def __call__(self, D):
with tf.variable_scope(self.name, reuse=self.reuse):
D_norm = D
G_conv1 = utils.conv2d_basic(D_norm, self.Param['G_W1'], self.Param['G_b1'])
G_relu1 = tf.nn.relu(G_conv1, name="G_relu1")
G_conv2 = utils.conv2d_basic(G_relu1, self.Param['G_W2'], self.Param['G_b2'])
G_relu2 = tf.nn.relu(G_conv2, name="G_relu2")
G_pool1 = utils.max_pool_2x2(G_relu2)
G_conv3 = utils.conv2d_basic(G_pool1, self.Param['G_W3'], self.Param['G_b3'])
G_relu3 = tf.nn.relu(G_conv3, name="G_relu3")
G_conv4 = utils.conv2d_basic(G_relu3, self.Param['G_W4'], self.Param['G_b4'])
G_relu4 = tf.nn.relu(G_conv4, name="G_relu4")
G_pool2 = utils.max_pool_2x2(G_relu4)
G_conv5 = utils.conv2d_basic(G_pool2, self.Param['G_W5'], self.Param['G_b5'])
G_relu5 = tf.nn.relu(G_conv5, name="G_relu5")
output_shape = G_relu5.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = self.Param['G_W6'].get_shape().as_list()[2]
G_rs6 = tf.image.resize_images(G_relu5, output_shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_conv6 = utils.conv2d_basic(G_rs6, self.Param['G_W6'], self.Param['G_b6'])
G_relu6 = tf.nn.relu(G_conv6, name="G_rs6")
output_shape = G_relu6.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = self.Param['G_W7'].get_shape().as_list()[2]
G_rs7 = tf.image.resize_images(G_relu6, output_shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_conv7 = utils.conv2d_basic(G_rs7, self.Param['G_W7'], self.Param['G_b7'])
G_relu7 = tf.nn.relu(G_conv7, name="G_rs7")
G_conv8 = utils.conv2d_basic(G_relu7, self.Param['G_W8'], self.Param['G_b8'])
G_relu8 = tf.nn.relu(G_conv8, name="G_relu8")
G_conv9 = utils.conv2d_basic(G_relu8, self.Param['G_W9'], self.Param['G_b9'])
Gama = tf.nn.softmax(G_conv9, name="G_latent_softmax")
return Gama
def __call__(self, D):
with tf.variable_scope(self.name, reuse=self.reuse):
D_norm = D
G_conv1 = utils.conv2d_basic(D_norm, self.Param['G_W1'], self.Param['G_b1'])
G_relu1 = tf.nn.relu(G_conv1, name="G_relu1")
G_conv2 = utils.conv2d_basic(G_relu1, self.Param['G_W2'], self.Param['G_b2'])
G_relu2 = tf.nn.relu(G_conv2, name="G_relu2")
G_pool1 = utils.max_pool_2x2(G_relu2)
G_conv3 = utils.conv2d_basic(G_pool1, self.Param['G_W3'], self.Param['G_b3'])
G_relu3 = tf.nn.relu(G_conv3, name="G_relu3")
G_conv4 = utils.conv2d_basic(G_relu3, self.Param['G_W4'], self.Param['G_b4'])
G_relu4 = tf.nn.relu(G_conv4, name="G_relu4")
G_pool2 = utils.max_pool_2x2(G_relu4)
G_conv5 = utils.conv2d_basic(G_pool2, self.Param['G_W5'], self.Param['G_b5'])
G_relu5 = tf.nn.relu(G_conv5, name="G_relu5")
output_shape = G_relu5.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = self.Param['G_W6'].get_shape().as_list()[2]
G_rs6 = tf.image.resize_images(G_relu5, output_shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_conv6 = utils.conv2d_basic(G_rs6, self.Param['G_W6'], self.Param['G_b6'])
G_relu6 = tf.nn.relu(G_conv6, name="G_rs6")
output_shape = G_relu6.get_shape().as_list()
output_shape[1] *= 2
output_shape[2] *= 2
output_shape[3] = self.Param['G_W7'].get_shape().as_list()[2]
G_rs7 = tf.image.resize_images(G_relu6, output_shape[1:3], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_conv7 = utils.conv2d_basic(G_rs7, self.Param['G_W7'], self.Param['G_b7'])
G_relu7 = tf.nn.relu(G_conv7, name="G_rs7")
G_conv8 = utils.conv2d_basic(G_relu7, self.Param['G_W8'], self.Param['G_b8'])
G_relu8 = tf.nn.relu(G_conv8, name="G_relu8")
G_conv9 = utils.conv2d_basic(G_relu8, self.Param['G_W9'], self.Param['G_b9'])
Gama = tf.nn.softmax(G_conv9, name="G_latent_softmax")
return Gama
def build_encoding(self, x):
"""
Builds graph to create encoding from input x
:param x: input image
:return: flat layer containing the encoding
"""
def getVars(name, w_shape):
"""
Helper function to resuse variables in order to create a siamese net.
