1
step_start = step_end
print('num:', image_size, num_channels, num_channels)
net = Network(train_batch_size=64, test_batch_size=500, pooling_scale=2)
net.define_inputs(
train_samples_shape=(64, image_size, image_size, num_channels),
train_labels_shape=(64, num_labels),
test_samples_shape=(500, image_size, image_size, num_channels)
)
#
net.add_conv(patch_size=3, in_depth=num_channels, out_depth=16, activation='relu', pooling=False, name='conv1')
net.add_conv(patch_size=3, in_depth=16, out_depth=16, activation='relu', pooling=True, name='conv2')
net.add_conv(patch_size=3, in_depth=16, out_depth=16, activation='relu', pooling=False, name='conv3')
net.add_conv(patch_size=3, in_depth=16, out_depth=16, activation='relu', pooling=True, name='conv4')
# 4 = 两次 pooling, 每一次缩小为 1/2
# 16 = conv4 out_depth
net.add_fc(in_num_nodes=(image_size // 4) * (image_size // 4) * 16, out_num_nodes=16, activation='relu', name='fc1')
net.add_fc(in_num_nodes=16, out_num_nodes=10, activation=None, name='fc2')