Merge pull request #1578 from peastman/keraslayers (a5fa0439) · Commits · 钟慕尧 / deepchem

deepchem/models/layers.py

0 → 100644

+2422 −0

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deepchem/models/tensorgraph/graph_layers.py

+73 −719

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deepchem/models/tensorgraph/layers.py

+130 −1363

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deepchem/models/tensorgraph/tests/test_layers.py

+8 −10

Original line number	Diff line number	Diff line
		@@ -63,10 +63,10 @@ from deepchem.models.tensorgraph.IRV import Slice
		from deepchem.models.tensorgraph.graph_layers import DTNNEmbedding
		from deepchem.models.tensorgraph.graph_layers import DTNNExtract
		from deepchem.models.tensorgraph.graph_layers import WeaveGather
		from deepchem.models.tensorgraph.graph_layers import GatedRecurrentUnit
		from deepchem.models.tensorgraph.graph_layers import EdgeNetwork
		from deepchem.models.tensorgraph.graph_layers import DTNNGather
		from deepchem.models.tensorgraph.graph_layers import DAGGather
		from deepchem.models.layers import GatedRecurrentUnit
		from deepchem.models.layers import EdgeNetwork


		class TestLayers(test_util.TensorFlowTestCase):
		@@ -950,7 +950,7 @@ class TestLayers(test_util.TensorFlowTestCase):
		# Layer is wrapper around embedding lookup, tested that then
		sess.run(tf.global_variables_initializer())
		out_tensor = dtnn_embedding.out_tensor.eval()
		embedding_val = dtnn_embedding.embedding_list.eval()
		embedding_val = dtnn_embedding.trainable_variables[0].eval()
		expected_output = embedding_val[test_tensor_input]
		self.assertAllClose(out_tensor, expected_output)
		self.assertAllClose(out_tensor.shape,
		@@ -1023,7 +1023,7 @@ class TestLayers(test_util.TensorFlowTestCase):
		messages_tf = tf.convert_to_tensor(messages_np, dtype=tf.float32)
		inputs_tf = tf.convert_to_tensor(inputs_np, dtype=tf.float32)
		gru = GatedRecurrentUnit(n_hidden=n_hidden, init=init_method)
		h = gru.forward(inputs_tf, messages_tf)
		h = gru([inputs_tf, messages_tf])

		sess.run(tf.global_variables_initializer())
		h = h.eval()
		@@ -1048,13 +1048,11 @@ class TestLayers(test_util.TensorFlowTestCase):
		atom_features_np, dtype=tf.float32)
		atom_to_pair_tf = tf.convert_to_tensor(atom_to_pair_np, dtype=tf.int32)
		edge_network = EdgeNetwork(
		pair_features=pair_features_tf,
		n_pair_features=n_pair_features,
		n_hidden=n_hidden,
		init=init_method)

		n_pair_features=n_pair_features, n_hidden=n_hidden, init=init_method)
		edge_network.build([])
		sess.run(tf.global_variables_initializer())
		output = edge_network.forward(atom_features_tf, atom_to_pair_tf).eval()
		output = edge_network(
		[pair_features_tf, atom_features_tf, atom_to_pair_tf]).eval()
		self.assertAllClose(output, expected_output)
		self.assertEqual(output.shape, expected_output.shape)

deepchem/models/tensorgraph/tests/test_layers_eager.py

+4 −3

Original line number	Diff line number	Diff line
		@@ -593,7 +593,8 @@ class TestLayersEager(test_util.TensorFlowTestCase):
		layer = layers.GraphConv(out_channels)
		result = layer(*args)
		assert result.shape == (n_atoms, out_channels)
		assert len(layer.trainable_variables) == 2 * layer.num_deg
		num_deg = 2 * layer.max_degree + (1 - layer.min_degree)
		assert len(layer.trainable_variables) == 2 * num_deg

		def test_graph_pool(self):
		"""Test invoking GraphPool in eager mode."""
		@@ -665,10 +666,10 @@ class TestLayersEager(test_util.TensorFlowTestCase):
		test_out, support_out = layer(test, support)
		assert test_out.shape == (n_test, n_feat)
		assert support_out.shape == (n_support, n_feat)
		assert len(layer.trainable_variables) == 7
		assert len(layer.trainable_variables) == 6

		def test_iter_ref_lstm_embedding(self):
		"""Test invoking AttnLSTMEmbedding in eager mode."""
		"""Test invoking IterRefLSTMEmbedding in eager mode."""
		with context.eager_mode():
		max_depth = 5
		n_test = 5

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