def classifier(x, dropout):
pool5 = cnn(x)
dim = pool5.get_shape().as_list()
flat_dim = dim[1] * dim[2] * dim[3]
flat = tf.reshape(pool5, [-1, flat_dim])
with tf.name_scope('classifier') as scope:
with tf.name_scope('classifier_fc1') as inner_scope:
wfc1 = tu.weight([flat_dim, 4096], name='wfc1')
bfc1 = tu.bias(0.0, [4096], name='bfc1')
fc1 = tf.add(tf.matmul(flat, wfc1), bfc1)
fc1 = tu.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
with tf.name_scope('classifier_fc2') as inner_scope:
wfc2 = tu.weight([4096, 4096], name='wfc2')
bfc2 = tu.bias(0.0, [4096], name='bfc2')
fc2 = tf.add(tf.matmul(fc1, wfc2), bfc2)
fc2 = tu.relu(fc2)
fc2 = tf.nn.dropout(fc2, dropout)
with tf.name_scope('classifier_output') as inner_scope:
wfc3 = tu.weight([4096, 1000], name='wfc3')
bfc3 = tu.bias(0.0, [1000], name='bfc3')
fc3 = tf.add(tf.matmul(fc2, wfc3), bfc3)
softmax = tf.nn.softmax(fc3)
return fc3, softmax
def cnn(x):
with tf.name_scope('cnn') as scope:
with tf.name_scope('cnn_conv1') as inner_scope:
wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
bcnn1 = tu.bias(0.0, [96], name='bcnn1')
conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME'),
bcnn1)
conv1 = tu.relu(conv1)
norm1 = tu.lrn(conv1,
depth_radius=2,
bias=1.0,
alpha=2e-05,
beta=0.75)
pool1 = tu.max_pool2d(norm1,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('cnn_conv2') as inner_scope:
wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2')
bcnn2 = tu.bias(1.0, [256], name='bcnn2')
conv2 = tf.add(
tu.conv2d(pool1, wcnn2, stride=(1, 1), padding='SAME'), bcnn2)
conv2 = tu.relu(conv2)
norm2 = tu.lrn(conv2,
depth_radius=2,
bias=1.0,
alpha=2e-05,
beta=0.75)
pool2 = tu.max_pool2d(norm2,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('cnn_conv3') as inner_scope:
wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3')
bcnn3 = tu.bias(0.0, [384], name='bcnn3')
conv3 = tf.add(
tu.conv2d(pool2, wcnn3, stride=(1, 1), padding='SAME'), bcnn3)
conv3 = tu.relu(conv3)
with tf.name_scope('cnn_conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
bcnn4 = tu.bias(1.0, [384], name='bcnn4')
conv4 = tf.add(
tu.conv2d(conv3, wcnn4, stride=(1, 1), padding='SAME'), bcnn4)
conv4 = tu.relu(conv4)
with tf.name_scope('cnn_conv5') as inner_scope:
wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5')
bcnn5 = tu.bias(1.0, [256], name='bcnn5')
conv5 = tf.add(
tu.conv2d(conv4, wcnn5, stride=(1, 1), padding='SAME'), bcnn5)
conv5 = tu.relu(conv5)
pool5 = tu.max_pool2d(conv5,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
return pool5
def cnn(x):
"""
AlexNet convolutional layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
Returns:
pool5: tensor with all convolutions, pooling and lrn operations applied
"""
with tf.name_scope('alexnet_cnn') as scope:
with tf.name_scope('alexnet_cnn_conv1') as inner_scope:
wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
bcnn1 = tu.bias(0.0, [96], name='bcnn1')
conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME'), bcnn1)
# conv1 = tu.batch_norm(conv1)
conv1 = tu.relu(conv1)
norm1 = tu.lrn(conv1, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75)
pool1 = tu.max_pool2d(norm1, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID')
with tf.name_scope('alexnet_cnn_conv2') as inner_scope:
wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2')
bcnn2 = tu.bias(1.0, [256], name='bcnn2')
conv2 = tf.add(tu.conv2d(pool1, wcnn2, stride=(1, 1), padding='SAME'), bcnn2)
# conv2 = tu.batch_norm(conv2)
conv2 = tu.relu(conv2)
norm2 = tu.lrn(conv2, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75)
pool2 = tu.max_pool2d(norm2, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID')
with tf.name_scope('alexnet_cnn_conv3') as inner_scope:
wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3')
bcnn3 = tu.bias(0.0, [384], name='bcnn3')
conv3 = tf.add(tu.conv2d(pool2, wcnn3, stride=(1, 1), padding='SAME'), bcnn3)
# conv3 = tu.batch_norm(conv3)
conv3 = tu.relu(conv3)
with tf.name_scope('alexnet_cnn_conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
bcnn4 = tu.bias(1.0, [384], name='bcnn4')
conv4 = tf.add(tu.conv2d(conv3, wcnn4, stride=(1, 1), padding='SAME'), bcnn4)
# conv4 = tu.batch_norm(conv4)
conv4 = tu.relu(conv4)
with tf.name_scope('alexnet_cnn_conv5') as inner_scope:
wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5')
bcnn5 = tu.bias(1.0, [256], name='bcnn5')
conv5 = tf.add(tu.conv2d(conv4, wcnn5, stride=(1, 1), padding='SAME'), bcnn5)
# conv5 = tu.batch_norm(conv5)
conv5 = tu.relu(conv5)
pool5 = tu.max_pool2d(conv5, kernel=[1, 3, 3, 1], stride=[1, 2, 2, 1], padding='VALID')
return pool5
def classifier(x, dropout):
"""
AlexNet fully connected layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
dropout: probability of non dropping out units
Returns:
fc3: 1000 linear tensor taken just before applying the softmax operation
it is needed to feed it to tf.softmax_cross_entropy_with_logits()
softmax: 1000 linear tensor representing the output probabilities of the image to classify
"""
pool5 = cnn(x)
dim = pool5.get_shape().as_list()
flat_dim = dim[1] * dim[2] * dim[3] # 6 * 6 * 256
flat = tf.reshape(pool5, [-1, flat_dim])
with tf.name_scope('alexnet_classifier') as scope:
with tf.name_scope('alexnet_classifier_fc1') as inner_scope:
wfc1 = tu.weight([flat_dim, 4096], name='wfc1')
wfc_1 = tu.weight([flat_dim, 4096], name='wfc_1')
bfc1 = tu.bias(0.0, [4096], name='bfc1')
alpha_full_1 = compute_alpha(wfc1)
wfc_1 = tenary_opration(wfc1)
flat = tf.multiply(flat, alpha_full_1)
fc1 = tf.add(tf.matmul(flat, wfc_1), bfc1)
fc1 = tu.batch_norm(fc1)
fc1 = selu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
with tf.name_scope('alexnet_classifier_fc2') as inner_scope:
wfc2 = tu.weight([4096, 4096], name='wfc2')
wfc_2 = tu.weight([4096, 4096], name='wfc_2')
bfc2 = tu.bias(0.0, [4096], name='bfc2')
alpha6 = compute_alpha(wfc2)
wfc_2 = tenary_opration(wfc2)
fc1 = tf.multiply(fc1, alpha6)
fc2 = tf.add(tf.matmul(fc1, wfc_2), bfc2)
fc2 = tu.batch_norm(fc2)
fc2 = selu(fc2)
fc2 = tf.nn.dropout(fc2, dropout)
with tf.name_scope('alexnet_classifier_output') as inner_scope:
wfc3 = tu.weight([4096, 1000], name='wfc3')
bfc3 = tu.bias(0.0, [1000], name='bfc3')
# wfc3 = tenary_opration(wfc3)
fc3 = tf.add(tf.matmul(fc2, wfc3), bfc3)
softmax = tf.nn.softmax(fc3)
return fc3, softmax
def classifier(x, dropout):
"""
AlexNet fully connected layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
dropout: probability of non dropping out units
Returns:
