YAPF (4712adac) · Commits · 钟慕尧 / deepchem

contrib/atomicconv/acnn/core/opt_scaffold.py

+5 −4

Original line number	Diff line number	Diff line
		@@ -40,6 +40,7 @@ transformers = [
		]
		for transformer in transformers:
		train_dataset = transformer.transform(train_dataset)
		test_dataset = transformer.transform(test_dataset)

		at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]
		radial = [[12.0], [0.0, 4.0, 8.0], [4.0]]
		@@ -85,7 +86,7 @@ train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset,
		train_scores = train_evaluator.compute_model_performance(metric)
		print("Train scores")
		print(train_scores)

		print("Prediction")
		predictions = model.predict(test_dataset, transformers)
		print(predictions)
		test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers)
		test_scores = test_evaluator.compute_model_performance(metric)
		print("Test scores")
		print(test_scores)

contrib/atomicconv/models/atomicnet_ops.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -80,8 +80,8 @@ def AtomicNNLayer(tensor, size, weights, biases, name=None):
		"""

		if len(tensor.get_shape()) != 2:
		raise ValueError(
		'Dense layer input must be 2D, not %dD' % len(tensor.get_shape()))
		raise ValueError('Dense layer input must be 2D, not %dD' %
		len(tensor.get_shape()))
		with tf.name_scope(name, 'fully_connected', [tensor, weights, biases]):
		return tf.nn.xw_plus_b(tensor, weights, biases)

contrib/atomicconv/models/legacy.py

+10 −9

Original line number	Diff line number	Diff line
		@@ -416,8 +416,8 @@ class TensorflowGraphModel(Model):
		feeding and fetching the same tensor.
		"""
		weights = []
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		with placeholder_scope:
		for task in range(self.n_tasks):
		weights.append(
		@@ -605,7 +605,8 @@ class TensorflowClassifier(TensorflowGraphModel):
		example.
		"""
		return tf.multiply(
		tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels), weights)
		tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels),
		weights)

		def add_label_placeholders(self, graph, name_scopes):
		"""Add Placeholders for labels for each task.
		@@ -616,8 +617,8 @@ class TensorflowClassifier(TensorflowGraphModel):
		Placeholders are wrapped in identity ops to avoid the error caused by
		feeding and fetching the same tensor.
		"""
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		with graph.as_default():
		batch_size = self.batch_size
		n_classes = self.n_classes
		@@ -769,8 +770,8 @@ class TensorflowRegressor(TensorflowGraphModel):
		Placeholders are wrapped in identity ops to avoid the error caused by
		feeding and fetching the same tensor.
		"""
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		with graph.as_default():
		batch_size = self.batch_size
		labels = []
		@@ -858,8 +859,8 @@ class TensorflowMultiTaskRegressor(TensorflowRegressor):
		batch_size x n_features.
		"""
		n_features = self.n_features
		placeholder_scope = TensorflowGraph.get_placeholder_scope(
		graph, name_scopes)
		placeholder_scope = TensorflowGraph.get_placeholder_scope(graph,
		name_scopes)
		with graph.as_default():
		with placeholder_scope:
		self.mol_features = tf.placeholder(

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