Weave single out_tensor (af33de9c) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/graph_layers.py

+4 −36

Original line number	Diff line number	Diff line
		@@ -20,38 +20,6 @@ from deepchem.models.tensorgraph.layers import Layer, PassThroughLayer
		from deepchem.models.tensorgraph.layers import convert_to_layers


		class Combine_AP(Layer):

		def __init__(self, **kwargs):
		super(Combine_AP, self).__init__(**kwargs)

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		if in_layers is None:
		in_layers = self.in_layers
		in_layers = convert_to_layers(in_layers)
		A = in_layers[0].out_tensor
		P = in_layers[1].out_tensor
		out_tensor = [A, P]
		if set_tensors:
		self.out_tensor = out_tensor
		return out_tensor


		class Separate_AP(Layer):

		def __init__(self, **kwargs):
		super(Separate_AP, self).__init__(**kwargs)

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		if in_layers is None:
		in_layers = self.in_layers
		in_layers = convert_to_layers(in_layers)
		out_tensor = in_layers[0].out_tensor[0]
		if set_tensors:
		self.out_tensor = out_tensor
		return out_tensor


		class WeaveLayer(Layer):
		""" TensorGraph style implementation
		The same as deepchem.nn.WeaveLayer
		@@ -167,11 +135,11 @@ class WeaveLayer(Layer):

		self.build()

		atom_features = in_layers[1].out_tensor
		pair_features = in_layers[2].out_tensor
		atom_features = in_layers[0].out_tensor
		pair_features = in_layers[1].out_tensor

		pair_split = in_layers[3].out_tensor
		atom_to_pair = in_layers[4].out_tensor
		pair_split = in_layers[2].out_tensor
		atom_to_pair = in_layers[3].out_tensor

		AA = tf.matmul(atom_features, self.W_AA) + self.b_AA
		AA = self.activation(AA)

deepchem/models/tensorgraph/models/graph_models.py

+8 −9

Original line number	Diff line number	Diff line
		import collections

		import numpy as np
		import six
		import tensorflow as tf

		from deepchem.data import NumpyDataset
		from deepchem.feat.graph_features import ConvMolFeaturizer
		from deepchem.feat.mol_graphs import ConvMol
		from deepchem.metrics import to_one_hot, from_one_hot
		from deepchem.models.tensorgraph.graph_layers import WeaveLayer, WeaveGather, \
		Combine_AP, Separate_AP, DTNNEmbedding, DTNNStep, DTNNGather, DAGLayer, \
		from deepchem.metrics import to_one_hot
		from deepchem.models.tensorgraph.graph_layers import WeaveGather, \
		DTNNEmbedding, DTNNStep, DTNNGather, DAGLayer, \
		DAGGather, DTNNExtract, MessagePassing, SetGather, WeaveLayerFactory
		from deepchem.models.tensorgraph.layers import Dense, Concat, SoftMax, \
		SoftMaxCrossEntropy, GraphConv, BatchNorm, \
		@@ -14,10 +17,6 @@ from deepchem.models.tensorgraph.layers import Dense, Concat, SoftMax, \
		from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature
		from deepchem.models.tensorgraph.tensor_graph import TensorGraph
		from deepchem.trans import undo_transforms
		from deepchem.utils.evaluate import GeneratorEvaluator
		from deepchem.data import NumpyDataset
		from deepchem.data.data_loader import featurize_smiles_np
		from deepchem.feat.graph_features import ConvMolFeaturizer


		class WeaveTensorGraph(TensorGraph):
		@@ -82,11 +81,11 @@ class WeaveTensorGraph(TensorGraph):
		in_layers=[
		weave_layer1[0], weave_layer1[1], self.pair_split, self.atom_to_pair
		])
		separated = Separate_AP(in_layers=[weave_layer2])
		separated = weave_layer2[0]
		dense1 = Dense(
		out_channels=self.n_graph_feat,
		activation_fn=tf.nn.tanh,
		in_layers=[separated])
		in_layers=separated)
		batch_norm1 = BatchNormalization(epsilon=1e-5, mode=1, in_layers=[dense1])
		weave_gather = WeaveGather(
		self.batch_size,

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