def FCDiscriminator_sampler(self, inputs, FC_DIM=512, n_layers=3):
with tf.variable_scope('Discriminator') as scope:
scope.reuse_variables()
output = tf.reshape(inputs, [-1, self.num_neurons, self.num_bins])
conv1d_II.set_weights_stdev(0.02)
output, filters = conv1d_II.Conv1D('Input',
self.num_neurons,
self.num_features,
self.kernel_width,
output,
stride=1,
save_filter=True)
output = tf.reshape(output,
[-1, self.num_features * self.num_bins])
outputs_mat = [output]
output = act_funct.LeakyReLULayer(
'0', self.num_features * self.num_bins, FC_DIM, output)
outputs_mat.append(output)
for i in range(n_layers - 1):
output = act_funct.LeakyReLULayer('{}'.format(i + 1), FC_DIM,
FC_DIM, output)
outputs_mat.append(output)
output = linear.Linear('Out', FC_DIM, 1, output)
conv1d_II.unset_weights_stdev()
return tf.reshape(output, [-1]), [filters], outputs_mat
def FCDiscriminator(self, inputs, n_layers=3):
output = act_funct.LeakyReLULayer('Discriminator.Input',
self.output_dim, self.num_units,
inputs)
for i in range(n_layers):
output = act_funct.LeakyReLULayer('Discriminator.{}'.format(i),
self.num_units, self.num_units,
output)
output = linear.Linear('Discriminator.Out', self.num_units, 1, output)
return tf.reshape(output, [-1])
def FCDiscriminator_sampler(self, inputs, n_layers=3):
output = act_funct.LeakyReLULayer('Discriminator.Input',
self.output_dim, self.num_units,
inputs)
outputs_mat = [output]
for i in range(n_layers):
output = act_funct.LeakyReLULayer('Discriminator.{}'.format(i),
self.num_units, self.num_units,
output)
outputs_mat.append(output)
output = linear.Linear('Discriminator.Out', self.num_units, 1, output)
filters = []
return tf.reshape(output, [-1]), filters, outputs_mat
def FCDiscriminator(self, inputs, print_arch=True):
output = act_funct.LeakyReLULayer('Discriminator.Input',
self.output_dim, self.num_units,
inputs)
if print_arch:
print('DISCRIMINATOR. -------------------------------')
print(str(output.get_shape()) + ' input')
for i in range(self.num_layers):
output = act_funct.LeakyReLULayer('Discriminator.{}'.format(i),
self.num_units, self.num_units,
output)
if print_arch:
print(str(output.get_shape()) + ' layer ' + str(i))
output = linear.Linear('Discriminator.Out', self.num_units, 1, output)
if print_arch:
print(str(output.get_shape()) + ' output')
return tf.reshape(output, [-1])
def FCDiscriminator(self, inputs, FC_DIM=512, n_layers=3):
output = tf.reshape(inputs, [-1, self.num_neurons, self.num_bins])
conv1d_II.set_weights_stdev(0.02)
output = conv1d_II.Conv1D('Discriminator.Input',
self.num_neurons,
self.num_features,
self.kernel_width,
output,
stride=1)
output = tf.reshape(output, [-1, self.num_features * self.num_bins])
output = act_funct.LeakyReLULayer('Discriminator.0',
self.num_features * self.num_bins,
FC_DIM, output)
for i in range(n_layers - 1):
output = act_funct.LeakyReLULayer('Discriminator.{}'.format(i + 1),
FC_DIM, FC_DIM, output)
output = linear.Linear('Discriminator.Out', FC_DIM, 1, output)
conv1d_II.unset_weights_stdev()
return tf.reshape(output, [-1])
def FCGenerator(self, n_samples, noise=None, print_arch=True):
if noise is None:
noise = tf.random_normal([n_samples, 128])
if print_arch:
print('GENERATOR. -------------------------------')
print(str(noise.get_shape()) + ' latent variable')
output = act_funct.ReLULayer('Generator.Input', 128, self.num_units,
noise)
if print_arch:
print(str(output.get_shape()) + ' linear projection')
for i in range(self.num_layers):
output = act_funct.LeakyReLULayer('Generator.{}'.format(i),
self.num_units, self.num_units,
output)
if print_arch:
print(str(output.get_shape()) + ' layer ' + str(i))
output = linear.Linear('Generator.Out', self.num_units,
self.output_dim, output)
if print_arch:
print(str(output.get_shape()) + ' output')
output = tf.nn.sigmoid(output)
return output