Conditional-PixelCNN-decoder/models.py at master · anantzoid/Conditional-PixelCNN-decoder · GitHub

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import tensorflow as tf

from layers import *

class PixelCNN(object):

def __init__(self, X, conf, full_horizontal=True, h=None):

self.X = X

if conf.data == "mnist":

self.X_norm = X

else:

'''

Image normalization for CIFAR-10 was supposed to be done here

'''

self.X_norm = X

v_stack_in, h_stack_in = self.X_norm, self.X_norm

if conf.conditional is True:

if h is not None:

self.h = h

else:

self.h = tf.placeholder(tf.float32, shape=[None, conf.num_classes])

else:

self.h = None

for i in range(conf.layers):

filter_size = 3 if i > 0 else 7

mask = 'b' if i > 0 else 'a'

residual = True if i > 0 else False

i = str(i)

with tf.variable_scope("v_stack"+i):

v_stack = GatedCNN([filter_size, filter_size, conf.f_map], v_stack_in, False, mask=mask, conditional=self.h).output()

v_stack_in = v_stack

with tf.variable_scope("v_stack_1"+i):

v_stack_1 = GatedCNN([1, 1, conf.f_map], v_stack_in, False, gated=False, mask=None).output()

with tf.variable_scope("h_stack"+i):

h_stack = GatedCNN([filter_size if full_horizontal else 1, filter_size, conf.f_map], h_stack_in, True, payload=v_stack_1, mask=mask, conditional=self.h).output()

with tf.variable_scope("h_stack_1"+i):

h_stack_1 = GatedCNN([1, 1, conf.f_map], h_stack, True, gated=False, mask=None).output()

if residual:

h_stack_1 += h_stack_in # Residual connection

h_stack_in = h_stack_1

with tf.variable_scope("fc_1"):

fc1 = GatedCNN([1, 1, conf.f_map], h_stack_in, True, gated=False, mask='b').output()

if conf.data == "mnist":

with tf.variable_scope("fc_2"):

self.fc2 = GatedCNN([1, 1, 1], fc1, True, gated=False, mask='b', activation=False).output()

self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.fc2, labels=self.X))

self.pred = tf.nn.sigmoid(self.fc2)

else:

color_dim = 256

with tf.variable_scope("fc_2"):

self.fc2 = GatedCNN([1, 1, conf.channel * color_dim], fc1, True, gated=False, mask='b', activation=False).output()

self.fc2 = tf.reshape(self.fc2, (-1, color_dim))

self.loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(self.fc2, tf.cast(tf.reshape(self.X, [-1]), dtype=tf.int32)))

'''

Since this code was not run on CIFAR-10, I'm not sure which

would be a suitable way to generate 3-channel images. Below are

the 2 methods which may be used, with the first one (self.pred)

being more likely.

'''

self.pred_sampling = tf.reshape(tf.multinomial(tf.nn.softmax(self.fc2), num_samples=1, seed=100), tf.shape(self.X))

self.pred = tf.reshape(tf.argmax(tf.nn.softmax(self.fc2), dimension=tf.rank(self.fc2) - 1), tf.shape(self.X))

class ConvolutionalEncoder(object):

def __init__(self, X, conf):

'''

This is the 6-layer architecture for Convolutional Autoencoder

mentioned in the original paper:

Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction

Note that only the encoder part is implemented as PixelCNN is taken

as the decoder.

'''

W_conv1 = get_weights([5, 5, conf.channel, 100], "W_conv1")

b_conv1 = get_bias([100], "b_conv1")

conv1 = tf.nn.relu(conv_op(X, W_conv1) + b_conv1)

pool1 = max_pool_2x2(conv1)

W_conv2 = get_weights([5, 5, 100, 150], "W_conv2")

b_conv2 = get_bias([150], "b_conv2")

conv2 = tf.nn.relu(conv_op(pool1, W_conv2) + b_conv2)

pool2 = max_pool_2x2(conv2)

W_conv3 = get_weights([3, 3, 150, 200], "W_conv3")

b_conv3 = get_bias([200], "b_conv3")

conv3 = tf.nn.relu(conv_op(pool2, W_conv3) + b_conv3)

conv3_reshape = tf.reshape(conv3, (-1, 7*7*200))

W_fc = get_weights([7*7*200, 10], "W_fc")

b_fc = get_bias([10], "b_fc")

self.pred = tf.nn.softmax(tf.add(tf.matmul(conv3_reshape, W_fc), b_fc))

Conditional-PixelCNN-decoder/models.py at master · anantzoid/Conditional-PixelCNN-decoder · GitHub

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