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,
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,
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,
"""
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=0.5,
base_learning_rate=0.001,
decay_rate=0.99,
save_path='D:/Python/CNN/Models/v1.1.ckpt') # default
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')