# bamos/dcgan-completion.tensorflow

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 # Original Version: Taehoon Kim (http://carpedm20.github.io) # + Source: https://github.com/carpedm20/DCGAN-tensorflow/blob/e30539fb5e20d5a0fed40935853da97e9e55eee8/ops.py # + License: MIT import math import numpy as np import tensorflow as tf from tensorflow.python.framework import ops from utils import * class batch_norm(object): """Code modification of http://stackoverflow.com/a/33950177""" 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): return tf.contrib.layers.batch_norm(x, decay=self.momentum, updates_collections=None, epsilon=self.epsilon, center=True, scale=True, is_training=train, scope=self.name) 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_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, 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='SAME') 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()) conv = tf.nn.bias_add(conv, biases) return conv def conv2d_transpose(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name="conv2d_transpose", with_w=False): with tf.variable_scope(name): # filter : [height, width, output_channels, in_channels] w = tf.get_variable('w', [k_h, k_w, 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]) # 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]) biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0)) # deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape()) deconv = tf.nn.bias_add(deconv, biases) if with_w: return deconv, w, biases else: return deconv def lrelu(x, leak=0.2, name="lrelu"): with tf.variable_scope(name): f1 = 0.5 * (1 + leak) f2 = 0.5 * (1 - leak) return f1 * x + f2 * abs(x) def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False): shape = input_.get_shape().as_list() with tf.variable_scope(scope or "Linear"): matrix = tf.get_variable("Matrix", [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