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GFL_model.py
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GFL_model.py
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from ops import *
class Model:
def __init__(self, batch_size, slope, num_pieces, lr, z_dim, raw_marginal):
self.batch_size = batch_size
self.slope = slope
self.num_pieces = num_pieces
self.lr = lr
self.z_dim = z_dim
self.raw_marginal = raw_marginal
self.create_model()
def create_model(self):
self.input_x = tf.placeholder(tf.float32, shape=[None, 17, 100, 1])
self.input_z = tf.placeholder(tf.float32, shape=[None, 1, 1, self.z_dim])
self.train_g = tf.placeholder(tf.bool, [])
self.train_d = tf.placeholder(tf.bool, [])
self.keep_prob = tf.placeholder(tf.float32)
self.G_x = self.decoder(self.input_z, train=self.train_g, reuse=False)
self.G_z = self.encoder(self.input_x, train=self.train_g, reuse=False)
self.resampler = self.decoder(self.encoder(self.input_x, train=False, reuse=True), train=False, reuse=True)
self.D_G_x, self.D_G_x_logits = self.discriminator(self.G_x, self.input_z, self.keep_prob, train=self.train_d, reuse=False)
self.D_G_z, self.D_G_z_logits = self.discriminator(self.input_x, self.G_z, self.keep_prob, train=self.train_d, reuse=True)
self.d_loss = tf.reduce_mean(tf.nn.softplus(-self.D_G_z_logits) + tf.nn.softplus(self.D_G_x_logits))
self.g_loss = tf.reduce_mean(tf.nn.softplus(self.D_G_z_logits) + tf.nn.softplus(-self.D_G_x_logits))
self.d_vars = [var for var in tf.trainable_variables() if 'discriminator' in var.name]
self.gx_vars = [var for var in tf.trainable_variables() if 'decoder' in var.name]
self.gz_vars = [var for var in tf.trainable_variables() if 'encoder' in var.name]
self.d_optim = tf.train.GradientDescentOptimizer(self.lr).minimize(self.d_loss, var_list=self.d_vars)
self.g_optim = tf.train.GradientDescentOptimizer(self.lr).minimize(self.g_loss, var_list=self.gx_vars+self.gz_vars)
self.optims = [self.d_optim, self.g_optim]
return self.optims
def decoder(self, input_, train=True, reuse=False):
with tf.variable_scope('decoder', reuse=reuse) as scope:
h = input_
# input, kernel_shape, c_d_shape, deconv_shape, step_size)
with tf.variable_scope('layer1'):
h = deconv(h, [2, 1, 256, 64], [self.batch_size, 1, 1, 64], [self.batch_size, 2, 1, 256], [1, 1, 1, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer2'):
h = deconv(h, [3, 1, 128, 256], [self.batch_size, 2, 1, 256], [self.batch_size, 5, 1, 128], [1, 2, 2, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer3'):
h = deconv(h, [3, 1, 64, 128], [self.batch_size, 5, 1, 128], [self.batch_size, 7, 1, 64], [1, 1, 1, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer4'):
h = deconv(h, [3, 1, 32, 64], [self.batch_size, 7, 1, 64], [self.batch_size, 15, 1, 32], [1, 2, 2, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer5'):
h = deconv(h, [3, 100, 32, 32], [self.batch_size, 15, 1, 32], [self.batch_size, 17, 100, 32], [1, 1, 1, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer6'):
h = conv2d(h, [1, 1, 32, 32], [1, 1, 1, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer7'):
marginal = cal_marginal(self.raw_marginal)
h = conv2d(h, [1, 1, 32, 1], [1, 1, 1, 1])
h = add_nontied_bias(h, initializer=tf.constant_initializer(marginal))
