Final Test Fixes (a4d269cd) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/tensor_graph.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -214,7 +214,7 @@ class TensorGraph(Model):
		feed_dict[self.task_weights[0]] = w_b
		yield feed_dict

		def predict_on_generator(self, generator, sess=None):
		def predict_on_generator(self, generator):
		"""Generates output predictions for the input samples,
		processing the samples in a batched way.

		@@ -226,7 +226,7 @@ class TensorGraph(Model):
		# Returns
		A Numpy array of predictions.
		"""
		retval = self.predict_proba_on_generator(generator, sess)
		retval = self.predict_proba_on_generator(generator)
		if self.mode == 'classification':
		retval = np.expand_dims(from_one_hot(retval, axis=2), axis=1)
		return retval

deepchem/utils/test/test_generator_evaluator.py

+10 −10

Original line number	Diff line number	Diff line
		@@ -19,8 +19,8 @@ class TestGeneratorEvaluator(TestCase):
		X = np.ones(shape=(n_data_points / 2, n_features)) * -1
		X1 = np.ones(shape=(n_data_points / 2, n_features))
		X = np.concatenate((X, X1))
		class_1 = np.array([[0.0, 1.0] for x in range(n_data_points / 2)])
		class_0 = np.array([[1.0, 0.0] for x in range(n_data_points / 2)])
		class_1 = np.array([[0.0, 1.0] for x in range(int(n_data_points / 2))])
		class_0 = np.array([[1.0, 0.0] for x in range(int(n_data_points / 2))])
		y1 = np.concatenate((class_0, class_1))
		y2 = np.concatenate((class_1, class_0))
		X = NumpyDataset(X)
		@@ -34,7 +34,7 @@ class TestGeneratorEvaluator(TestCase):
		outputs = []
		entropies = []
		labels = []
		for i in xrange(2):
		for i in range(2):
		label = Label(shape=(None, 2))
		labels.append(label)
		dense = Dense(out_channels=2, in_layers=[features])
		@@ -67,11 +67,11 @@ class TestGeneratorEvaluator(TestCase):
		n_data_points = 20
		n_features = 10

		X = np.ones(shape=(n_data_points / 2, n_features)) * -1
		X1 = np.ones(shape=(n_data_points / 2, n_features))
		X = np.ones(shape=(int(n_data_points / 2), n_features)) * -1
		X1 = np.ones(shape=(int(n_data_points / 2), n_features))
		X = np.concatenate((X, X1))
		class_1 = np.array([[0.0, 1.0] for x in range(n_data_points / 2)])
		class_0 = np.array([[1.0, 0.0] for x in range(n_data_points / 2)])
		class_1 = np.array([[0.0, 1.0] for x in range(int(n_data_points / 2))])
		class_0 = np.array([[1.0, 0.0] for x in range(int(n_data_points / 2))])
		y1 = np.concatenate((class_0, class_1))
		X = NumpyDataset(X)
		ys = [NumpyDataset(y1)]
		@@ -84,7 +84,7 @@ class TestGeneratorEvaluator(TestCase):
		outputs = []
		entropies = []
		labels = []
		for i in xrange(1):
		for i in range(1):
		label = Label(shape=(None, 2))
		labels.append(label)
		dense = Dense(out_channels=2, in_layers=[features])
		@@ -131,7 +131,7 @@ class TestGeneratorEvaluator(TestCase):
		outputs = []
		losses = []
		labels = []
		for i in xrange(2):
		for i in range(2):
		label = Label(shape=(None, 1))
		dense = Dense(out_channels=1, in_layers=[features])
		loss = ReduceSquareDifference(in_layers=[dense, label])
		@@ -177,7 +177,7 @@ class TestGeneratorEvaluator(TestCase):
		outputs = []
		losses = []
		labels = []
		for i in xrange(1):
		for i in range(1):
		label = Label(shape=(None, 1))
		dense = Dense(out_channels=1, in_layers=[features])
		loss = ReduceSquareDifference(in_layers=[dense, label])

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