fc3: 1000 linear tensor taken just before applying the softmax operation
it is needed to feed it to tf.softmax_cross_entropy_with_logits()
softmax: 1000 linear tensor representing the output probabilities of the image to classify
"""
pool5 = cnn(x)
dim = pool5.get_shape().as_list()
flat_dim = dim[1] * dim[2] * dim[3] # 6 * 6 * 256
flat = tf.reshape(pool5, [-1, flat_dim])
with tf.name_scope('alexnet_classifier') as scope:
with tf.name_scope('alexnet_classifier_fc1') as inner_scope:
wfc1 = tu.weight([flat_dim, 4096], name='wfc1')
bfc1 = tu.bias(0.0, [4096], name='bfc1')
fc1 = tf.add(tf.matmul(flat, wfc1), bfc1)
# fc1 = tu.batch_norm(fc1)
fc1 = tu.relu(fc1)
fc1 = tf.nn.dropout(fc1, dropout)
with tf.name_scope('alexnet_classifier_fc2') as inner_scope:
wfc2 = tu.weight([4096, 4096], name='wfc2')
bfc2 = tu.bias(0.0, [4096], name='bfc2')
fc2 = tf.add(tf.matmul(fc1, wfc2), bfc2)
# fc2 = tu.batch_norm(fc2)
fc2 = tu.relu(fc2)
fc2 = tf.nn.dropout(fc2, dropout)
with tf.name_scope('alexnet_classifier_output') as inner_scope:
wfc3 = tu.weight([4096, 6], name='wfc3')
bfc3 = tu.bias(0.0, [6], name='bfc3')
fc3 = tf.add(tf.matmul(fc2, wfc3), bfc3)
softmax = tf.nn.softmax(fc3)
return fc3, softmax
def cnn(x):
"""
AlexNet convolutional layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
Returns:
pool5: tensor with all convolutions, pooling and lrn operations applied
"""
with tf.name_scope('alexnet_cnn') as scope:
with tf.name_scope('alexnet_cnn_conv1') as inner_scope:
wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
# bcnn1 = tu.bias(0.0, [96], name='bcnn1')
# wcnn1_t = fw(wcnn1)
# x_t =fa(cabs(x))
conv1 = tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME')
#conv1 = tu.batch_norm(conv1)
conv1 = tf.nn.relu(conv1)
norm1 = tu.lrn(conv1,
depth_radius=2,
bias=1.0,
alpha=2e-05,
beta=0.75)
pool1 = tu.max_pool2d(norm1,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('alexnet_cnn_conv2') as inner_scope:
wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2')
# bcnn2 = tu.bias(1.0, [256], name='bcnn2')
pool1_t = fa(cabs(pool1))
wcnn2_t = fw(wcnn2)
conv2 = tu.conv2d(pool1_t, wcnn2_t, stride=(1, 1), padding='SAME')
#conv2 = tu.batch_norm(conv2)
conv2 = tf.nn.relu(conv2)
norm2 = tu.lrn(conv2,
depth_radius=2,
bias=1.0,
alpha=2e-05,
beta=0.75)
pool2 = tu.max_pool2d(norm2,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('alexnet_cnn_conv3') as inner_scope:
wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3')
# bcnn3 = tu.bias(0.0, [384], name='bcnn3')
pool2_t = fa(cabs(pool2))
wcnn3_t = fw(wcnn3)
conv3 = tu.conv2d(pool2_t, wcnn3_t, stride=(1, 1), padding='SAME')
#conv3 = tu.batch_norm(conv3)
conv3 = tf.nn.relu(conv3)
with tf.name_scope('alexnet_cnn_conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
# bcnn4 = tu.bias(1.0, [384], name='bcnn4')
conv3_t = fa(cabs(conv3))
wcnn4_t = fw(wcnn4)
conv4 = tu.conv2d(conv3_t, wcnn4_t, stride=(1, 1), padding='SAME')
#conv4 = tu.batch_norm(conv4)
conv4 = tf.nn.relu(conv4)
with tf.name_scope('alexnet_cnn_conv5') as inner_scope:
wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5')
# bcnn5 = tu.bias(1.0, [256], name='bcnn5')
conv4_t = fa(cabs(conv4))
wcnn5_t = fw(wcnn5)
conv5 = tu.conv2d(conv4_t, wcnn5_t, stride=(1, 1), padding='SAME')
#conv5 = tu.batch_norm(conv5)