h = tf.nn.sigmoid(h)
output = h
return output
def encoder(self, input_, train=True, reuse=False):
input = input_
with tf.variable_scope('encoder', reuse=reuse) as scope:
h = input
with tf.variable_scope('layer1'): # 15
h = conv2d(h, [3, 100, 1, 32], [1, 1, 1, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer2'): # 7
h = conv2d(h, [3, 1, 32, 64], [1, 2, 2, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer3'): # 5
h = conv2d(h, [3, 1, 64, 128], [1, 1, 1, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer4'): # 2
h = conv2d(h, [3, 1, 128, 256], [1, 2, 2, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer5'): # 1
h = conv2d(h, [2, 1, 256, 512], [1, 1, 1, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer6'):
h = conv2d(h, [1, 1, 512, 512], [1, 1, 1, 1])
h = batch_norm(h, training=train)
h = lrelu(h, slope=self.slope)
with tf.variable_scope('layer7_mu'):
h_mu = conv2d(h, [1, 1, 512, 64], [1, 1, 1, 1])
h_mu = add_nontied_bias(h_mu)
G_z_mu = h_mu
with tf.variable_scope('layer7_sigma'):
h_sigma = conv2d(h, [1, 1, 512, 64], [1, 1, 1, 1])
h_sigma = add_nontied_bias(h_sigma)
G_z_sigma = h_sigma
h_sigma = tf.exp(h_sigma)
rng = tf.random_normal(shape=tf.shape(h_mu))
output = (rng * h_sigma) + h_mu
return output
def discriminator(self, input_x, input_z, keep_prob, train=True, reuse=False):
with tf.variable_scope('discriminator', reuse=reuse):
h_x = input_x
h_z = input_z
with tf.variable_scope('x'):
with tf.variable_scope('layer1'):
h_x = tf.nn.dropout(h_x, keep_prob)
h_x = conv2d(h_x, [3, 100, 1, 32], [1, 1, 1, 1])
h_x = conv_maxout(h_x, self.num_pieces)
with tf.variable_scope('layer2'):
h_x = tf.nn.dropout(h_x, keep_prob)
h_x = conv2d(h_x, [3, 1, 16, 64], [1, 2, 2, 1])
h_x = conv_maxout(h_x, self.num_pieces)
with tf.variable_scope('layer3'):
h_x = tf.nn.dropout(h_x, keep_prob)
h_x = conv2d(h_x, [3, 1, 32, 128], [1, 1, 1, 1])
h_x = conv_maxout(h_x, self.num_pieces)
with tf.variable_scope('layer4'):
h_x = tf.nn.dropout(h_x, keep_prob)
h_x = conv2d(h_x, [3, 1, 64, 256], [1, 2, 2, 1])
h_x = conv_maxout(h_x, self.num_pieces)
with tf.variable_scope('layer5'):
h_x = tf.nn.dropout(h_x, keep_prob)
h_x = conv2d(h_x, [2, 1, 128, 512], [1, 1, 1, 1])
# h_x = conv_maxout(h_x, self.num_pieces)
with tf.variable_scope('z'):
with tf.variable_scope('layer1'):
h_z = tf.nn.dropout(h_z, keep_prob)
h_z = conv2d(h_z, [1, 1, 64, 512], [1, 1, 1, 1])
h_z = conv_maxout(h_z, self.num_pieces)
with tf.variable_scope('layer2'):
h_z = tf.nn.dropout(h_z, keep_prob)
h_z = conv2d(h_z, [1, 1, 256, 512], [1, 1, 1, 1])
# h_z = conv_maxout(h_z, self.num_pieces)
with tf.variable_scope('xz'):
h_xz = tf.concat(3, [h_x, h_z]) # h_x.get_shape().ndims-1)
with tf.variable_scope('layer1'):
h_xz = tf.nn.dropout(h_xz, keep_prob)
h_xz = conv2d(h_xz, [1, 1, 1024, 1024], [1, 1, 1, 1])
h_xz = conv_maxout(h_xz, self.num_pieces)
with tf.variable_scope('layer2'):
h_xz = tf.nn.dropout(h_xz, keep_prob)
h_xz = conv2d(h_xz, [1, 1, 512, 1024], [1, 1, 1, 1])
h_xz = conv_maxout(h_xz, self.num_pieces)
with tf.variable_scope('layer3'):
h_xz = tf.nn.dropout(h_xz, keep_prob)
h_xz = conv2d(h_xz, [1, 1, 512, 1], [1, 1, 1, 1])
logits = h_xz
output = tf.nn.sigmoid(h_xz)
return output, logits