Python源码示例：tfcode.cmp.deconv()
示例1
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
示例2
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
示例3
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
示例4
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
示例5
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
示例6
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
示例7
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
示例8
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
示例9
def readout_general(multi_scale_belief, num_neurons, strides, layers_per_block,
kernel_size, batch_norm_is_training_op, wt_decay):
multi_scale_belief = tf.stop_gradient(multi_scale_belief)
with tf.variable_scope('readout_maps_deconv'):
x, outs = deconv(multi_scale_belief, batch_norm_is_training_op,
wt_decay=wt_decay, neurons=num_neurons, strides=strides,
layers_per_block=layers_per_block, kernel_size=kernel_size,
conv_fn=slim.conv2d_transpose, offset=0,
name='readout_maps_deconv')
probs = tf.sigmoid(x)
return x, probs