conv5 = tf.nn.relu(conv5)
pool5 = tu.max_pool2d(conv5,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
return pool5
def cnn(x):
"""
AlexNet convolutional layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
Returns:
pool5: tensor with all convolutions, pooling and lrn operations applied
"""
with tf.name_scope('alexnet_cnn') as scope:
with tf.name_scope('alexnet_cnn_conv1') as inner_scope:
wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
bcnn1 = tu.bias(0.0, [96], name='bcnn1')
conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME'),
bcnn1)
#conv1 = tu.batch_norm(conv1)
conv1 = tu.relu(conv1)
norm1 = tu.batch_norm(conv1)
pool1 = tu.max_pool2d(norm1,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('alexnet_cnn_conv2') as inner_scope:
wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2')
bcnn2 = tu.bias(1.0, [256], name='bcnn2')
conv2 = tf.add(
tu.conv2d(pool1, wcnn2, stride=(1, 1), padding='SAME'), bcnn2)
#conv2 = tu.batch_norm(conv2)
conv2 = tu.relu(conv2)
norm2 = tu.batch_norm(conv2)
pool2 = tu.max_pool2d(norm2,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('alexnet_cnn_conv3') as inner_scope:
wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3')
bcnn3 = tu.bias(0.0, [384], name='bcnn3')
conv3 = tf.add(
tu.conv2d(pool2, wcnn3, stride=(1, 1), padding='SAME'), bcnn3)
conv3 = tu.batch_norm(conv3)
conv3 = tu.relu(conv3)
with tf.name_scope('alexnet_cnn_conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
bcnn4 = tu.bias(1.0, [384], name='bcnn4')
conv4 = tf.add(
tu.conv2d(conv3, wcnn4, stride=(1, 1), padding='SAME'), bcnn4)
conv4 = tu.batch_norm(conv4)
conv4 = tu.relu(conv4)
with tf.name_scope('alexnet_cnn_conv5') as inner_scope:
wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5')
bcnn5 = tu.bias(1.0, [256], name='bcnn5')
conv5 = tf.add(
tu.conv2d(conv4, wcnn5, stride=(1, 1), padding='SAME'), bcnn5)
conv5 = tu.batch_norm(conv5)
conv5 = tu.relu(conv5)
pool5 = tu.max_pool2d(conv5,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
return pool5
def cnn(x):
"""
AlexNet convolutional layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
Returns:
pool5: tensor with all convolutions, pooling and lrn operations applied
"""
with tf.name_scope('alexnet_cnn') as scope:
with tf.name_scope('alexnet_cnn_conv1') as inner_scope:
wcnn1 = tu.weight([11, 11, 3, 96], name='wcnn1')
bcnn1 = tu.bias(0.0, [96], name='bcnn1')
# alpha1 = compute_alpha(wcnn1)
# wcnn1 = tenary_opration(wcnn1)
# wcnn1_1 = tf.multiply(alpha1, wcnn1)
conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(4, 4), padding='SAME'),
bcnn1)
conv1 = tu.batch_norm(conv1)
conv1 = selu(conv1)
# norm1 = tu.lrn(conv1, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75)
pool1 = tu.max_pool2d(conv1,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('alexnet_cnn_conv2') as inner_scope:
wcnn2 = tu.weight([5, 5, 96, 256], name='wcnn2')
wcnn_2 = tu.weight([5, 5, 96, 256], name='wcnn_2')
bcnn2 = tu.bias(1.0, [256], name='bcnn2')
alpha2 = compute_alpha(wcnn2)
pool1 = tf.multiply(pool1, alpha2)
wcnn_2 = tenary_opration(wcnn2)
# wcnn_2 = tf.multiply(alpha2, wcnn2)
conv2 = tf.add(
tu.conv2d(pool1, wcnn_2, stride=(1, 1), padding='SAME'), bcnn2)
conv2 = tu.batch_norm(conv2)
conv2 = selu(conv2)
