def pz1_given_z2y(y, z2, num_channels, filter_sizes, num_filters, input_dim, fc_size, reuse=False):
with tf.variable_scope("decoder_M2", reuse=reuse):
# Variables
z2y = tf.concat([y, z2], axis=1)
expanded_z2y = tf.expand_dims(tf.expand_dims(z2y, 1), 1)
print("filter_sizes:{}".format(filter_sizes))
layer_conv1, weights_conv1 = conv_layer(input=expanded_z2y, num_input_channels=num_channels,
filter_size=filter_sizes[0],
num_filters=num_filters[0], use_pooling=True, layer_name='layer1')
print("layer conv1: {}".format(layer_conv1))
# ### Convolutional Layer 2
layer_conv2, weights_conv2 = conv_layer(input=layer_conv1, num_input_channels=num_filters[0],
filter_size=filter_sizes[1], num_filters=num_filters[1],
use_pooling=True, layer_name='layer2')
print("layer conv2: {}".format(layer_conv2))
# ### Flatten Layer
layer_flat, num_features = flatten_layer(layer_conv2)
print("layer flat: {}".format(layer_flat))
print("num_features: {}".format(num_features))
# ### Fully-Connected Layer 1
layer_fc1 = fc_layer(input=layer_flat, num_inputs=num_features, num_outputs=fc_size, use_relu=True)
print("layer fc1: {}".format(layer_fc1))
# x_mu = mlp_neuron(h2, w_mu, b_mu, activation=False)
z1_logvar = fc_layer(input=layer_fc1, num_inputs=fc_size, num_outputs=input_dim, use_relu=False)
z1_mu = fc_layer(input=layer_fc1, num_inputs=fc_size, num_outputs=input_dim, use_relu=False)
z1 = draw_norm(input_dim, z1_mu, z1_logvar)
return z1, z1_mu, z1_logvar
def q_z2_given_z1y(z1,
y,
latent_dim,
input_dim,
fc_size,
filter_sizes,
num_channels,
num_filters,
reuse=False):
with tf.variable_scope("encoder_M2", reuse=reuse):
z1y = tf.concat([y, z1], axis=1)
expanded_z1y = tf.expand_dims(tf.expand_dims(z1y, 1), 1)
print("filter_sizes:{}".format(filter_sizes))
layer_conv1, weights_conv1 = conv_layer(
input=expanded_z1y,
num_input_channels=num_channels,
filter_size=filter_sizes[0],
num_filters=num_filters[0],
use_pooling=True,
layer_name='layer1')
print("layer conv1: {}".format(layer_conv1))
# ### Convolutional Layer 2
layer_conv2, weights_conv2 = conv_layer(
input=layer_conv1,
num_input_channels=num_filters[0],
filter_size=filter_sizes[1],
num_filters=num_filters[1],
use_pooling=True,
layer_name='layer2')
print("layer conv2: {}".format(layer_conv2))
# ### Flatten Layer
layer_flat, num_features = flatten_layer(layer_conv2)
print("layer flat: {}".format(layer_flat))
print("num_features: {}".format(num_features))
# ### Fully-Connected Layer 1
layer_fc1 = fc_layer(input=layer_flat,
num_inputs=num_features,
num_outputs=fc_size,
use_relu=True)
print("layer fc1: {}".format(layer_fc1))
logvar_z2 = fc_layer(input=layer_fc1,
num_inputs=fc_size,
num_outputs=input_dim,
use_relu=False)
mu_z2 = fc_layer(input=layer_fc1,
num_inputs=fc_size,
num_outputs=input_dim,
use_relu=False)
z2 = draw_norm(latent_dim, mu_z2, logvar_z2)
return z2, mu_z2, logvar_z2
def q_z1_given_x(x,
num_channels,
filter_sizes,
num_filters,
latent_dim,
fc_size,
reuse=False):
with tf.variable_scope("encoder_M1", reuse=reuse):
layer_conv1, weights_conv1 = conv_layer(
input=x,
num_input_channels=num_channels,
filter_size=filter_sizes[0],
num_filters=num_filters[0],
use_pooling=True,
layer_name='layer1')
print("layer conv1: {}".format(layer_conv1))
# ### Convolutional Layer 2
layer_conv2, weights_conv2 = conv_layer(
