alexbrillant

time_steps = 50
input_dim = 1
output_dim = 1

dataset = tf.data.Dataset.from_generator(
     generator=generator,
     output_types=(tf.float32, tf.float32),
     output_shapes=(tf.TensorShape([None, time_steps, input_dim]), tf.TensorShape([None, time_steps, output_dim]))
)

alexbrillant

model.compile(
  optimizer=Adam(),
  loss='mean_squared_error'
)

epochs = 25
log_dir='logs'

model.fit(
  train_data, # train dataset that yields (x, y) tuples

alexbrillant

def generator():
  for factor in np.arange(0.5, 1, 0.01):
    time = np.arange(0, 50, 0.2)
    sin: np.ndarray = np.sin(factor * time) + np.random.normal(scale=0.25, size=len(time))
    yield sin[:25], sin[25:]
    
time_steps = 25
input_dim = 1
output_dim = 1