def __init__(self,
input_shape=None,
batch_input_shape=None,
input_dtype=None,
name=None):
self.uses_learning_phase = False
self.trainable = False
if not name:
prefix = 'input'
# TODO(rbharath): Keras uses a global var here to maintain
# unique counts. This seems dangerous. How does tensorflow handle?
name = prefix + '_' + str(model_ops.get_uid(prefix))
self.name = name
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 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 = (None,) + tuple(input_shape)
else:
batch_input_shape = tuple(batch_input_shape)
if not input_dtype:
input_dtype = tf.float32
self.batch_input_shape = batch_input_shape
self.input_dtype = input_dtype
def __init__(self, **kwargs):
# These properties should have been set
# by the child class, as appropriate.
if not hasattr(self, 'input_spec'):
self.input_spec = None
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().
self.inbound_nodes = []
self.outbound_nodes = []
# 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 = []
if not hasattr(self, '_non_trainable_weights'):
self._non_trainable_weights = []
if not hasattr(self, 'losses'):
self.losses = []
if not hasattr(self, 'constraints'):
self.constraints = {} # dict {tensor: constraint instance}
self.built = False
# 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.
allowed_kwargs = {'input_shape',
'batch_input_shape',
'input_dtype',
'name',
'trainable'}
for kwarg in kwargs.keys():
if kwarg not in allowed_kwargs:
raise TypeError('Keyword argument not understood:', kwarg)
name = kwargs.get('name')
if not name:
prefix = self.__class__.__name__.lower()
name = prefix + '_' + str(model_ops.get_uid(prefix))
self.name = name
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
# to insert before the current layer
if 'batch_input_shape' in kwargs:
batch_input_shape = tuple(kwargs['batch_input_shape'])
elif 'input_shape' in kwargs:
batch_input_shape = (None,) + tuple(kwargs['input_shape'])
self.batch_input_shape = batch_input_shape
input_dtype = kwargs.get('input_dtype', tf.float32)
self.input_dtype = input_dtype
def __init__(self, **kwargs):
# These properties should have been set
# by the child class, as appropriate.
if not hasattr(self, 'input_spec'):
self.input_spec = None
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().
self.inbound_nodes = []
self.outbound_nodes = []
# 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 = []
if not hasattr(self, '_non_trainable_weights'):
self._non_trainable_weights = []
if not hasattr(self, 'losses'):
self.losses = []
if not hasattr(self, 'constraints'):
self.constraints = {} # dict {tensor: constraint instance}
self.built = False
# 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.
allowed_kwargs = {
'input_shape', 'batch_input_shape', 'input_dtype', 'name',
'trainable'
}
for kwarg in kwargs.keys():
if kwarg not in allowed_kwargs:
raise TypeError('Keyword argument not understood:', kwarg)
name = kwargs.get('name')
if not name:
prefix = self.__class__.__name__.lower()
name = prefix + '_' + str(model_ops.get_uid(prefix))
self.name = name
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
# to insert before the current layer
if 'batch_input_shape' in kwargs:
batch_input_shape = tuple(kwargs['batch_input_shape'])
elif 'input_shape' in kwargs:
batch_input_shape = (None, ) + tuple(kwargs['input_shape'])
self.batch_input_shape = batch_input_shape
input_dtype = kwargs.get('input_dtype', tf.float32)
self.input_dtype = input_dtype
def __init__(self, name=None, logdir=None):
super(Sequential, self).__init__(self, model_dir=logdir)
self.layers = [] # stack of layers
self.outputs = None # tensors (length 1)
if not name:
prefix = 'sequential_'
name = prefix + str(model_ops.get_uid(prefix))
self.name = name
self.graph = tf.Graph()
config = tf.ConfigProto(allow_soft_placement=True)
self.session = tf.Session(graph=self.graph, config=config)
# Path to save checkpoint files
self._save_path = os.path.join(self.model_dir, 'model.ckpt')
def __init__(self, name=None, logdir=None):
self.layers = [] # stack of layers
self.outputs = None # tensors (length 1)
if not name:
prefix = 'sequential_'
name = prefix + str(model_ops.get_uid(prefix))
self.name = name
self.graph = tf.Graph()
config = tf.ConfigProto(allow_soft_placement=True)
self.session = tf.Session(graph=self.graph, config=config)
# Path to save checkpoint files
if logdir is not None:
if not os.path.exists(logdir):
os.makedirs(logdir)
else:
logdir = tempfile.mkdtemp()
self.logdir = logdir
self._save_path = os.path.join(self.logdir, 'model.ckpt')
def __init__(self, name=None, logdir=None):
self.layers = [] # stack of layers
self.outputs = None # tensors (length 1)
if not name:
prefix = 'sequential_'
name = prefix + str(model_ops.get_uid(prefix))
self.name = name
self.graph = tf.Graph()
config = tf.ConfigProto(allow_soft_placement=True)
self.session = tf.Session(graph=self.graph, config=config)
# Path to save checkpoint files
if logdir is not None:
if not os.path.exists(logdir):
os.makedirs(logdir)
else:
logdir = tempfile.mkdtemp()
self.logdir = logdir
self._save_path = os.path.join(self.logdir, 'model.ckpt')
def __init__(self, **kwargs):
