Merge pull request #360 from rbharath/low_data_updates (a37c108a) · Commits · 钟慕尧 / deepchem

deepchem/models/tests/test_overfit.py

+9 −9

Original line number	Diff line number	Diff line
		@@ -619,7 +619,7 @@ class TestOverfit(test_util.TensorFlowTestCase):
		# can measure model has memorized support). Replacement is turned off to
		# ensure that support contains full training set. This checks that the
		# model has mastered memorization of provided support.
		scores = model.evaluate(dataset, classification_metric, n_trials=5,
		scores, _ = model.evaluate(dataset, classification_metric, n_trials=5,
		n_pos=n_pos, n_neg=n_neg,
		exclude_support=False)

		@@ -686,7 +686,7 @@ class TestOverfit(test_util.TensorFlowTestCase):
		# can measure model has memorized support). Replacement is turned off to
		# ensure that support contains full training set. This checks that the
		# model has mastered memorization of provided support.
		scores = model.evaluate(dataset, classification_metric, n_trials=5,
		scores, _ = model.evaluate(dataset, classification_metric, n_trials=5,
		n_pos=n_pos, n_neg=n_neg,
		exclude_support=False)

		@@ -754,7 +754,7 @@ class TestOverfit(test_util.TensorFlowTestCase):
		# can measure model has memorized support). Replacement is turned off to
		# ensure that support contains full training set. This checks that the
		# model has mastered memorization of provided support.
		scores = model.evaluate(dataset, classification_metric, n_trials=5,
		scores, _ = model.evaluate(dataset, classification_metric, n_trials=5,
		n_pos=n_pos, n_neg=n_neg,
		exclude_support=False)

deepchem/models/tf_new_models/multitask_classifier.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -100,7 +100,7 @@ class MultitaskGraphClassifier(Model):
		self.add_optimizer()

		# Initialize
		self.init_fn = tf.initialize_all_variables()
		self.init_fn = tf.global_variables_initializer()
		sess.run(self.init_fn)

		# Path to save checkpoint files, which matches the

deepchem/models/tf_new_models/support_classifier.py

+3 −1

Original line number	Diff line number	Diff line
		@@ -413,6 +413,8 @@ class SupportGraphClassifier(Model):

		# Join information for all tasks.
		mean_task_scores = {}
		std_task_scores = {}
		for task in test_tasks:
		mean_task_scores[task] = np.mean(np.array(task_scores[task]))
		return mean_task_scores
		std_task_scores[task] = np.std(np.array(task_scores[task]))
		return mean_task_scores, std_task_scores

deepchem/nn/model_ops.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -640,7 +640,7 @@ 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)

		@@ -718,7 +718,7 @@ 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)
		@@ -773,8 +773,8 @@ 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))
		@@ -800,7 +800,7 @@ 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),
		@@ -817,7 +817,7 @@ 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,

examples/low_data/datasets.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -175,7 +175,7 @@ def load_sider_convmol():

		loader = dc.data.CSVLoader(
		tasks=SIDER_tasks, smiles_field="smiles", featurizer=featurizer)
		dataset = loader.featurize(dataset_file, debug=True)
		dataset = loader.featurize(dataset_file)
		print("%d datapoints in SIDER dataset" % len(dataset))

		# Initialize transformers

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