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ops.py
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ops.py
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import math
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.python.framework import ops
from utils import *
def batch_norm(x, name="batch_norm"):
return tf.contrib.layers.batch_norm(x, decay=0.9, updates_collections=None, epsilon=1e-5, scale=True, scope=name)
def instance_norm(input, name="instance_norm"):
with tf.variable_scope(name):
depth = input.get_shape()[3]
scale = tf.get_variable("scale", [depth], initializer=tf.random_normal_initializer(1.0, 0.02, dtype=tf.float32))
offset = tf.get_variable("offset", [depth], initializer=tf.constant_initializer(0.0))
mean, variance = tf.nn.moments(input, axes=[1,2], keep_dims=True)
epsilon = 1e-5
inv = tf.rsqrt(variance + epsilon)
normalized = (input-mean)*inv
return scale*normalized + offset
def fc(input_, output_dim, name="fc"):
with tf.variable_scope(name):
return slim.fully_connected(input_, output_dim, activation_fn=tf.nn.tanh)
def conv2d(input_, output_dim, ks=4, s=2, stddev=0.02, padding='SAME', name="conv2d"):
with tf.variable_scope(name):
return slim.conv2d(input_, output_dim, ks, s, padding=padding, activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
biases_initializer=None)
def pool(input_, ks=2, s=1, name='max_pool'):
with tf.variable_scope(name):
return slim.max_pool2d(input_, ks, s)
def conv1d(input_, filters, bias, output_shape, stride, name="conv1d"):
with tf.variable_scope(name):
x = tf.nn.conv1d(input_, filters=filters, stride=stride, data_format='NWC', dilations=None, name=None, padding='SAME')
x = tf.nn.bias_add(x, bias)
return tf.nn.tanh(x)
def deconv1d(input_, filters, output_shape, strides, padding, name="deconv1d"):
'''
input_ : [batch, in_width, in_channels]
filters : [filter_width, output_channels, in_channels]
output_shape : 1-D tensor, containing three elements, representing the output shape of the decovolution op.
padding : 'VALID' or 'SAME'
'''
with tf.variable_scope(name):
return tf.nn.conv1d_transpose(input_, filters, output_shape, strides, padding='SAME', data_format='NWC',dilations=None, name=None)
def deconv2d(input_, output_dim, ks=4, s=2, stddev=0.02, name="deconv2d"):
with tf.variable_scope(name):
return slim.conv2d_transpose(input_, output_dim, ks, s, padding='SAME', activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=stddev),
biases_initializer=None)
def lrelu(x, leak=0.2, name="lrelu"):
return tf.maximum(x, leak*x)
def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
with tf.variable_scope(scope or "Linear"):
matrix = tf.get_variable("Matrix", [input_.get_shape()[-1], output_size], tf.float32,
tf.random_normal_initializer(stddev=stddev))
bias = tf.get_variable("bias", [output_size],
initializer=tf.constant_initializer(bias_start))
if with_w:
return tf.matmul(input_, matrix) + bias, matrix, bias
else:
return tf.matmul(input_, matrix) + bias
def softplus(x): #log(exp(x) + 1).
return tf.nn.softplus(x)#tf.log(tf.exp(x)+1)