Still trying to make yapf happy (8cbcb5b7) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorflow_models/init.py

+4 −3

Original line number	Diff line number	Diff line
		@@ -294,7 +294,6 @@ class TensorflowGraphModel(Model):

		return loss


		def fit(self,
		dataset,
		nb_epoch=10,
		@@ -340,7 +339,8 @@ class TensorflowGraphModel(Model):
		def enqueue(sess, dataset, nb_epoch, epoch_end_indices):
		index = 0
		for epoch in range(nb_epoch):
		for X_b, y_b, w_b, ids_b in dataset.iterbatches(self.batch_size, pad_batches=self.pad_batches):
		for X_b, y_b, w_b, ids_b in dataset.iterbatches(
		self.batch_size, pad_batches=self.pad_batches):
		feed_dict = self.construct_feed_dict(X_b, y_b, w_b, ids_b)
		sess.run(self.train_graph.graph.enqueue, feed_dict=feed_dict)
		index += 1
		@@ -348,7 +348,8 @@ class TensorflowGraphModel(Model):
		sess.run(self.train_graph.graph.queue.close())

		epoch_end_indices = []
		enqueue_thread = threading.Thread(target=enqueue, args=[sess, dataset, nb_epoch, epoch_end_indices])
		enqueue_thread = threading.Thread(
		target=enqueue, args=[sess, dataset, nb_epoch, epoch_end_indices])
		enqueue_thread.daemon = True
		enqueue_thread.start()

deepchem/models/tensorflow_models/fcnet.py

+24 −12

Original line number	Diff line number	Diff line
		@@ -52,10 +52,15 @@ class TensorflowMultiTaskClassifier(TensorflowClassifier):
		assert n_layers > 0, 'Must have some layers defined.'

		label_placeholders = self.add_label_placeholders(graph, name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(graph, name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(graph,
		name_scopes)
		if training:
		graph.queue = tf.FIFOQueue(capacity=5, dtypes=[tf.float32]*(len(label_placeholders)+len(weight_placeholders)+1))
		graph.enqueue = graph.queue.enqueue([mol_features]+label_placeholders+weight_placeholders)
		graph.queue = tf.FIFOQueue(
		capacity=5,
		dtypes=[tf.float32] *
		(len(label_placeholders) + len(weight_placeholders) + 1))
		graph.enqueue = graph.queue.enqueue([mol_features] + label_placeholders
		+ weight_placeholders)
		queue_outputs = graph.queue.dequeue()
		labels = queue_outputs[1:len(label_placeholders) + 1]
		weights = queue_outputs[len(label_placeholders) + 1:]
		@@ -144,10 +149,15 @@ class TensorflowMultiTaskRegressor(TensorflowRegressor):
		assert n_layers > 0, 'Must have some layers defined.'

		label_placeholders = self.add_label_placeholders(graph, name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(graph, name_scopes)
		weight_placeholders = self.add_example_weight_placeholders(graph,
		name_scopes)
		if training:
		graph.queue = tf.FIFOQueue(capacity=5, dtypes=[tf.float32]*(len(label_placeholders)+len(weight_placeholders)+1))
		graph.enqueue = graph.queue.enqueue([mol_features]+label_placeholders+weight_placeholders)
		graph.queue = tf.FIFOQueue(
		capacity=5,
		dtypes=[tf.float32] *
		(len(label_placeholders) + len(weight_placeholders) + 1))
		graph.enqueue = graph.queue.enqueue([mol_features] + label_placeholders
		+ weight_placeholders)
		queue_outputs = graph.queue.dequeue()
		labels = queue_outputs[1:len(label_placeholders) + 1]
		weights = queue_outputs[len(label_placeholders) + 1:]
		@@ -375,7 +385,8 @@ class TensorflowMultiTaskFitTransformRegressor(TensorflowMultiTaskRegressor):
		def enqueue(sess, dataset, nb_epoch, epoch_end_indices):
		index = 0
		for epoch in range(nb_epoch):
		for X_b, y_b, w_b, ids_b in dataset.iterbatches(self.batch_size, pad_batches=self.pad_batches):
		for X_b, y_b, w_b, ids_b in dataset.iterbatches(
		self.batch_size, pad_batches=self.pad_batches):
		for transformer in self.fit_transformers:
		X_b = transformer.X_transform(X_b)
		feed_dict = self.construct_feed_dict(X_b, y_b, w_b, ids_b)
		@@ -385,7 +396,8 @@ class TensorflowMultiTaskFitTransformRegressor(TensorflowMultiTaskRegressor):
		sess.run(self.train_graph.graph.queue.close())

		epoch_end_indices = []
		enqueue_thread = threading.Thread(target=enqueue, args=[sess, dataset, nb_epoch, epoch_end_indices])
		enqueue_thread = threading.Thread(
		target=enqueue, args=[sess, dataset, nb_epoch, epoch_end_indices])
		enqueue_thread.daemon = True
		enqueue_thread.start()

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