Merge pull request #511 from lilleswing/atomic-convs (2920bc29) · Commits · 钟慕尧 / deepchem

contrib/atomicconv/models/atomicnet_ops.py

+3 −3

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)

		@@ -282,7 +282,7 @@ def AtomicConvolutionLayer(X, Nbrs, Nbrs_Z, atom_types, radial_params, boxsize,
		"""Atomic convoluation layer

		N = max_num_atoms, M = max_num_neighbors, B = batch_size, d = num_features
		l = num_radial_filters
		l = num_radial_filters * num_atom_types

		Parameters
		----------

contrib/atomicconv/models/legacy.py

+12 −11

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(
		@@ -604,8 +604,9 @@ class TensorflowClassifier(TensorflowGraphModel):
		A tensor with shape batch_size containing the weighted cost for each
		example.
		"""
		return tf.mul(
		tf.nn.softmax_cross_entropy_with_logits(logits, labels), weights)
		return tf.multiply(
		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
		@@ -758,7 +759,7 @@ class TensorflowRegressor(TensorflowGraphModel):
		A tensor with shape batch_size containing the weighted cost for each
		example.
		"""
		return tf.mul(0.5 * tf.square(output - labels), weights)
		return tf.multiply(0.5 * tf.square(output - labels), weights)

		def add_label_placeholders(self, graph, name_scopes):
		"""Add Placeholders for labels for each task.
		@@ -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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