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ops.py
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import math
import numpy as np
import tensorflow as tf
from tensorflow.python.framework import ops
from utils import *
F = tf.flags.FLAGS
class batch_norm(object):
def __init__(self, epsilon=1e-5, momentum = 0.9, name="batch_norm"):
with tf.variable_scope(name):
self.epsilon = epsilon
self.momentum = momentum
self.name = name
def __call__(self, x, train=True):
return tf.contrib.layers.batch_norm(x,
decay=self.momentum,
updates_collections=None,
epsilon=self.epsilon,
scale=True,
is_training=train,
scope=self.name)
def minibatch_disc(input, num_kernels=10, kernel_size=5, scope="m_bat"):
'''
Modified from http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/
'''
tensor_out = linear(input, num_kernels * kernel_size, scope=scope)
tensor_out = tf.reshape(tensor_out, (-1, num_kernels, kernel_size)) # [bat, B, C]
tensor_out = tf.expand_dims(tensor_out, 3) # [bat, B, C, 1]
diffs = tensor_out - tf.transpose(tensor_out, [3, 1, 2, 0]) # [bat, B, C, bat]
l1_norm = tf.reduce_sum(tf.abs(diffs), 2) # [bat, B, bat]
mb_feats = tf.reduce_sum(tf.exp(-l1_norm), 2) # [bat, B]
return tf.concat([input, mb_feats], 1)
def instance_norm(x):
# instance normalization
epsilon = 1e-9
mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
return tf.div(tf.subtract(x, mean), tf.sqrt(tf.add(var, epsilon)))
def binary_cross_entropy(preds, targets, name=None):
"""Computes binary cross entropy given `preds`.
For brevity, let `x = `, `z = targets`. The logistic loss is
loss(x, z) = - sum_i (x[i] * log(z[i]) + (1 - x[i]) * log(1 - z[i]))
Args:
preds: A `Tensor` of type `float32` or `float64`.
targets: A `Tensor` of the same type and shape as `preds`.
"""
eps = 1e-12
with ops.op_scope([preds, targets], name, "bce_loss") as name:
preds = ops.convert_to_tensor(preds, name="preds")
targets = ops.convert_to_tensor(targets, name="targets")
return tf.reduce_mean(-(targets * tf.log(preds + eps) +
(1. - targets) * tf.log(1. - preds + eps)))
def conv_cond_concatv2(x, y):
return tf.concat([x, y], -1)
def conv_cond_concat(x, y):
"""Concatenate conditioning vector on feature map axis."""
x_shapes = x.get_shape()
y_shapes = y.get_shape()
return tf.concat(3, [x, y * tf.ones([x_shapes[0], x_shapes[1], x_shapes[2], y_shapes[3]])])
def conv2d(input_, output_dim,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, bias=True, pad='SAME',
name="conv2d"):
with tf.variable_scope(name):
w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv2d(input_, w, strides=[1, d_h, d_w, 1], padding=pad)
if bias == True:
biases = tf.get_variable('biases', [output_dim], initializer=tf.constant_initializer(0.0))
conv = tf.reshape(tf.nn.bias_add(conv, biases), conv.get_shape())
return conv
def deconv2d(input_, output_shape, k_h=3, k_w=3, d_h=2, d_w=2, stddev=0.02,
start_bias=0.0, bias=True, padding="SAME", name="deconv2d", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_h, k_h, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
try:
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1], padding=padding)
# Support for verisons of TensorFlow before 0.7.0
except AttributeError:
deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
if bias == True:
biases = tf.get_variable('biases', [output_shape[-1]],
initializer=tf.constant_initializer(start_bias))
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
if with_w:
return deconv, w, biases
else:
return deconv
def lrelu(x, leak=0.1, name="lrelu"):
return tf.maximum(x, leak * x)
def linear(input_, output_size, scope=None, bias=True, stddev=0.01, bias_start=0.0, with_w=False):
shape = input_.get_shape().as_list()
#print ('In linear::::::::::::: ', input_.get_shape())
with tf.variable_scope(scope or "Linear"):
matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
tf.contrib.layers.xavier_initializer() if F.dataset == "mnist"
else tf.random_normal_initializer(stddev=stddev))
if bias == True:
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