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| def redidual_block(x, output_channel):
"""redidual connection implementation"""
input_channel = x.get_shape().as_list()[-1]
if input_channel * 2 == output_channel:
increase_dim = True
strides = (2,2)
elif input_channel == output_channel:
increase_dim = False
strides = (1,1)
else:
raise Exception("input channel can't match output channel")
conv1 = tf.layers.conv2d(
x,
output_channel,
(3,3),
strides = strides,
padding ='same',
activation = tf.nn.relu,
name = 'conv1'
)
conv2 = tf.layers.conv2d(
conv1,
output_channel,
(3,3),
strides = (1,1),
padding ='same',
activation = tf.nn.relu,
name = 'conv2'
)
if increase_dim:
pooled_x = tf.layers.average_pooling2d(
x,
(2,2),
(2,2),
padding = 'valid'
)
padded_x = tf.pad(
pooled_x,
[
[0,0],
[0,0],
[0,0],
[input_channel // 2,input_channel // 2]
])
else:
padded_x = x
output_x = conv2 + padded_x
return output_x
def res_net(x,num_residual_blocks,num_filter_base,class_num):
'''
x: 输入数据
num_residual_blocks: 残差连接块数 eg:[3, 4, 6, 3]
num_filter_base: 最初通道数
class_num: 泛化不同数据集
'''
num_subsampling = len(num_residual_blocks)
layers = []
with tf.variable_scope('conv0'):
conv0 = tf.layers.conv2d(
x,
num_filter_base,
(3,3),
strides = (1,1),
activation = tf.nn.relu,
padding = 'same',
name = 'conv0'
)
layers.append(conv0)
for sample_id in range(num_subsampling):
for i in range(num_residual_blocks[sample_id]):
with tf.variable_scope('conv%d_%d' % (sample_id, i)):
conv = redidual_block(
layers[-1],
num_filter_base * (2 ** sample_id)
)
layers.append(conv)
with tf.variable_scope('fc'):
global_pool = tf.reduce_mean(layers[-1], [1,2])
logits = tf.layers.dense(global_pool, class_num)
layers.append(logits)
return layers[-1]
|
