Follow best practices for learning rate (bacd3b5e) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/tests/test_tensor_graph.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -30,7 +30,7 @@ class TestTensorGraph(unittest.TestCase):
		label = Label(shape=(None, 2))
		smce = SoftMaxCrossEntropy(in_layers=[label, dense])
		loss = ReduceMean(in_layers=[smce])
		tg = dc.models.TensorGraph(learning_rate=0.1)
		tg = dc.models.TensorGraph(learning_rate=0.01)
		tg.add_output(output)
		tg.set_loss(loss)
		tg.fit(dataset, nb_epoch=1000)
		@@ -66,7 +66,7 @@ class TestTensorGraph(unittest.TestCase):

		total_loss = ReduceMean(in_layers=entropies)

		tg = dc.models.TensorGraph(learning_rate=0.1)
		tg = dc.models.TensorGraph(learning_rate=0.01)
		for output in outputs:
		tg.add_output(output)
		tg.set_loss(total_loss)
		@@ -90,7 +90,7 @@ class TestTensorGraph(unittest.TestCase):
		dense = Dense(out_channels=1, in_layers=[features])
		label = Label(shape=(None, 1))
		loss = ReduceSquareDifference(in_layers=[dense, label])
		tg = dc.models.TensorGraph(learning_rate=0.1)
		tg = dc.models.TensorGraph(learning_rate=0.01)
		tg.add_output(dense)
		tg.set_loss(loss)
		tg.fit(dataset, nb_epoch=1000)
		@@ -173,7 +173,7 @@ class TestTensorGraph(unittest.TestCase):
		tg = dc.models.TensorGraph(
		tensorboard=True,
		tensorboard_log_frequency=1,
		learning_rate=0.1,
		learning_rate=0.01,
		model_dir='/tmp/tensorgraph')
		tg.add_output(output)
		tg.set_loss(loss)
		@@ -197,7 +197,7 @@ class TestTensorGraph(unittest.TestCase):
		label = Label(shape=(None, 2))
		smce = SoftMaxCrossEntropy(in_layers=[label, dense])
		loss = ReduceMean(in_layers=[smce])
		tg = dc.models.TensorGraph(learning_rate=0.1)
		tg = dc.models.TensorGraph(learning_rate=0.01)
		tg.add_output(output)
		tg.set_loss(loss)
		tg.fit(dataset, nb_epoch=1)
		@@ -237,7 +237,7 @@ class TestTensorGraph(unittest.TestCase):

		total_loss = ReduceMean(in_layers=[smce])

		tg = dc.models.TensorGraph(learning_rate=0.1)
		tg = dc.models.TensorGraph(learning_rate=0.01)
		for output in outputs:
		tg.add_output(output)
		tg.set_loss(total_loss)

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