# norm2 = tu.lrn(conv2, depth_radius=2, bias=1.0, alpha=2e-05, beta=0.75)
pool2 = tu.max_pool2d(conv2,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
with tf.name_scope('alexnet_cnn_conv3') as inner_scope:
wcnn3 = tu.weight([3, 3, 256, 384], name='wcnn3')
wcnn_3 = tu.weight([3, 3, 256, 384], name='wcnn_3')
bcnn3 = tu.bias(0.0, [384], name='bcnn3')
alpha3 = compute_alpha(wcnn3)
wcnn_3 = tenary_opration(wcnn3)
pool2 = tf.multiply(pool2, alpha3)
conv3 = tf.add(
tu.conv2d(pool2, wcnn_3, stride=(1, 1), padding='SAME'), bcnn3)
conv3 = tu.batch_norm(conv3)
conv3 = selu(conv3)
with tf.name_scope('alexnet_cnn_conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 384, 384], name='wcnn4')
wcnn_4 = tu.weight([3, 3, 383, 384], name='wcnn_4')
bcnn4 = tu.bias(1.0, [384], name='bcnn4')
alpha4 = compute_alpha(wcnn4)
wcnn_4 = tenary_opration(wcnn4)
conv3 = tf.multiply(conv3, alpha4)
conv4 = tf.add(
tu.conv2d(conv3, wcnn_4, stride=(1, 1), padding='SAME'), bcnn4)
conv4 = tu.batch_norm(conv4)
conv4 = selu(conv4)
with tf.name_scope('alexnet_cnn_conv5') as inner_scope:
wcnn5 = tu.weight([3, 3, 384, 256], name='wcnn5')
wcnn_5 = tu.weight([3, 3, 384, 256], name='wcnn_5')
bcnn5 = tu.bias(1.0, [256], name='bcnn5')
alpha5 = compute_alpha(wcnn5)
wcnn_5 = tenary_opration(wcnn5)
conv4 = tf.multiply(conv4, alpha5)
conv5 = tf.add(
tu.conv2d(conv4, wcnn_5, stride=(1, 1), padding='SAME'), bcnn5)
conv5 = tu.batch_norm(conv5)
conv5 = selu(conv5)
pool5 = tu.max_pool2d(conv5,
kernel=[1, 3, 3, 1],
stride=[1, 2, 2, 1],
padding='VALID')
return pool5
def cnn(x):
"""
AlexNet convolutional layers definition
Args:
x: tensor of shape [batch_size, width, height, channels]
Returns:
pool5: tensor with all convolutions, pooling and lrn operations applied
"""
with tf.name_scope('vgg_cnn') as scope:
with tf.name_scope('vgg_cnn_conv1') as inner_scope:
wcnn1 = tu.weight([3, 3, 3, 64], name='wcnn1')
bcnn1 = tu.bias(0.0, [64], name='bcnn1')
conv1 = tf.add(tu.conv2d(x, wcnn1, stride=(1,1), padding='SAME'), bcnn1)
conv1 = tu.relu(conv1)
with tf.name_scope('vgg_cnn_conv2') as inner_scope:
wcnn2 = tu.weight([3, 3, 64, 64], name='wcnn2')
bcnn2 = tu.bias(0.0, [64], name='bcnn2')
conv2 = tf.add(tu.conv2d(conv1, wcnn2, stride=(1,1),padding='SAME'), bcnn2)
conv2 = tu.relu(conv2)
with tf.name_scope('vgg_cnn_pool1') as inner_scope:
pool1 = tu.max_pool2d(conv2, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')
with tf.name_scope('vgg_cnn_conv3') as inner_scope:
wcnn3 = tu.weight([3, 3, 64, 128], name = 'wcnn3')
bcnn3 = tu.bias(0.0, [128], name = 'bcnn3')
conv3 = tf.add(tu.conv2d(pool1, wcnn3, stride = (1,1), padding = 'SAME'), bcnn3)
conv3 = tu.relu(conv3)
with tf.name_scope('vgg_cnn_conv4') as inner_scope:
wcnn4 = tu.weight([3, 3, 128, 128], name = 'wcnn4')
bcnn4 = tu.bias(0.0, [128], name = 'bcnn4')
conv4 = tf.add(tu.conv2d(conv3, wcnn4, stride = (1,1), padding = 'SAME'), bcnn4)
conv4 = tu.relu(conv4)
with tf.name_scope('vgg_cnn_pool2') as inner_scope:
pool2 = tu.max_pool2d(conv4, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')
with tf.name_scope('vgg_cnn_conv5') as inner_scope:
wcnn5 = tu.weight([3, 3, 128, 256 ], name='wcnn5')
bcnn5 = tu.bias(0.0, [256], name='bcnn5')
conv5 = tf.add(tu.conv2d(pool2, wcnn5, stride=(1,1), padding='SAME'), bcnn5)
