Merge pull request #2111 from peastman/gan (7c514756) · Commits · 钟慕尧 / deepchem

deepchem/models/gan.py

+6 −8

Original line number	Diff line number	Diff line
		@@ -83,13 +83,11 @@ class GAN(KerasModel):
		self.data_input_layers = []
		for shape in self.get_data_input_shapes():
		self.data_input_layers.append(Input(shape=shape))
		self.data_inputs = [i.experimental_ref() for i in self.data_input_layers]
		self.data_inputs = [i.ref() for i in self.data_input_layers]
		self.conditional_input_layers = []
		for shape in self.get_conditional_input_shapes():
		self.conditional_input_layers.append(Input(shape=shape))
		self.conditional_inputs = [
		i.experimental_ref() for i in self.conditional_input_layers
		]
		self.conditional_inputs = [i.ref() for i in self.conditional_input_layers]

		# Create the generators.

		@@ -344,9 +342,9 @@ class GAN(KerasModel):

		inputs = [self.get_noise_batch(self.batch_size)]
		for input in self.data_input_layers:
		inputs.append(feed_dict[input.experimental_ref()])
		inputs.append(feed_dict[input.ref()])
		for input in self.conditional_input_layers:
		inputs.append(feed_dict[input.experimental_ref()])
		inputs.append(feed_dict[input.ref()])
		discrim_error += self.fit_generator(
		[(inputs, [], [])],
		variables=self.discrim_variables,
		@@ -373,7 +371,7 @@ class GAN(KerasModel):
		# Write checkpoints and report progress.

		if discrim_average_steps == checkpoint_interval:
		self._exec_with_session(lambda: manager.save())
		manager.save()
		discrim_loss = discrim_error / max(1, discrim_average_steps)
		gen_loss = gen_error / max(1, gen_average_steps)
		print(
		@@ -393,7 +391,7 @@ class GAN(KerasModel):
		print(
		'Ending global_step %d: generator average loss %g, discriminator average loss %g'
		% (global_step, gen_loss, discrim_loss))
		self._exec_with_session(lambda: manager.save())
		manager.save()
		time2 = time.time()
		print("TIMING: model fitting took %0.3f s" % (time2 - time1))

deepchem/models/tests/test_gan.py

+1 −4

Original line number	Diff line number	Diff line
		@@ -128,10 +128,7 @@ class TestGAN(unittest.TestCase):
		# it far too much.

		gan = ExampleWGAN(learning_rate=0.01, gradient_penalty=0.1)
		gan.fit_gan(
		generate_data(gan, 1000, 100),
		generator_steps=0.1,
		checkpoint_interval=0)
		gan.fit_gan(generate_data(gan, 1000, 100), generator_steps=0.1)

		# See if it has done a plausible job of learning the distribution.

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