# These properties should have been set
# by the child class, as appropriate.
if not hasattr(self, 'uses_learning_phase'):
self.uses_learning_phase = False
if not hasattr(self, 'losses'):
self.losses = []
# 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.
allowed_kwargs = {
'input_shape', 'batch_input_shape', 'input_dtype', 'name', 'trainable'
}
for kwarg in kwargs.keys():
if kwarg not in allowed_kwargs:
raise TypeError('Keyword argument not understood:', kwarg)
name = kwargs.get('name')
if not name:
prefix = self.__class__.__name__.lower()
name = prefix + '_' + str(model_ops.get_uid(prefix))
self.name = name
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
# to insert before the current layer
if 'batch_input_shape' in kwargs:
batch_input_shape = tuple(kwargs['batch_input_shape'])
elif 'input_shape' in kwargs:
batch_input_shape = (None,) + tuple(kwargs['input_shape'])
self.batch_input_shape = batch_input_shape
input_dtype = kwargs.get('input_dtype', tf.float32)
self.input_dtype = input_dtype
def __init__(self, input_shape=None, batch_input_shape=None,
input_dtype=None, input_tensor=None, name=None):
self.input_spec = None
self.uses_learning_phase = False
self.trainable = False
self.built = True
self._trainable_weights = []
self._non_trainable_weights = []
self.inbound_nodes = []
self.outbound_nodes = []
self.constraints = {}
if not name:
prefix = 'input'
# TODO(rbharath): Keras uses a global var here to maintain
# unique counts. This seems dangerous. How does tensorflow handle?
name = prefix + '_' + str(model_ops.get_uid(prefix))
self.name = name
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:
# Attempt automatic input shape inference.
try:
batch_input_shape = model_ops.int_shape(input_tensor)
except:
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 = (None,) + tuple(input_shape)
else:
batch_input_shape = tuple(batch_input_shape)
if not input_dtype:
if input_tensor is None:
input_dtype = tf.float32
else:
input_dtype = model_ops.get_dtype(input_tensor)
self.batch_input_shape = batch_input_shape
self.input_dtype = input_dtype
if input_tensor is None:
input_tensor = tf.placeholder(dtype=input_dtype,
shape=batch_input_shape,
name=self.name)
else:
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],
input_shapes=[batch_input_shape],
output_shapes=[batch_input_shape])
def __init__(self,
input_shape=None,
batch_input_shape=None,
input_dtype=None,
input_tensor=None,
name=None):
self.input_spec = None
self.uses_learning_phase = False
self.trainable = False
self.built = True
self._trainable_weights = []
self._non_trainable_weights = []
self.inbound_nodes = []
self.outbound_nodes = []
self.constraints = {}
if not name:
prefix = 'input'
# TODO(rbharath): Keras uses a global var here to maintain
# unique counts. This seems dangerous. How does tensorflow handle?
name = prefix + '_' + str(model_ops.get_uid(prefix))
self.name = name
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:
# Attempt automatic input shape inference.
try:
batch_input_shape = model_ops.int_shape(input_tensor)
except:
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 = (None, ) + tuple(input_shape)
else:
batch_input_shape = tuple(batch_input_shape)
if not input_dtype:
if input_tensor is None:
input_dtype = tf.float32
else:
input_dtype = model_ops.get_dtype(input_tensor)
self.batch_input_shape = batch_input_shape
self.input_dtype = input_dtype
if input_tensor is None:
input_tensor = tf.placeholder(dtype=input_dtype,
shape=batch_input_shape,
name=self.name)
else:
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],
input_shapes=[batch_input_shape],
output_shapes=[batch_input_shape])