Update deprecated inialize_all_variables and op_scope functions (98d32fdb) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorflow_models/init.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -301,7 +301,7 @@ class TensorflowGraphModel(Model):
		train_op = self.get_training_op(
		self.train_graph.graph, self.train_graph.loss)
		with self._get_shared_session(train=True) as sess:
		sess.run(tf.initialize_all_variables())
		sess.run(tf.global_variables_initializer())
		saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)
		# Save an initial checkpoint.
		saver.save(sess, self._save_path, global_step=0)

deepchem/models/tensorflow_models/model_ops.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -78,7 +78,7 @@ def add_bias(tensor, init=None, name=None):
		"""
		if init is None:
		init = tf.zeros([tensor.get_shape()[-1].value])
		with tf.op_scope([tensor], name, tensor.op.name):
		with tf.name_scope(name, tensor.op.name, [tensor]):
		b = tf.Variable(init, name='b')
		return tf.nn.bias_add(tensor, b)

		@@ -156,7 +156,7 @@ def fully_connected_layer(tensor, size=None, weight_init=None, bias_init=None,
		if bias_init is None:
		bias_init = tf.zeros([size])

		with tf.op_scope([tensor], name, 'fully_connected'):
		with tf.name_scope(name, 'fully_connected', [tensor]):
		w = tf.Variable(weight_init, name='w', dtype=tf.float32)
		b = tf.Variable(bias_init, name='b', dtype=tf.float32)
		return tf.nn.xw_plus_b(tensor, w, b)
		@@ -211,8 +211,8 @@ def multitask_logits(features, num_tasks, num_classes=2, weight_init=None,
		logits_list = []
		with tf.name_scope('multitask_logits'):
		for task_idx in range(num_tasks):
		with tf.op_scope([features], name,
		('task' + str(task_idx).zfill(len(str(num_tasks))))):
		with tf.name_scope(name,
		('task' + str(task_idx).zfill(len(str(num_tasks)))), [features]):
		logits_list.append(
		logits(features, num_classes, weight_init=weight_init,
		bias_init=bias_init, dropout_prob=dropout_prob))
		@@ -238,7 +238,7 @@ def logits(features, num_classes=2, weight_init=None, bias_init=None,
		Returns:
		A logits tensor with shape batch_size x num_classes.
		"""
		with tf.op_scope([features], name, 'logits') as name:
		with tf.name_scope(name, 'logits', [features]) as name:
		return dropout(
		fully_connected_layer(features, num_classes, weight_init=weight_init,
		bias_init=bias_init, name=name),
		@@ -255,7 +255,7 @@ def softmax_N(tensor, name=None):
		Returns:
		A tensor with softmax-normalized values on the last dimension.
		"""
		with tf.op_scope([tensor], name, 'softmax_N'):
		with tf.name_scope(name, 'softmax_N', [tensor]):
		exp_tensor = tf.exp(tensor)
		reduction_indices = [tensor.get_shape().ndims - 1]
		return tf.div(exp_tensor,

deepchem/models/tensorflow_models/progressive_joint.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -234,7 +234,7 @@ class ProgressiveJointRegressor(TensorflowMultiTaskRegressor):
		train_op = self.get_training_op(
		self.train_graph.graph, self.train_graph.loss)
		with self._get_shared_session(train=True) as sess:
		sess.run(tf.initialize_all_variables())
		sess.run(tf.global_variables_initializer())
		saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)
		# Save an initial checkpoint.
		saver.save(sess, self._save_path, global_step=0)

deepchem/models/tensorflow_models/progressive_multitask.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -282,7 +282,7 @@ class ProgressiveMultitaskRegressor(TensorflowMultiTaskRegressor):
		train_op = self.get_training_op(
		self.train_graph.graph, self.train_graph.loss)
		with self._get_shared_session(train=True) as sess:
		sess.run(tf.initialize_all_variables())
		sess.run(tf.global_variables_initializer())
		saver = tf.train.Saver(max_to_keep=max_checkpoints_to_keep)
		# Save an initial checkpoint.
		saver.save(sess, self._save_path, global_step=0)

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