deeppavlov/core/layers/keras_layers.py
def expand_tile(units, axis):
    """
    Expand and tile tensor along given axis

    Args:
        units: tf tensor with dimensions [batch_size, time_steps, n_input_features]
        axis: axis along which expand and tile. Must be 1 or 2

    """
    assert axis in (1, 2)
    n_time_steps = K.int_shape(units)[1]
    repetitions = [1, 1, 1, 1]
    repetitions[axis] = n_time_steps
    if axis == 1:
        expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units)
    else:
        expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units)
    return K.tile(expanded, repetitions)
def expand_tile(units, axis):
    """
    Expand and tile tensor along given axis

    Args:
        units: tf tensor with dimensions [batch_size, time_steps, n_input_features]
        axis: axis along which expand and tile. Must be 1 or 2

    """
    assert axis in (1, 2)
    n_time_steps = K.int_shape(units)[1]
    repetitions = [1, 1, 1, 1]
    repetitions[axis] = n_time_steps
    if axis == 1:
        expanded = Reshape(target_shape=( (1,) + K.int_shape(units)[1:] ))(units)
    else:
        expanded = Reshape(target_shape=(K.int_shape(units)[1:2] + (1,) + K.int_shape(units)[2:]))(units)
    return K.tile(expanded, repetitions)
def expand_tile ( units , axis ) : assert axis in ( 1 , 2 ) n_time_steps = K . int_shape ( units ) [ 1 ] repetitions = [ 1 , 1 , 1 , 1 ] repetitions [ axis ] = n_time_steps if axis == 1 : expanded = Reshape ( target_shape = ( ( 1 , ) + K . int_shape ( units ) [ 1 : ] ) ) ( units ) else : expanded = Reshape ( target_shape = ( K . int_shape ( units ) [ 1 : 2 ] + ( 1 , ) + K . int_shape ( units ) [ 2 : ] ) ) ( units ) return K . tile ( expanded , repetitions )
Expand and tile tensor along given axis

    Args:
        units: tf tensor with dimensions [batch_size, time_steps, n_input_features]
        axis: axis along which expand and tile. Must be 1 or 2
Expand and tile tensor along given axis