import tensorflow as tf
from network.Layer import Layer
from network.Util import prepare_input
class LinearCombination(Layer):
output_layer = False
def __init__(self, name, inputs, tower_setup, initial_weights, hack_gradient_magnitude=1.0):
super().__init__()
assert len(initial_weights) == len(inputs)
with tf.variable_scope(name):
initializer = tf.constant_initializer(initial_weights)
weights = self.create_bias_variable("linear_combination_weights", len(inputs), tower_setup,
initializer=initializer)
if hack_gradient_magnitude > 1.0:
# https://stackoverflow.com/a/43948872
@tf.custom_gradient
def amplify_gradient_layer(x):
def grad(dy):
return hack_gradient_magnitude * dy
return tf.identity(x), grad
weights = amplify_gradient_layer(weights)
y = inputs[0] * weights[0]
for n in range(1, len(inputs)):
y += inputs[n] * weights[n]
self.outputs.append(y)
for n in range(len(inputs)):
self.add_scalar_summary(weights[n], "linear_combination_weights_" + str(n))
class NNUpsizing(Layer):
output_layer = False
def __init__(self, name, inputs, tower_setup, factor=2):
super().__init__()
with tf.variable_scope(name):
inp, dim = prepare_input(inputs)
height = tf.shape(inp)[1]
width = tf.shape(inp)[2]
resized_input = tf.image.resize_images(inputs[0], (height*factor, width*factor),
tf.image.ResizeMethod.NEAREST_NEIGHBOR)
self.outputs.append(resized_input)
class StackingLayer(Layer):
output_layer = False
def __init__(self, name, inputs, tower_setup):
super().__init__()
assert len(inputs) == 2
self.outputs.append(tf.concat(inputs, axis=-1))
