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This commit is contained in:
Francois Chollet 2016-11-07 15:36:57 -08:00
parent 02fe371839
commit c95c32e473
1 changed files with 27 additions and 26 deletions
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53
keras/engine/topology.py
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@ -95,33 +95,34 @@ class Node(object):
input_tensors, output_tensors,
input_masks, output_masks,
input_shapes, output_shapes):
# layer instance (NOT a list).
# Layer instance (NOT a list).
# this is the layer that takes a list of input tensors
# and turns them into a list of output tensors.
# the current node will be added to the inbound_nodes of outbound_layer
# the current node will be added to the inbound_nodes of outbound_layer.
self.outbound_layer = outbound_layer
# the following 3 properties describe where
# The following 3 properties describe where
# the input tensors come from: which layers,
# and for each layer, which node and which
# tensor output of each node.
self.inbound_layers = inbound_layers # list of layer instances
self.node_indices = node_indices # list of integers, 1:1 mapping with inbound_layers
self.tensor_indices = tensor_indices # list of integers, 1:1 mapping with inbound_layers
# tensor inputs and outputs of outbound_layer
self.input_tensors = input_tensors # list of tensors. 1:1 mapping with inbound_layers
self.output_tensors = output_tensors # list of tensors, created by outbound_layer.call()
self.inbound_layers = inbound_layers # List of layer instances
self.node_indices = node_indices # List of integers, 1:1 mapping with inbound_layers.
self.tensor_indices = tensor_indices # List of integers, 1:1 mapping with inbound_layers.
# Tensor inputs and outputs of outbound_layer.
self.input_tensors = input_tensors # List of tensors. 1:1 mapping with inbound_layers.
self.output_tensors = output_tensors # List of tensors, created by outbound_layer.call().
# input and output masks
self.input_masks = input_masks # list of tensors, 1:1 mapping with input_tensor
self.output_masks = output_masks # list of tensors, created by outbound_layer.compute_mask()
self.input_masks = input_masks # List of tensors, 1:1 mapping with input_tensor.
self.output_masks = output_masks # List of tensors, created by outbound_layer.compute_mask().
# input and output shapes
self.input_shapes = input_shapes # list of shape tuples, shapes of input_tensors
self.output_shapes = output_shapes # list of shape tuples, shapes of output_tensors
self.input_shapes = input_shapes # List of shape tuples, shapes of input_tensors.
self.output_shapes = output_shapes # List of shape tuples, shapes of output_tensors.
# add nodes to all layers involved.
# Add nodes to all layers involved.
for layer in inbound_layers:
if layer is not None:
layer.outbound_nodes.append(self)
@ -152,7 +153,7 @@ class Node(object):
if len(input_tensors) == 1:
output_tensors = to_list(outbound_layer.call(input_tensors[0], mask=input_masks[0]))
output_masks = to_list(outbound_layer.compute_mask(input_tensors[0], input_masks[0]))
# TODO: try to auto-infer shape if exception is raised by get_output_shape_for
# TODO: try to auto-infer shape if exception is raised by get_output_shape_for.
output_shapes = to_list(outbound_layer.get_output_shape_for(input_shapes[0]))
else:
output_tensors = to_list(outbound_layer.call(input_tensors, mask=input_masks))
@ -270,7 +271,7 @@ class Layer(object):
assert_input_compatibility()
'''
def __init__(self, **kwargs):
# these properties should have been set
# These properties should have been set
# by the child class, as appropriate.
if not hasattr(self, 'input_spec'):
self.input_spec = None
@ -279,12 +280,12 @@ class Layer(object):
if not hasattr(self, 'uses_learning_phase'):
self.uses_learning_phase = False
# these lists will be filled via successive calls
# to self.add_inbound_node()
# These lists will be filled via successive calls
# to self.add_inbound_node().
self.inbound_nodes = []
self.outbound_nodes = []
# these properties will be set upon call of self.build(),
# These properties will be set upon call of self.build(),
# which itself will be called upon self.add_inbound_node if necessary.
if not hasattr(self, 'trainable_weights'):
self.trainable_weights = []
@ -296,7 +297,7 @@ class Layer(object):
self.constraints = {} # dict {tensor: constraint instance}
self.built = False
# these properties should be set by the user via keyword arguments.
# These properties should be set by the user via keyword arguments.
# note that 'input_dtype', 'input_shape' and 'batch_input_shape'
# are only applicable to input layers: do not pass these keywords
# to non-input layers.
@ -317,7 +318,7 @@ class Layer(object):
self.trainable = kwargs.get('trainable', True)
if 'batch_input_shape' in kwargs or 'input_shape' in kwargs:
# in this case we will create an input layer
# In this case we will create an input layer
# to insert before the current layer
if 'batch_input_shape' in kwargs:
batch_input_shape = tuple(kwargs['batch_input_shape'])
@ -364,10 +365,10 @@ class Layer(object):
self.batch_input_shape = batch_input_shape
self.input_dtype = input_dtype
# instantiate the input layer
# Instantiate the input layer.
x = Input(batch_shape=batch_input_shape,
dtype=input_dtype, name=name)
# this will build the current layer
# This will build the current layer
# and create the node connecting the current layer
# to the input layer we just created.
self(x)
@ -394,7 +395,7 @@ class Layer(object):
if spec is None:
continue
# check ndim
# Check ndim.
if spec.ndim is not None:
if type(spec.ndim) is str:
int_ndim = spec.ndim[:spec.ndim.find('+')]
@ -423,7 +424,7 @@ class Layer(object):
if hasattr(x, '_keras_shape'):
x_shape = x._keras_shape
elif hasattr(K, 'int_shape'):
# tensorflow shape inference
# Tensorflow shape inference.
x_shape = K.int_shape(x)
else:
continue
@ -502,7 +503,7 @@ class Layer(object):
tensor_indices = []
for input_tensor in input_tensors:
if hasattr(input_tensor, '_keras_history') and input_tensor._keras_history:
# this is a Keras tensor
# This is a Keras tensor.
previous_layer, node_index, tensor_index = input_tensor._keras_history
inbound_layers.append(previous_layer)
node_indices.append(node_index)