class SequentialGraph(object):
"""An analog of Keras Sequential class for Graph data.
Like the Sequential class from Keras, but automatically passes topology
placeholders from GraphTopology to each graph layer (from layers) added
to the network. Non graph layers don't get the extra placeholders.
"""
def __init__(self, n_feat):
"""
Parameters
----------
n_feat: int
Number of features per atom.
"""
warnings.warn(
"SequentialGraph is deprecated. "
"Will be removed in DeepChem 1.4.", DeprecationWarning)
self.graph = tf.Graph()
with self.graph.as_default():
self.graph_topology = GraphTopology(n_feat)
self.output = self.graph_topology.get_atom_features_placeholder()
# Keep track of the layers
self.layers = []
def add(self, layer):
"""Adds a new layer to model."""
with self.graph.as_default():
# For graphical layers, add connectivity placeholders
if type(layer).__name__ in [
'GraphConv', 'GraphGather', 'GraphPool'
]:
if (len(self.layers) > 0
and hasattr(self.layers[-1], "__name__")):
assert self.layers[-1].__name__ != "GraphGather", \
'Cannot use GraphConv or GraphGather layers after a GraphGather'
self.output = layer(
[self.output] +
self.graph_topology.get_topology_placeholders())
else:
self.output = layer(self.output)
# Add layer to the layer list
self.layers.append(layer)
def get_graph_topology(self):
return self.graph_topology
def get_num_output_features(self):
"""Gets the output shape of the featurization layers of the network"""
return self.layers[-1].output_shape[1]
def return_outputs(self):
return self.output
def return_inputs(self):
return self.graph_topology.get_input_placeholders()
def get_layer(self, layer_id):
return self.layers[layer_id]
class SequentialGraph(object):
"""An analog of Keras Sequential class for Graph data.
Like the Sequential class from Keras, but automatically passes topology
placeholders from GraphTopology to each graph layer (from layers) added
to the network. Non graph layers don't get the extra placeholders.
"""
def __init__(self, n_feat):
"""
Parameters
----------
n_feat: int
Number of features per atom.
"""
warnings.warn("SequentialGraph is deprecated. "
"Will be removed in DeepChem 1.4.", DeprecationWarning)
self.graph = tf.Graph()
with self.graph.as_default():
self.graph_topology = GraphTopology(n_feat)
self.output = self.graph_topology.get_atom_features_placeholder()
# Keep track of the layers
self.layers = []
def add(self, layer):
"""Adds a new layer to model."""
with self.graph.as_default():
# For graphical layers, add connectivity placeholders
if type(layer).__name__ in ['GraphConv', 'GraphGather', 'GraphPool']:
if (len(self.layers) > 0 and hasattr(self.layers[-1], "__name__")):
assert self.layers[-1].__name__ != "GraphGather", \
'Cannot use GraphConv or GraphGather layers after a GraphGather'
self.output = layer([self.output] +
self.graph_topology.get_topology_placeholders())
else:
self.output = layer(self.output)
# Add layer to the layer list
self.layers.append(layer)
def get_graph_topology(self):
return self.graph_topology
def get_num_output_features(self):
"""Gets the output shape of the featurization layers of the network"""
return self.layers[-1].output_shape[1]
def return_outputs(self):
return self.output
def return_inputs(self):
return self.graph_topology.get_input_placeholders()
def get_layer(self, layer_id):
return self.layers[layer_id]
class SequentialGraph(object):
"""An analog of Keras Sequential class for Graph data.
Like the Sequential class from Keras, but automatically passes topology
placeholders from GraphTopology to each graph layer (from layers) added
to the network. Non graph layers don't get the extra placeholders.
"""
def __init__(self, n_feat):
"""
Parameters
----------
n_feat: int
Number of features per atom.
"""
self.graph = tf.Graph()
#with self.graph.as_default():
if 1:
self.graph_topology = GraphTopology(n_feat)
self.output = self.graph_topology.get_atom_features_placeholder()
self.training = tf.placeholder(dtype='float32',
shape=(),
name='ops_training')
# Keep track of the layers
self.layers = []
self.add_time = 0
def get_training_state(self):
return self.training
def add(self, layer):
"""Adds a new layer to model."""
#with self.graph.as_default():
if 1:
# For graphical layers, add connectivity placeholders
if type(layer).__name__ in [
'GraphResBlock', 'GraphConv', 'GraphGather', 'GraphPool'
]:
if (len(self.layers) > 0
and hasattr(self.layers[-1], "__name__")):
assert self.layers[-1].__name__ != "GraphGather", \
'Cannot use GraphConv or GraphGather layers after a GraphGather'
self.output = layer(
[self.output] +
self.graph_topology.get_topology_placeholders() +
[self.training] + [self.add_time])
else:
self.output = layer(self.output)
# Add layer to the layer list
self.layers.append(layer)
self.add_time = self.add_time + 1
def get_graph_topology(self):
return self.graph_topology
def get_num_output_features(self):
"""Gets the output shape of the featurization layers of the network"""
return self.layers[-1].output_shape[1]
def return_outputs(self):
return self.output
def return_inputs(self):
return self.graph_topology.get_input_placeholders()
def get_layer(self, layer_id):
return self.layers[layer_id]
