Merge pull request #1293 from Chasearmer/patch (93422613) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+33 −15

Original line number	Diff line number	Diff line
		@@ -199,7 +199,11 @@ class Layer(object):
		layer, complete with trained values for its variables."""
		self.variable_values = values

		def set_summary(self, summary_op, summary_description=None, collections=None):
		def set_summary(self,
		summary_op,
		include_variables=True,
		summary_description=None,
		collections=None):
		"""Annotates a tensor with a tf.summary operation

		This causes self.out_tensor to be logged to Tensorboard.
		@@ -208,6 +212,8 @@ class Layer(object):
		----------
		summary_op: str
		summary operation to annotate node
		include_variables: bool
		Optional bool to include layer variables to the summary
		summary_description: object, optional
		Optional summary_pb2.SummaryDescription()
		collections: list of graph collections keys, optional
		@@ -219,11 +225,12 @@ class Layer(object):
		"Invalid summary_op arg. Only 'tensor_summary', 'scalar', 'histogram' supported"
		)
		self.summary_op = summary_op
		self.include_variables = include_variables
		self.summary_description = summary_description
		self.collections = collections
		self.tensorboard = True

		def add_summary_to_tg(self, tb_input=None):
		def add_summary_to_tg(self, layer_vars):
		"""
		Create the summary operation for this layer, if set_summary() has been called on it.

		@@ -231,20 +238,30 @@ class Layer(object):

		Parameters
		----------
		tb_input: tensor
		the tensor to log to Tensorboard. If None, self.out_tensor is used.
		layer_vars: list of variables
		the list of variables to log to Tensorboard
		"""
		if self.tensorboard == False:
		return
		if tb_input == None:
		tb_input = self.out_tensor

		if self.summary_op == "tensor_summary":
		tf.summary.tensor_summary(self.name, tb_input, self.summary_description,
		tf.summary.tensor_summary(self.name, self.out_tensor,
		self.summary_description, self.collections)
		if self.include_variables:
		for var in layer_vars:
		tf.summary.tensor_summary(var.name, var, self.summary_description,
		self.collections)
		elif self.summary_op == 'scalar':
		tf.summary.scalar(self.name, tb_input, self.collections)
		tf.summary.scalar(self.name, self.out_tensor, self.collections)
		if self.include_variables:
		for var in layer_vars:
		tf.summary.tensor_summary(var.name, var, self.collections,
		self.collections)
		elif self.summary_op == 'histogram':
		tf.summary.histogram(self.name, tb_input, self.collections)
		tf.summary.histogram(self.name, self.out_tensor, self.collections)
		if self.include_variables:
		for var in layer_vars:
		tf.summary.histogram(var.name, var, self.collections)

		def copy(self, replacements={}, variables_graph=None, shared=False):
		"""Duplicate this Layer and all its inputs.
		@@ -2542,13 +2559,14 @@ class GraphConv(Layer):
		def _create_variables(self, in_channels):
		# Generate the nb_affine weights and biases
		W_list = [
		initializations.glorot_uniform([in_channels, self.out_channel])
		initializations.glorot_uniform(
		[in_channels, self.out_channel], name='kernel')
		for k in range(self.num_deg)
		]
		b_list = [
		model_ops.zeros(shape=[
		self.out_channel,
		]) for k in range(self.num_deg)
		], name='bias') for k in range(self.num_deg)
		]
		return (W_list, b_list)

		@@ -3278,9 +3296,9 @@ class VinaFreeEnergy(Layer):

		def hydrogen_bond(self, d):
		"""Computes Autodock Vina's hydrogen bond interaction term."""
		out_tensor = tf.where(d < -0.7, tf.ones_like(d),
		tf.where(d < 0, (1.0 / 0.7) * (0 - d),
		tf.zeros_like(d)))
		out_tensor = tf.where(
		d < -0.7, tf.ones_like(d),
		tf.where(d < 0, (1.0 / 0.7) * (0 - d), tf.zeros_like(d)))
		return out_tensor

		def gaussian_first(self, d):

deepchem/models/tensorgraph/tensor_graph.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -661,15 +661,15 @@ class TensorGraph(Model):
		if self.random_seed is not None:
		tf.set_random_seed(self.random_seed)
		self._install_queue()
		self.built = True
		for layer in self.topsort():
		with tf.name_scope(layer.name):
		layer.create_tensor(training=self._training_placeholder)
		self.rnn_initial_states += layer.rnn_initial_states
		self.rnn_final_states += layer.rnn_final_states
		self.rnn_zero_states += layer.rnn_zero_states
		layer.add_summary_to_tg()
		layer.add_summary_to_tg(self.get_layer_variables(layer))
		self.session = tf.Session(config=self.configproto)
		self.built = True

		# Ensure all training operators have been created.

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