def _():
base_model_fn = teacher_force_language_modeling(
lambda: snt.GRU(256), embed_dim=64)
dataset = lm1b_byte(128, 128)
return base.DatasetModelTask(base_model_fn, dataset)
def _():
base_model_fn = rnn_classification(
lambda: snt.GRU(64), embed_dim=64, aggregate_method="avg")
dataset = imdb_subword(128, 32)
return base.DatasetModelTask(base_model_fn, dataset)
presence
]
return output, state
if __name__ == '__main__':
learning_rate = 1e-4
batch_size = 10
img_size = 50, 50
crop_size = 20, 20
n_latent = 10
n_steps = 3
x = tf.placeholder(tf.float32, (batch_size, ) + img_size, name='inpt')
transition = snt.GRU(n_latent)
air = AIRCell(img_size, crop_size, n_latent, transition)
initial_state = air.initial_state(x)
dummy_sequence = tf.zeros((n_steps, batch_size, 1), name='dummy_sequence')
outputs, state = tf.nn.dynamic_rnn(air,
dummy_sequence,
initial_state=initial_state,
time_major=True)
canvas, crop, what, what_loc, what_scale, where, where_loc, where_scale, presence_prob, presence = outputs
canvas = tf.reshape(canvas, (
n_steps,
batch_size,
) + tuple(img_size))
final_canvas = canvas[-1]
