def __init__(self, layers=None, name=None):
self.layers = [] # Stack of layers.
self.model = None # Internal Model instance.
self.inputs = [] # List of input tensors
self.outputs = [] # List of length 1: the output tensor (unique).
self._trainable = True
self._initial_weights = None
# Model attributes.
self.inbound_nodes = []
self.outbound_nodes = []
self.built = False
# Set model name.
if not name:
prefix = 'sequential_'
name = prefix + str(K.get_uid(prefix))
self.name = name
# The following properties are not actually used by Keras;
# they exist for compatibility with TF's variable scoping mechanism.
self._updates = []
self._losses = []
self._scope = None
self._reuse = None
self._base_name = name
self._graph = ops.get_default_graph()
# Add to the model any layers passed to the constructor.
if layers:
for layer in layers:
self.add(layer)
def __init__(self, layers=None, name=None):
self.layers = [] # Stack of layers.
self.model = None # Internal Model instance.
self.inputs = [] # List of input tensors
self.outputs = [] # List of length 1: the output tensor (unique).
self._trainable = True
self._initial_weights = None
# Model attributes.
self.inbound_nodes = []
self.outbound_nodes = []
self.built = False
# Set model name.
if not name:
prefix = 'sequential_'
name = prefix + str(K.get_uid(prefix))
self.name = name
# The following properties are not actually used by Keras;
# they exist for compatibility with TF's variable scoping mechanism.
self._updates = []
self._losses = []
self._scope = None
self._reuse = None
self._base_name = name
self._graph = ops.get_default_graph()
# Add to the model any layers passed to the constructor.
if layers:
for layer in layers:
self.add(layer)
def __init__(self, layers=None, name=None):
self.layers = [] # Stack of layers.
self.model = None # Internal Model instance.
self.inputs = [] # List of input tensors
self.outputs = [] # List of length 1: the output tensor (unique).
self._trainable = True
self._initial_weights = None
# Model attributes.
self.inbound_nodes = []
self.outbound_nodes = []
self.built = False
# Set model name.
if not name:
prefix = 'sequential_'
name = prefix + str(K.get_uid(prefix))
self.name = name
# Add to the model any layers passed to the constructor.
if layers:
for layer in layers:
self.add(layer)
def __init__(self, layers=None, name=None):
self.layers = [] # Stack of layers.
self.model = None # Internal Model instance.
self.inputs = [] # List of input tensors
self.outputs = [] # List of length 1: the output tensor (unique).
self._trainable = True
self._initial_weights = None
# Model attributes.
self.inbound_nodes = []
self.outbound_nodes = []
self.built = False
# Set model name.
if not name:
prefix = 'sequential_'
name = prefix + str(K.get_uid(prefix))
self.name = name
# Add to the model any layers passed to the constructor.
if layers:
for layer in layers:
self.add(layer)