def _convolution(name, input_tensor, kernel_shape, stddev=1e-4, wd=0.0, reuse=False):
with tf.variable_scope(name, reuse=reuse) as scope:
kernel = helper._variable_with_weight_decay('weights', shape=kernel_shape, stddev=stddev, wd=wd)
biases = helper._variable('biases', [kernel_shape[-1]], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(input_tensor, kernel, [1, 1, 1, 1], padding='SAME')
output_tensor = tf.nn.bias_add(conv, biases)
if not reuse:
helper._activation_summary(output_tensor)
helper._multichannel_image_summary(scope.name + '/weights', kernel, perm=[3, 2, 1, 0])
return output_tensor
def _convolution(name, input_tensor, kernel_shape, stddev=1e-4, wd=0.0, reuse=False):
with tf.variable_scope(name, reuse=reuse) as scope:
kernel = helper._variable_with_weight_decay("weights", shape=kernel_shape, stddev=stddev, wd=wd)
biases = helper._variable("biases", [kernel_shape[-1]], tf.constant_initializer(0.0))
conv = tf.nn.conv2d(input_tensor, kernel, [1, 1, 1, 1], padding="SAME")
output_tensor = tf.nn.bias_add(conv, biases)
if not reuse:
helper._activation_summary(output_tensor)
helper._multichannel_image_summary(scope.name + "/weights", kernel, perm=[3, 2, 1, 0])
return output_tensor
def forward(self, images, dropratio_tensor, reuse=False):
conv1 = tf.nn.elu(elements._convolution('layer1_conv',
images,
kernel_shape=[5, 5, 3, 64],
reuse=reuse),
name='layer1_conv')
pool1 = elements._pooling('layer1_pool',
conv1,
kernel_shape=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
reuse=reuse)
conv2 = tf.nn.elu(elements._convolution('layer2_conv',
pool1,
kernel_shape=[5, 5, 64, 64],
reuse=reuse),
name='layer2_conv')
pool2 = elements._pooling('layer2_pool',
conv2,
kernel_shape=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
reuse=reuse)
conv3 = tf.nn.elu(elements._convolution('layer3_conv',
pool2,
kernel_shape=[5, 5, 64, 64],
reuse=reuse),
name='layer3_conv')
pool3 = elements._pooling('layer3_pool',
conv3,
kernel_shape=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
reuse=reuse)
local4 = tf.nn.elu(elements._dropout('layer4_dropout',
pool3,
hidden_nodes=512,
keep_prob_tensor=dropratio_tensor,
stddev=0.04,
wd=0.004,
reuse=reuse),
name='layer4_dropout')
logits = elements._fullconnect('layer5_linear',
local4,
hidden_nodes=10,
stddev=1 / 192.0,
wd=0.0,
reuse=reuse)
if reuse:
return logits
with tf.name_scope('summary_images') as scope:
helper._activation_summary(images)
tf.image_summary('input/images', images, max_images=3)
helper._multichannel_image_summary('conv1/output', conv1)
helper._multichannel_image_summary('conv2/output', conv2)
helper._multichannel_image_summary('conv3/output', conv3)
return logits
def forward(self, images, dropratio_tensor, reuse=False):
conv1 = tf.nn.elu( elements._convolution('layer1_conv', images, kernel_shape=[5, 5, 3, 64], reuse=reuse), name='layer1_conv')
pool1 = elements._pooling('layer1_pool', conv1, kernel_shape=[1, 3, 3, 1], strides=[1, 2, 2, 1], reuse=reuse)
conv2 = tf.nn.elu( elements._convolution('layer2_conv', pool1, kernel_shape=[5, 5, 64, 64], reuse=reuse), name='layer2_conv')
pool2 = elements._pooling('layer2_pool', conv2, kernel_shape=[1, 3, 3, 1], strides=[1, 2, 2, 1], reuse=reuse)
conv3 = tf.nn.elu( elements._convolution('layer3_conv', pool2, kernel_shape=[5, 5, 64, 64], reuse=reuse), name='layer3_conv')
pool3 = elements._pooling('layer3_pool', conv3, kernel_shape=[1, 3, 3, 1], strides=[1, 2, 2, 1], reuse=reuse)
local4 = tf.nn.elu( elements._dropout('layer4_dropout', pool3, hidden_nodes=512, keep_prob_tensor=dropratio_tensor, stddev=0.04, wd=0.004, reuse=reuse), name='layer4_dropout')
logits = elements._fullconnect('layer5_linear', local4, hidden_nodes=10, stddev=1/192.0, wd=0.0, reuse=reuse)
if reuse:
return logits
with tf.name_scope('summary_images') as scope:
helper._activation_summary(images)
tf.image_summary('input/images', images, max_images=3)
helper._multichannel_image_summary('conv1/output', conv1)
helper._multichannel_image_summary('conv2/output', conv2)
helper._multichannel_image_summary('conv3/output', conv3)
return logits
