def build_decoder(latent_dim, timesteps):
decoder_inputs = Input((latent_dim, ))
decoded = decoder_inputs
decoded = Dense(15)(decoded)
decoded = utils.BatchNormalization()(decoded)
decoded = LeakyReLU(0.2)(decoded)
decoded = Lambda(lambda x: K.expand_dims(x))(decoded)
decoded = Conv1D(32, 3, padding='same')(decoded)
decoded = utils.BatchNormalization()(decoded)
decoded = LeakyReLU(0.2)(decoded)
decoded = UpSampling1D(2)(decoded)
decoded = Conv1D(32, 3, padding='same')(decoded)
decoded = utils.BatchNormalization()(decoded)
decoded = LeakyReLU(0.2)(decoded)
decoded = UpSampling1D(2)(decoded)
decoded = Conv1D(32, 3, padding='same')(decoded)
decoded = utils.BatchNormalization()(decoded)
decoded = LeakyReLU(0.2)(decoded)
decoded = UpSampling1D(2)(decoded)
decoded = Conv1D(1, 3, padding='same')(decoded)
decoded = utils.BatchNormalization()(decoded)
decoded = LeakyReLU(0.2)(decoded)
decoded = Lambda(lambda x: K.squeeze(x, -1))(decoded)
decoded = Dense(timesteps, activation='tanh')(decoded)
decoder = Model(decoder_inputs, decoded, 'decoder')
return decoder
def build_encoder(latent_dim, timesteps):
encoder_inputs = Input((timesteps, ))
encoded = Lambda(lambda x: K.expand_dims(x, -1))(encoder_inputs)
encoded = Conv1D(32, 3, padding='same')(encoded)
encoded = utils.BatchNormalization()(encoded)
encoded = LeakyReLU(0.2)(encoded)
encoded = MaxPooling1D(2, padding='same')(encoded)
encoded = Conv1D(32, 3, padding='same')(encoded)
encoded = utils.BatchNormalization()(encoded)
encoded = LeakyReLU(0.2)(encoded)
encoded = MaxPooling1D(2, padding='same')(encoded)
encoded = Conv1D(32, 3, padding='same')(encoded)
encoded = utils.BatchNormalization()(encoded)
encoded = LeakyReLU(0.2)(encoded)
encoded = MaxPooling1D(2, padding='same')(encoded)
encoded = Conv1D(32, 3, padding='same')(encoded)
encoded = utils.BatchNormalization()(encoded)
encoded = LeakyReLU(0.2)(encoded)
encoded = Flatten()(encoded)
encoded = Dense(128)(encoded)
encoded = utils.BatchNormalization()(encoded)
encoded = Activation('tanh')(encoded)
z_mean = Dense(latent_dim)(encoded)
z_log_var = Dense(latent_dim)(encoded)
encoder = Model(encoder_inputs, [z_mean, z_log_var])
return encoder
def build_generator(latent_dim, timesteps):
generator_inputs = Input((latent_dim, ))
generated = generator_inputs
generated = Dense(15)(generated)
generated = utils.BatchNormalization()(generated)
generated = LeakyReLU(0.2)(generated)
generated = Lambda(lambda x: K.expand_dims(x))(generated)
generated = Conv1D(32, 3, padding='same')(generated)
generated = utils.BatchNormalization()(generated)
generated = LeakyReLU(0.2)(generated)
generated = UpSampling1D(2)(generated)
generated = Conv1D(32, 3, padding='same')(generated)
generated = utils.BatchNormalization()(generated)
generated = LeakyReLU(0.2)(generated)
generated = UpSampling1D(2)(generated)
generated = Conv1D(32, 3, padding='same')(generated)
generated = utils.BatchNormalization()(generated)
generated = LeakyReLU(0.2)(generated)
generated = UpSampling1D(2)(generated)
generated = Conv1D(1, 3, padding='same')(generated)
generated = utils.BatchNormalization()(generated)
generated = LeakyReLU(0.2)(generated)
generated = Lambda(lambda x: K.squeeze(x, -1))(generated)
generated = Dense(timesteps, activation='tanh')(generated)
generator = Model(generator_inputs, generated, 'generator')
return generator
def __init__(self,
is_training_bn: bool,
act_type: Union[Text, None],
init_zero: bool = False,
data_format: Text = 'channels_last',
momentum: float = 0.99,
epsilon: float = 1e-3,
use_tpu: bool = False,
name: Text = None):
super(BatchNormAct, self).__init__()
self.act_type = act_type
self.training = is_training_bn
if init_zero:
self.gamma_initializer = tf.zeros_initializer()
else:
self.gamma_initializer = tf.ones_initializer()
if data_format == 'channels_first':
self.axis = 1
else:
self.axis = 3
if is_training_bn and use_tpu:
self.layer = utils.TpuBatchNormalization(
axis=self.axis,
momentum=momentum,
epsilon=epsilon,
center=True,
scale=True,
gamma_initializer=self.gamma_initializer,
name=f'{name}')
else:
self.layer = utils.BatchNormalization(
axis=self.axis,
momentum=momentum,
epsilon=epsilon,
center=True,
scale=True,
gamma_initializer=self.gamma_initializer,
name=f'{name}')
self.act = ActivationFn(act_type)