def __init__(self,
input_layer,
hidden_size=1,
dropout_rate=0.,
reverse_sequence=False,
activation='relu',
name=None):
if type(input_layer) is PythonWrapper.IndRnn:
super().__init__(input_layer)
return
layers, outputs = check_input_layers(input_layer, 1)
if hidden_size <= 0:
raise ValueError('The `hidden_size` must be > 0.')
if dropout_rate >= 1.:
raise ValueError('The `dropout_rate` must be < 1.')
if activation != 'sigmoid' and activation != 'relu':
raise ValueError(
'The `activation` must be one of {`sigmoid`, `relu`}')
internal = PythonWrapper.IndRnn(str(name), layers, outputs,
int(hidden_size), float(dropout_rate),
bool(reverse_sequence),
str(activation))
super().__init__(internal)
def __init__(self, input_layer, hidden_size=1, dropout_rate=0., reverse_sequence=False, name=None):
if type(input_layer) is PythonWrapper.IndRnn:
super().__init__(input_layer)
return
layers, outputs = check_input_layers(input_layer, 1)
if hidden_size <= 0:
raise ValueError('The `hidden_size` must be > 0.')
if dropout_rate >= 1.:
raise ValueError('The `dropout_rate` must be < 1.')
internal = PythonWrapper.IndRnn(str(name), layers, outputs, int(hidden_size), float(dropout_rate),
bool(reverse_sequence))
super().__init__(internal)
