tensorflow_probability/examples/disentangled_vae.py
def zero_state(self, sample_batch_shape=()):
    """Returns an initial state for the LSTM cell.

    Args:
      sample_batch_shape: A 0D or 1D tensor of the combined sample and
        batch shape.

    Returns:
      A tuple of the initial previous output at timestep 0 of shape
      [sample_batch_shape, dimensions], and the cell state.
    """
    h0 = tf.zeros([1, self.hidden_size])
    c0 = tf.zeros([1, self.hidden_size])
    combined_shape = tf.concat((tf.convert_to_tensor(
        value=sample_batch_shape, dtype=tf.int32), [self.dimensions]),
                               axis=-1)
    previous_output = tf.zeros(combined_shape)
    return previous_output, (h0, c0)
def zero_state(self, sample_batch_shape=()):
    h0 = tf.zeros([1, self.hidden_size])
    c0 = tf.zeros([1, self.hidden_size])
    combined_shape = tf.concat((tf.convert_to_tensor(
        value=sample_batch_shape, dtype=tf.int32), [self.dimensions]),
                               axis=-1)
    previous_output = tf.zeros(combined_shape)
    return previous_output, (h0, c0)
def zero_state ( self , sample_batch_shape = ( ) ) : h0 = tf . zeros ( [ 1 , self . hidden_size ] ) c0 = tf . zeros ( [ 1 , self . hidden_size ] ) combined_shape = tf . concat ( ( tf . convert_to_tensor ( value = sample_batch_shape , dtype = tf . int32 ) , [ self . dimensions ] ) , axis = - 1 ) previous_output = tf . zeros ( combined_shape ) return previous_output , ( h0 , c0 )
Returns an initial state for the LSTM cell.

    Args:
      sample_batch_shape: A 0D or 1D tensor of the combined sample and
        batch shape.

    Returns:
      A tuple of the initial previous output at timestep 0 of shape
      [sample_batch_shape, dimensions], and the cell state.
Returns an initial state for the LSTM cell .