def make_conv1():
latent_dim = 16
x_shape = (128, 54)
huzz = HuzzerSource()
data_pipeline = OneHotVecotorizer(TokenDatasource(huzz), x_shape[1],
x_shape[0])
decoder_input = tf.placeholder(tf.float32,
shape=(1, latent_dim),
name='decoder_input')
with conv_arg_scope():
encoder_input = tf.placeholder(tf.float32,
shape=(1, *x_shape),
name='encoder_input')
encoder_output, _ = build_conv1_encoder(encoder_input, latent_dim)
decoder_output = build_conv1_decoder(decoder_input, x_shape)
decoder_output = tf.reshape(decoder_output, x_shape)
return data_pipeline, encoder_input, encoder_output, decoder_input, decoder_output
def make_special_conv():
latent_dim = 64
x_shape = (128, 54)
huzz = HuzzerSource()
data_pipeline = OneHotVecotorizer(TokenDatasource(huzz), x_shape[1],
x_shape[0])
decoder_input = tf.placeholder(tf.float32,
shape=(1, latent_dim),
name='decoder_input')
encoder_input = tf.placeholder(tf.float32,
shape=(1, *x_shape),
name='encoder_input')
with conv_arg_scope():
encoder_output, _ = build_special_conv_encoder(encoder_input,
latent_dim)
decoder_output = build_special_conv_decoder(decoder_input, x_shape)
decoder_output = tf.nn.softmax(decoder_output, dim=-1)
decoder_output = tf.squeeze(decoder_output, 0)
return data_pipeline, encoder_input, encoder_output, decoder_input, decoder_output