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 DCGANDiscriminator_sampler(self, inputs):
kernel_width = self.width_kernel # in the time dimension
num_features = self.num_features
#neurons are treated as different channels
output = tf.reshape(inputs, [-1, self.num_neurons, self.num_bins])
#initialize weights
conv1d_II.set_weights_stdev(0.02)
deconv1d_II.set_weights_stdev(0.02)
linear.set_weights_stdev(0.02)
out_puts_mat = []
filters_mat = []
for ind_l in range(self.num_layers):
if ind_l == 0:
output, filters = conv1d_II.Conv1D(
'Discriminator.' + str(ind_l + 1),
self.num_neurons,
num_features * 2**(ind_l + 1),
kernel_width,
output,
stride=self.stride,
save_filter=True)
else:
output, filters = conv1d_II.Conv1D(
'Discriminator.' + str(ind_l + 1),
num_features * 2**(ind_l),
num_features * 2**(ind_l + 1),
kernel_width,
output,
stride=self.stride,
save_filter=True)
output = act_funct.LeakyReLU(output)
out_puts_mat.append(output)
filters_mat.append(filters)
output = tf.reshape(output, [-1, int(num_features * self.num_bins)])
output = linear.Linear('Discriminator.Output',
int(num_features * self.num_bins), 1, output)
#unset weights
conv1d_II.unset_weights_stdev()
deconv1d_II.unset_weights_stdev()
linear.unset_weights_stdev()
return tf.reshape(output, [-1]), filters_mat, out_puts_mat
def DCGANDiscriminator(self, inputs, print_arch=False):
kernel_width = self.width_kernel # in the time dimension
num_features = self.num_features
#neurons are treated as different channels
output = tf.reshape(inputs, [-1, self.num_neurons, self.num_bins])
conv1d_II.set_weights_stdev(0.02)
deconv1d_II.set_weights_stdev(0.02)
linear.set_weights_stdev(0.02)
if print_arch:
print('DISCRIMINATOR. -------------------------------')
print(str(output.get_shape()) + ' input')
for ind_l in range(self.num_layers):
if ind_l == 0:
output = conv1d_II.Conv1D('Discriminator.' + str(ind_l + 1),
self.num_neurons,
int(num_features * 2**(ind_l + 1)),
int(kernel_width),
output,
stride=self.stride)
else:
output = conv1d_II.Conv1D('Discriminator.' + str(ind_l + 1),
int(num_features * 2**(ind_l)),
int(num_features * 2**(ind_l + 1)),
int(kernel_width),
output,
stride=self.stride)
output = act_funct.LeakyReLU(output)
if print_arch:
print(str(output.get_shape()) + ' layer ' + str(ind_l + 1))
output = tf.reshape(output, [-1, int(num_features * self.num_bins)])
if print_arch:
print(str(output.get_shape()) + ' fully connected layer')
output = linear.Linear('Discriminator.Output',
int(num_features * self.num_bins), 1, output)
if print_arch:
print(str(output.get_shape()) + ' output')
conv1d_II.unset_weights_stdev()
deconv1d_II.unset_weights_stdev()
linear.unset_weights_stdev()
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])