if __name__ == '__main__':

import load

from dp import Network

train_samples, train_labels = load._train_samples, load._train_labels

test_samples, test_labels = load._test_samples, load._test_labels

print('Training set', train_samples.shape, train_labels.shape)

print(' Test set', test_samples.shape, test_labels.shape)

image_size = load.image_size

num_labels = load.num_labels

num_channels = load.num_channels

def train_data_iterator(samples, labels, iteration_steps, chunkSize):

'''

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 i < iteration_steps:

stepStart = (i * chunkSize) % (labels.shape[0] - chunkSize)

yield i, samples[stepStart:stepStart + chunkSize], labels[stepStart:stepStart + chunkSize]

i += 1

def test_data_iterator(samples, labels, chunkSize):

'''

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,

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', name='fc1')

net.add_fc(in_num_nodes=128, out_num_nodes=10, activation=None, name='fc2')

net.define_model()

#net.run(train_samples, train_labels, test_samples, test_labels, train_data_iterator=train_data_iterator, iteration_steps=3000, test_data_iterator=test_data_iterator)

#net.train(train_samples, train_labels, data_iterator=train_data_iterator, iteration_steps=2000)

net.test(test_samples, test_labels, data_iterator=test_data_iterator)

else:

raise Exception('main.py: Should Not Be Imported!!! Must Run by "python main.py"')