:param name: Name of the variable we want
:param w_shape: Shape of the variable we want
:return: Variable with given name if exists, otherwise new variable with given shape
"""
w = tf.get_variable("W" + name, w_shape, initializer=tf.random_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(0.01))
b = tf.get_variable("b" + name, [w_shape[-1]], initializer=tf.constant_initializer(0.1),
regularizer=tf.contrib.layers.l2_regularizer(0.01))
return w, b
prev_layer = x
img_size = self.shape[2]
for ind in range(len(self.conv_layer_size)):
"""
Iterate through conv_layers and apply convolution, rele and max-pool
"""
if ind == 0:
w_shape = [self.conv_dim[ind], self.conv_dim[ind], self.shape[3], self.conv_layer_size[ind]]
else:
w_shape = [self.conv_dim[ind], self.conv_dim[ind], self.conv_layer_size[ind - 1],
self.conv_layer_size[ind]]
w, b = getVars("enc%s" % ind, w_shape)
prev_layer = ops.max_pool_2x2(tf.nn.relu(ops.conv_2d(prev_layer, w, b)))
self.enc_weights.append(w)
self.enc_weights.append(b)
# Reshape for fully connected layers
next_size = self.conv_layer_size[-1] * img_size / (2 ** len(self.conv_layer_size)) * img_size / (
2 ** len(self.conv_layer_size))
flat_layer = tf.reshape(prev_layer, [-1, next_size])
for ind in range(len(self.fcl_layer_size)):
"""
Iterate through fully connected layers and apply matmul and sigmoid
"""
if ind == 0:
w_shape = [next_size, self.fcl_layer_size[0]]
else:
w_shape = [self.fcl_layer_size[ind - 1], self.fcl_layer_size[ind]]
w, b = getVars("enc_fcl%s" % ind, w_shape)
flat_layer = tf.nn.sigmoid(tf.matmul(flat_layer, w) + b)
self.enc_weights.append(w)
self.enc_weights.append(b)
return flat_layer
def make_convnet(self):
n_ffnet_inputs = self.n_ffnet_input
n_ffnet_outputs = self.n_ffnet_output
print("COVNET: Inputs: ", n_ffnet_inputs, " outputs: ", n_ffnet_outputs)
with tf.name_scope('reshape'):
x_image = tf.reshape(self.covnet_input, [-1, self.resolution[0], self.resolution[1], 1])
with tf.name_scope('conv1'):
W_conv1 = my_ops.weight_variable([5, 5, 1, 32])
b_conv1 = my_ops.bias_variable([32])
h_conv1 = tf.nn.relu(my_ops.conv2d(x_image, W_conv1) + b_conv1)
with tf.name_scope('pool1'):
h_pool1 = my_ops.max_pool_2x2(h_conv1)
with tf.name_scope('conv2'):
W_conv2 = my_ops.weight_variable([5, 5, 32, 64])
b_conv2 = my_ops.bias_variable([64])
h_conv2 = tf.nn.relu(my_ops.conv2d(h_pool1, W_conv2) + b_conv2)
with tf.name_scope('pool2'):
h_pool2 = my_ops.max_pool_2x2(h_conv2)
with tf.name_scope('fc1'):
W_fc1 = my_ops.weight_variable([int(self.resolution[0]/4) * int(self.resolution[1]/4) * 64, 64])
b_fc1 = my_ops.bias_variable([64])
h_pool2_flat = tf.reshape(h_pool2, [-1, int(self.resolution[0]/4) * int(self.resolution[1]/4) * 64])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# single output:
with tf.name_scope('fc2'):
W_fc2 = my_ops.weight_variable([64, 1])
b_fc2 = my_ops.bias_variable([1])
self.y_conv = tf.tanh(tf.matmul(h_fc1, W_fc2) + b_fc2)
self.covloss = tf.squared_difference(self.y_conv, self.covnet_target)
self.covnet_train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(self.covloss)
self.covaccuracy = tf.reduce_mean(self.covloss)
def __call__(self, D):
with tf.variable_scope(self.name, reuse=self.reuse):
D_norm = D
G_conv1 = utils.conv2d_basic(D_norm, self.Param['G_W1'],
