words_in_dir.add(word_dir.parts[-1])
index_by_word = {}
with open(str(dictionary_path)) as f:
for i, word in enumerate(f.readlines()):
stripped = word.strip()
if (stripped in words_in_dir):
index_by_word[stripped] = i
num_words = len(index_by_word)
model_builder = ModelBuilder(num_frames, frame_shape, num_words, noise_dim,
learning_rate)
generator = model_builder.build_generator()
discriminator = model_builder.build_discriminator()
cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def discriminator_loss(real_output, fake_output):
real_loss = cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
total_loss = real_loss + fake_loss
return total_loss
def generator_loss(fake_output):
return cross_entropy(tf.ones_like(fake_output), fake_output)
