def ResBlock(name, inputs):
output = inputs
output = tf.nn.relu(output)
output = cv.Conv1D(name + '.1', DIM, DIM, 5, output)
output = tf.nn.relu(output)
output = cv.Conv1D(name + '.2', DIM, DIM, 5, output)
return inputs + (0.3 * output)
def Discriminator(inputs):
output = tf.transpose(inputs, [0, 2, 1])
output = cv.Conv1D('Discriminator.Input', len(charmap), DIM, 1, output)
output = ResBlock('Discriminator.1', output)
output = ResBlock('Discriminator.2', output)
output = ResBlock('Discriminator.3', output)
output = ResBlock('Discriminator.4', output)
output = ResBlock('Discriminator.5', output)
output = tf.reshape(output, [-1, SEQ_LEN * DIM])
output = li.Linear('Discriminator.Output', SEQ_LEN * DIM, 1, output)
return output
def Discriminator(inputs, y, isSupervised=True):
output = tf.transpose(inputs, [0, 2, 1])
output = cv.Conv1D('Discriminator.Input', len(charmap), DIM, 1, output)
output = ResBlock('Discriminator.1', output)
output = ResBlock('Discriminator.2', output)
output = ResBlock('Discriminator.3', output)
output = ResBlock('Discriminator.4', output)
output = ResBlock('Discriminator.5', output)
output = tf.reshape(output, [-1, SEQ_LEN * DIM])
output = tf.concat([output, y], 1)
output = li.Linear('Discriminator.Output', SEQ_LEN * DIM + 10, 1, output)
return output
def Generator(z, y, isSupervised=True):
output = tf.concat([z, y], 1)
output = li.Linear('Generator.Input', 64 + 10, SEQ_LEN * DIM, output)
output = tf.reshape(output, [-1, DIM, SEQ_LEN])
output = ResBlock('Generator.1', output)
output = ResBlock('Generator.2', output)
output = ResBlock('Generator.3', output)
output = ResBlock('Generator.4', output)
output = ResBlock('Generator.5', output)
output = cv.Conv1D('Generator.Output', DIM, len(charmap), 1, output)
output = tf.transpose(output, [0, 2, 1])
output = softmax(output)
# print(output.shape)
return output
def Generator(n_samples):
output = tf.random_normal(shape=[n_samples, 64], dtype=tf.float32)
output = li.Linear('Generator.Input', 64, SEQ_LEN * DIM, output)
output = tf.reshape(output, [-1, DIM, SEQ_LEN])
output = ResBlock('Generator.1', output)
output = ResBlock('Generator.2', output)
output = ResBlock('Generator.3', output)
output = ResBlock('Generator.4', output)
output = ResBlock('Generator.5', output)
output = cv.Conv1D('Generator.Output', DIM, len(charmap), 1, output)
output = tf.transpose(output, [0, 2, 1])
output = softmax(output)
# print(output.shape)
return output