def __init__(self, name=None, **kwargs):
if not name:
prefix = 'optional_input_placeholder'
name = prefix + '_' + str(K.get_uid(prefix))
kwargs['batch_input_shape'] = (2,)
super(_OptionalInputPlaceHolder, self).__init__(**kwargs)
self.tensor = K.zeros(shape=(2,))
self.tensor._keras_shape = (2,)
self.tensor._uses_learning_phase = False
self.tensor._keras_history = (self, 0, 0)
Node(self,
inbound_layers=[],
node_indices=[],
tensor_indices=[],
input_tensors=[],
output_tensors=[self.tensor],
input_masks=[None],
output_masks=[None],
input_shapes=[],
output_shapes=[(2,)])
self.build((2,))
def __init__(self,
input_shape=None,
batch_size=None,
batch_input_shape=None,
dtype=None,
input_tensor=None,
sparse=False,
name=None):
if not name:
prefix = 'input'
name = prefix + '_' + str(K.get_uid(prefix))
super(InputLayer, self).__init__(dtype=dtype, name=name)
self.trainable = False
self.built = True
self.sparse = sparse
if input_shape and batch_input_shape:
raise ValueError('Only provide the input_shape OR '
'batch_input_shape argument to '
'InputLayer, not both at the same time.')
if input_tensor is not None and batch_input_shape is None:
# If input_tensor is set, and batch_input_shape is not set:
# Attempt automatic input shape inference.
try:
batch_input_shape = K.int_shape(input_tensor)
except TypeError:
if not input_shape and not batch_input_shape:
raise ValueError('InputLayer was provided '
'an input_tensor argument, '
'but its input shape cannot be '
'automatically inferred. '
'You should pass an input_shape or '
'batch_input_shape argument.')
if not batch_input_shape:
if not input_shape:
raise ValueError('An Input layer should be passed either '
'a `batch_input_shape` or an `input_shape`.')
else:
batch_input_shape = (batch_size, ) + tuple(input_shape)
else:
batch_input_shape = tuple(batch_input_shape)
if not dtype:
if input_tensor is None:
dtype = K.floatx()
else:
dtype = K.dtype(input_tensor)
self.batch_input_shape = batch_input_shape
self.dtype = dtype
if input_tensor is None:
self.is_placeholder = True
input_tensor = K.placeholder(shape=batch_input_shape,
dtype=dtype,
sparse=self.sparse,
name=self.name)
else:
self.is_placeholder = False
input_tensor._keras_shape = batch_input_shape
# Create an input node to add to self.outbound_node
# and set output_tensors' _keras_history.
input_tensor._uses_learning_phase = False
input_tensor._keras_history = (self, 0, 0)
Node(self,
inbound_layers=[],
node_indices=[],
tensor_indices=[],
input_tensors=[input_tensor],
output_tensors=[input_tensor])