changes (1505edb6) · Commits · 钟慕尧 / deepchem

deepchem/models/graph_models.py

+29 −16

Original line number	Diff line number	Diff line
		@@ -4,6 +4,8 @@ import deepchem as dc
		import numpy as np
		import tensorflow as tf

		from typing import List
		from deepchem.utils.typing import OneOrMany
		from deepchem.data import NumpyDataset, pad_features
		from deepchem.feat.graph_features import ConvMolFeaturizer
		from deepchem.feat.mol_graphs import ConvMol
		@@ -114,25 +116,36 @@ class WeaveModel(KerasModel):

		# Build the model.

		atom_features = Input(shape=(self.n_atom_feat,))
		pair_features = Input(shape=(self.n_pair_feat,))
		atom_features = Input(shape=(self.n_atom_feat[0],))
		pair_features = Input(shape=(self.n_pair_feat[0],))
		pair_split = Input(shape=tuple(), dtype=tf.int32)
		atom_split = Input(shape=tuple(), dtype=tf.int32)
		atom_to_pair = Input(shape=(2,), dtype=tf.int32)
		weave_layer1A, weave_layer1P = layers.WeaveLayer(
		n_atom_input_feat=self.n_atom_feat,
		n_pair_input_feat=self.n_pair_feat,
		n_atom_output_feat=self.n_hidden,
		n_pair_output_feat=self.n_hidden)(
		[atom_features, pair_features, pair_split, atom_to_pair])
		weave_layer2A, weave_layer2P = layers.WeaveLayer(
		n_atom_input_feat=self.n_hidden,
		n_pair_input_feat=self.n_hidden,
		n_atom_output_feat=self.n_hidden,
		n_pair_output_feat=self.n_hidden,
		update_pair=False)(
		[weave_layer1A, weave_layer1P, pair_split, atom_to_pair])
		dense1 = Dense(self.n_graph_feat, activation=tf.nn.tanh)(weave_layer2A)
		inputs = [atom_features, pair_features, pair_split, atom_to_pair]
		for ind in range(n_weave):
		n_atom = self.n_atom_feat[ind]
		n_pair = self.n_pair_feat[ind]
		if ind < n_weave - 1:
		n_atom_next = self.n_atom_feat[ind+1]
		n_pair_next = self.n_pair_feat[ind+1]
		else:
		n_atom_next = n_hidden
		n_pair_next = n_hidden
		weave_layer_ind_A, weave_layer_ind_P = layers.WeaveLayer(
		n_atom_input_feat=n_atom,
		n_pair_input_feat=n_pair,
		n_atom_output_feat=n_atom_next,
		n_pair_output_feat=n_pair_next)(inputs)
		inputs = [weave_layer_ind_A, weave_layer_ind_P, pair_split, atom_to_pair]
		#weave_layer2A, weave_layer2P = layers.WeaveLayer(
		# n_atom_input_feat=self.n_hidden,
		# n_pair_input_feat=self.n_hidden,
		# n_atom_output_feat=self.n_hidden,
		# n_pair_output_feat=self.n_hidden,
		# update_pair=False)(
		# [weave_layer1A, weave_layer1P, pair_split, atom_to_pair])
		#dense1 = Dense(self.n_graph_feat, activation=tf.nn.tanh)(weave_layer2A)
		dense1 = Dense(self.n_graph_feat, activation=tf.nn.tanh)(weave_layer_ind_A)
		# Batch normalization causes issues, spitting out NaNs if
		# allowed to train
		batch_norm1 = BatchNormalization(epsilon=1e-5, trainable=False)(dense1)

deepchem/models/layers.py

+3 −1

Original line number	Diff line number	Diff line
		@@ -2,6 +2,7 @@
		import tensorflow as tf
		import numpy as np
		import collections
		from typing import Callable, Dict, List
		from tensorflow.keras import activations, initializers, backend
		from tensorflow.keras.layers import Dropout

		@@ -2121,7 +2122,8 @@ class WeaveLayer(tf.keras.layers.Layer):
		self.n_pair_output_feat = n_pair_output_feat
		self.W_AP, self.b_AP, self.W_PP, self.b_PP, self.W_P, self.b_P = None, None, None, None, None, None

		def get_config(self):
		def get_config(self) -> Dict:
		"""Returns config dictionary for this layer."""
		config = super(WeaveLayer, self).get_config()
		config['n_atom_input_feat'] = self.n_atom_input_feat
		config['n_pair_input_feat'] = self.n_pair_input_feat

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