self.Param['G_b1'])
G_relu1 = tf.nn.relu(G_conv1, name="G_relu1")
G_conv2 = utils.conv2d_basic(G_relu1, self.Param['G_W2'],
self.Param['G_b2'])
G_relu2 = tf.nn.relu(G_conv2, name="G_relu2")
G_pool1 = utils.max_pool_2x2(G_relu2)
G_conv3 = utils.conv2d_basic(G_pool1, self.Param['G_W3'],
self.Param['G_b3'])
G_relu3 = tf.nn.relu(G_conv3, name="G_relu3")
G_conv4 = utils.conv2d_basic(G_relu3, self.Param['G_W4'],
self.Param['G_b4'])
G_relu4 = tf.nn.relu(G_conv4, name="G_relu4")
G_pool2 = utils.max_pool_2x2(G_relu4)
G_conv5 = utils.conv2d_basic(G_pool2, self.Param['G_W5'],
self.Param['G_b5'])
G_relu5 = tf.nn.relu(G_conv5, name="G_relu5")
G_conv6 = utils.conv2d_basic(G_relu5, self.Param['G_W6'],
self.Param['G_b6'])
G_relu6 = tf.nn.relu(G_conv6, name="G_relu6")
G_conv7 = utils.conv2d_basic(G_relu6, self.Param['G_W7'],
self.Param['G_b7'])
G_relu7 = tf.nn.relu(G_conv7, name="G_relu7")
G_pool3 = utils.max_pool_2x2(G_relu7)
G_conv8 = utils.conv2d_basic(G_pool3, self.Param['G_W8'],
self.Param['G_b8'])
G_relu8 = tf.nn.relu(G_conv8, name="G_relu8")
G_conv9 = utils.conv2d_basic(G_relu8, self.Param['G_W9'],
self.Param['G_b9'])
G_relu9 = tf.nn.relu(G_conv9, name="G_relu9")
G_conv10 = utils.conv2d_basic(G_relu9, self.Param['G_W10'],
self.Param['G_b10'])
G_relu10 = tf.nn.relu(G_conv10, name="G_relu10")
G_pool4 = utils.max_pool_2x2(G_relu10)
G_conv11 = utils.conv2d_basic(G_pool4, self.Param['G_W11'],
self.Param['G_b11'])
G_relu11 = tf.nn.relu(G_conv11, name="G_relu11")
G_conv12 = utils.conv2d_basic(G_relu11, self.Param['G_W12'],
self.Param['G_b12'])
G_relu12 = tf.nn.relu(G_conv12, name="G_relu12")
G_conv13 = utils.conv2d_basic(G_relu12, self.Param['G_W13'],
self.Param['G_b13'])
G_relu13 = tf.nn.relu(G_conv13, name="G_relu13")
G_rs14 = tf.image.resize_images(
G_relu13, [63, 63],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#G_rs14 = tf.image.resize_images(G_relu13, [64, 64], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_cc14 = tf.concat([G_rs14, G_relu10], 3, name="G_cc14")
G_conv14 = utils.conv2d_basic(G_cc14, self.Param['G_W14'],
self.Param['G_b14'])
G_relu14 = tf.nn.relu(G_conv14, name="G_rs14")
G_rs15 = tf.image.resize_images(
G_relu14, [125, 125],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#G_rs15 = tf.image.resize_images(G_relu14, [128, 128], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_cc15 = tf.concat([G_rs15, G_relu7], 3, name="G_cc15")
G_conv15 = utils.conv2d_basic(G_cc15, self.Param['G_W15'],
self.Param['G_b15'])
G_relu15 = tf.nn.relu(G_conv15, name="G_rs15")
G_rs16 = tf.image.resize_images(
G_relu15, [250, 250],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#G_rs16 = tf.image.resize_images(G_relu15, [256, 256], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_cc16 = tf.concat([G_rs16, G_relu4], 3, name="G_cc16")
G_conv16 = utils.conv2d_basic(G_cc16, self.Param['G_W16'],
self.Param['G_b16'])
G_relu16 = tf.nn.relu(G_conv16, name="G_rs16")
G_rs17 = tf.image.resize_images(
G_relu16, [500, 500],
method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
#G_rs17 = tf.image.resize_images(G_relu16, [512, 512], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
G_cc17 = tf.concat([G_rs17, G_relu2], 3, name="G_cc17")
G_conv17 = utils.conv2d_basic(G_cc17, self.Param['G_W17'],