input=layer_conv1,
num_input_channels=num_filters[0],
filter_size=filter_sizes[1],
num_filters=num_filters[1],
use_pooling=True,
layer_name='layer2')
print("layer conv2: {}".format(layer_conv2))
# ### Flatten Layer
layer_flat, num_features = flatten_layer(layer_conv2)
print("layer flat: {}".format(layer_flat))
print("num_features: {}".format(num_features))
# ### Fully-Connected Layer 1
layer_fc1 = fc_layer(input=layer_flat,
num_inputs=num_features,
num_outputs=fc_size,
use_relu=True)
print("layer fc1: {}".format(layer_fc1))
# ### Fully-Connected Layer 2
logvar_z1 = fc_layer(input=layer_fc1,
num_inputs=fc_size,
num_outputs=latent_dim,
use_relu=False)
mu_z1 = fc_layer(input=layer_fc1,
num_inputs=fc_size,
num_outputs=latent_dim,
use_relu=False)
# Model
z1 = draw_norm(latent_dim, mu_z1, logvar_z1)
return z1, mu_z1, logvar_z1
def px_given_z1(z1,
input_dim,
num_channels,
filter_sizes,
fc_size,
num_filters,
reuse=False):
with tf.variable_scope("decoder_M1", reuse=reuse):
# Variables
print("filter_sizes:{}".format(filter_sizes))
layer_conv1, weights_conv1 = conv_layer(
input=z1,
num_input_channels=num_channels,
filter_size=filter_sizes[0],
num_filters=num_filters[0],
use_pooling=True,
layer_name='layer1')
print("layer conv1: {}".format(layer_conv1))
# ### Convolutional Layer 2
layer_conv2, weights_conv2 = conv_layer(
input=layer_conv1,
num_input_channels=num_filters[0],
filter_size=filter_sizes[1],
num_filters=num_filters[1],
use_pooling=True,
layer_name='layer2')
print("layer conv2: {}".format(layer_conv2))
# ### Flatten Layer
layer_flat, num_features = flatten_layer(layer_conv2)
print("layer flat: {}".format(layer_flat))
print("num_features: {}".format(num_features))
# ### Fully-Connected Layer 1
layer_fc1 = fc_layer(input=layer_flat,
num_inputs=num_features,
num_outputs=fc_size,
use_relu=True)
print("layer fc1: {}".format(layer_fc1))
# x_mu = mlp_neuron(h2, w_mu, b_mu, activation=False)
x_mu = tf.nn.sigmoid(
fc_layer(input=layer_fc1,
num_inputs=fc_size,
num_outputs=input_dim,
use_relu=False))
tf.summary.image('x_mu', tf.reshape(x_mu[0], [1, 28, 28, 1]))
return x_mu
def qy_given_z1(z1,
num_classes,
filter_sizes,
num_channels,
num_filters,
fc_size,
reuse=False):
with tf.variable_scope("y_classifier", reuse=reuse):
print("filter_sizes:{}".format(filter_sizes))
expanded_z1 = tf.expand_dims(tf.expand_dims(z1, 1), 1)
layer_conv1, weights_conv1 = conv_layer(
input=expanded_z1,
num_input_channels=num_channels,
filter_size=filter_sizes[0],
num_filters=num_filters[0],
use_pooling=True,
layer_name='layer1')
print("layer conv1: {}".format(layer_conv1))
# ### Convolutional Layer 2
layer_conv2, weights_conv2 = conv_layer(
input=layer_conv1,
num_input_channels=num_filters[0],
filter_size=filter_sizes[1],
num_filters=num_filters[1],
use_pooling=True,
layer_name='layer2')
print("layer conv2: {}".format(layer_conv2))
# ### Flatten Layer
layer_flat, num_features = flatten_layer(layer_conv2)
print("layer flat: {}".format(layer_flat))
print("num_features: {}".format(num_features))
# ### Fully-Connected Layer 1
layer_fc1 = fc_layer(input=layer_flat,
num_inputs=num_features,
num_outputs=fc_size,
use_relu=True)
print("layer fc1: {}".format(layer_fc1))
logits = fc_layer(input=layer_fc1,
num_inputs=fc_size,
num_outputs=num_classes,
use_relu=False)
return logits