conv5 = tu.relu(conv5)
with tf.name_scope('vgg_cnn_conv6') as inner_scope:
wcnn6 = tu.weight([3, 3, 256, 256], name='wcnn6')
bcnn6 = tu.bias(0.0, [256], name='bcnn6')
conv6 = tf.add(tu.conv2d(conv5, wcnn6, stride=(1,1), padding='SAME'), bcnn5)
conv6 = tu.relu(conv6)
with tf.name_scope('vgg_cnn_conv7') as inner_scope:
wcnn7 = tu.weight([3, 3, 256, 256], name='wcnn7')
bcnn7 = tu.bias(0.0, [256], name='bcnn7')
conv7 = tf.add(tu.conv2d(conv6, wcnn7, stride=(1,1), padding='SAME'), bcnn7)
conv7 = tu.relu(conv7)
with tf.name_scope('vgg_cnn_conv8') as inner_scope:
wcnn8 = tu.weight([3, 3, 256, 256], name='wcnn8')
bcnn8 = tu.bias(0.0, [256], name='bcnn8')
conv8 = tf.add(tu.conv2d(conv7, wcnn8, stride=(1,1), padding='SAME'), bcnn8)
conv8 = tu.relu(conv8)
with tf.name_scope('vgg_cnn_pool3') as inner_scope:
pool3 = tu.max_pool2d(conv8, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')
with tf.name_scope('vgg_cnn_conv9') as inner_scope:
wcnn9 = tu.weight([3, 3, 256, 512], name='wcnn9')
bcnn9 = tu.bias(0.0, [512], name='bcnn9')
conv9 = tf.add(tu.conv2d(pool3, wcnn9, stride=(1,1), padding='SAME'), bcnn9)
conv9 = tu.relu(conv9)
with tf.name_scope('vgg_cnn_conv10') as inner_scope:
wcnn10 = tu.weight([3, 3, 512, 512], name='wcnn10')
bcnn10 = tu.bias(0.0, [512], name='bcnn10')
conv10 = tf.add(tu.conv2d(conv9, wcnn10, stride=(1,1), padding='SAME'), bcnn10)
conv10 = tu.relu(conv10)
with tf.name_scope('vgg_cnn_conv11') as inner_scope:
wcnn11 = tu.weight([3, 3, 512, 512], name='wcnn11')
bcnn11 = tu.bias(0.0, [512], name='bcnn11')
conv11 = tf.add(tu.conv2d(conv10, wcnn11, stride=(1,1), padding='SAME'), bcnn11)
conv11 = tu.relu(conv11)
with tf.name_scope('vgg_cnn_conv12') as inner_scope:
wcnn12 = tu.weight([3, 3, 512, 512], name='wcnn12')
bcnn12 = tu.bias(0.0, [512], name='bcnn12')
conv12 = tf.add(tu.conv2d(conv11, wcnn12, stride=(1,1), padding='SAME'), bcnn12)
conv12 = tu.relu(conv12)
with tf.name_scope('vgg_cnn_pool4') as inner_scope:
pool4 = tu.max_pool2d(conv12, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')
with tf.name_scope('vgg_cnn_conv13') as inner_scope:
wcnn13 = tu.weight([3, 3, 512, 512], name='wcnn13')
bcnn13 = tu.bias(0.0, [512], name='bcnn13')
conv13 = tf.add(tu.conv2d(pool4, wcnn13, stride=(1,1), padding='SAME'), bcnn13)
conv13 = tu.relu(conv13)
with tf.name_scope('vgg_cnn_conv14') as inner_scope:
wcnn14 = tu.weight([3, 3, 512, 512], name='wcnn14')
bcnn14 = tu.bias(0.0, [512], name='bcnn14')
conv14 = tf.add(tu.conv2d(conv13, wcnn14, stride=(1,1), padding='SAME'), bcnn14)
conv14 = tu.relu(conv14)
with tf.name_scope('vgg_cnn_conv15') as inner_scope:
wcnn15 = tu.weight([3, 3, 512, 512], name='wcnn15')
bcnn15 = tu.bias(0.0, [512], name='bcnn15')
conv15 = tf.add(tu.conv2d(conv14, wcnn15, stride=(1,1), padding='SAME'), bcnn15)
conv15 = tu.relu(conv15)
with tf.name_scope('vgg_cnn_conv16') as inner_scope:
wcnn16 = tu.weight([3, 3, 512, 512], name='wcnn16')
bcnn16 = tu.bias(0.0, [512], name='bcnn16')
conv16 = tf.add(tu.conv2d(conv15, wcnn16, stride=(1,1), padding='SAME'), bcnn16)
conv16 = tu.relu(conv16)
with tf.name_scope('vgg_cnn_pool5') as inner_scope:
pool5 = tu.max_pool2d(conv16, kernel=[1, 2, 2, 1], stride=[1, 2, 2, 1], padding='SAME')
return pool5