self.Param['G_b17'])
G_relu17 = tf.nn.relu(G_conv17, name="G_rs17")
G_conv18 = utils.conv2d_basic(G_relu17, self.Param['G_W18'],
self.Param['G_b18'])
G_relu18 = tf.nn.relu(G_conv18, name="G_relu18")
G_conv19 = utils.conv2d_basic(G_relu18, self.Param['G_W19'],
self.Param['G_b19'])
Gama = tf.nn.softmax(G_conv19, name="G_latent_softmax")
return Gama
def make_convnet(self):
# # self.resolution is [width, height]
# x_image = tf.reshape(self.covnet_input, [-1, self.resolution[0], self.resolution[1], 1])
#
# n_features_maps = 20
# filter_width = int(2)
# filter_height = int(2)
#
# W_conv1 = my_ops.weight_variable([filter_width, filter_height, 1, n_features_maps], 0.001)
# b_conv1 = my_ops.bias_variable([n_features_maps])
# h_conv1 = tf.nn.tanh(my_ops.conv2d(x_image, W_conv1) + b_conv1)
#
# h_pool1 = my_ops.max_pool_2x2(h_conv1)
# # h_pool1 = h_conv1
#
# W_fc1 = my_ops.weight_variable([int(self.resolution[0]/2) * int(self.resolution[1]/2) * n_features_maps, 20], 0.001)
# b_fc1 = my_ops.bias_variable([20])
#
# h_pool1_flat = tf.reshape(h_pool1, [-1, int(self.resolution[0]/2) * int(self.resolution[1]/2) * n_features_maps])
# h_fc1 = tf.nn.tanh(tf.matmul(h_pool1_flat, W_fc1) + b_fc1)
#
# # single output:
# W_fc2 = my_ops.weight_variable([20, 1], 0.01)
# b_fc2 = my_ops.bias_variable([1])
#
# self.y_conv = tf.tanh(tf.matmul(h_fc1, W_fc2) + b_fc2)
#
# self.covloss = tf.squared_difference(self.y_conv, self.covnet_target)
# self.covnet_train_step = tf.train.AdamOptimizer(self.learning_rate).minimize(self.covloss)
# self.covaccuracy = tf.reduce_mean(self.covloss)
with tf.name_scope('reshape'):
x_image = tf.reshape(
self.covnet_input,
[-1, self.resolution[0], self.resolution[1], 1])
# First convolutional layer - maps one grayscale image to 32 feature maps.
with tf.name_scope('conv1'):
W_conv1 = my_ops.weight_variable([5, 5, 1, 8], 0.1)
b_conv1 = my_ops.bias_variable([8])
h_conv1 = tf.nn.relu(my_ops.conv2d(x_image, W_conv1) + b_conv1)
# Pooling layer - downsamples by 2X.
with tf.name_scope('pool1'):
h_pool1 = my_ops.max_pool_2x2(h_conv1)
# Second convolutional layer -- maps 32 feature maps to 64.
with tf.name_scope('conv2'):
W_conv2 = my_ops.weight_variable([5, 5, 8, 8], 0.1)
b_conv2 = my_ops.bias_variable([8])
h_conv2 = tf.nn.relu(my_ops.conv2d(h_pool1, W_conv2) + b_conv2)
# Second pooling layer.
with tf.name_scope('pool2'):
h_pool2 = my_ops.max_pool_2x2(h_conv2)
# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
# is down to 7x7x64 feature maps -- maps this to 1024 features.
with tf.name_scope('fc1'):
W_fc1 = my_ops.weight_variable([
int(self.resolution[0] / 4) * int(self.resolution[1] / 4) * 8,
10
], 0.001)
b_fc1 = my_ops.bias_variable([10])
h_pool2_flat = tf.reshape(h_pool2, [
-1,
int(self.resolution[0] / 4) * int(self.resolution[1] / 4) * 8
])
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
# Map the 1024 features to 10 classes, one for each digit
with tf.name_scope('fc2'):
W_fc2 = my_ops.weight_variable([10, 1], 0.1)
b_fc2 = my_ops.bias_variable([1])
self.y_conv = tf.tanh(tf.matmul(h_fc1, W_fc2) + b_fc2)
self.covloss = tf.squared_difference(self.y_conv,
self.covnet_target)
self.covnet_train_step = tf.train.AdamOptimizer(
self.learning_rate).minimize(self.covloss)
self.covaccuracy = tf.reduce_mean(self.covloss)
