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VGG.py
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import tensorflow as tf
import tools
#%%
def VGG16(x, n_classes, is_pretrain=True):
x = tools.conv('conv1_1', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv1_2', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.pool('pool1', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv2_1', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv2_2', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.pool('pool2', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv3_1', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv3_2', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv3_3', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv4_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv4_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv4_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv5_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv5_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv5_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.FC_layer('fc6', x, out_nodes=4096)
#x = tools.batch_norm(x)
x = tools.FC_layer('fc7', x, out_nodes=4096)
#x = tools.batch_norm(x)
x = tools.FC_layer('fc8', x, out_nodes=n_classes)
return x
#%% TO get better tensorboard figures!
def VGG16N(x, n_classes, is_pretrain=True):
with tf.name_scope('VGG16'):
x = tools.conv('conv1_1', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv1_2', x, 64, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
with tf.name_scope('pool1'):
x = tools.pool('pool1', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv2_1', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv2_2', x, 128, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
with tf.name_scope('pool2'):
x = tools.pool('pool2', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv3_1', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv3_2', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv3_3', x, 256, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
with tf.name_scope('pool3'):
x = tools.pool('pool3', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv4_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv4_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv4_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
with tf.name_scope('pool4'):
x = tools.pool('pool4', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.conv('conv5_1', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv5_2', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
x = tools.conv('conv5_3', x, 512, kernel_size=[3,3], stride=[1,1,1,1], is_pretrain=is_pretrain)
with tf.name_scope('pool5'):
x = tools.pool('pool5', x, kernel=[1,2,2,1], stride=[1,2,2,1], is_max_pool=True)
x = tools.FC_layer('fc6', x, out_nodes=4096)
#with tf.name_scope('batch_norm1'):
#x = tools.batch_norm(x)
x = tools.FC_layer('fc7', x, out_nodes=4096)
#with tf.name_scope('batch_norm2'):
#x = tools.batch_norm(x)
x = tools.FC_layer('fc8', x, out_nodes=n_classes)
return x
#%%