yapf (cee61b48) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/gan.py

+20 −7

Original line number	Diff line number	Diff line
		@@ -75,6 +75,8 @@ class GAN(TensorGraph):
		the number of discriminators to include
		"""
		super(GAN, self).__init__(use_queue=False, **kwargs)
		self.n_generators = n_generators
		self.n_discriminators = n_discriminators

		# Create the inputs.

		@@ -151,8 +153,9 @@ class GAN(TensorGraph):
		discrim_losses = []
		for i in range(n_discriminators):
		for j in range(n_generators):
		discrim_losses.append(self.create_discriminator_loss(self.discrim_train[i],
		self.discrim_gen[i*n_generators+j]))
		discrim_losses.append(
		self.create_discriminator_loss(
		self.discrim_train[i], self.discrim_gen[i * n_generators + j]))
		if n_generators == 1 and n_discriminators == 1:
		total_gen_loss = gen_losses[0]
		total_discrim_loss = discrim_losses[0]
		@@ -166,16 +169,25 @@ class GAN(TensorGraph):

		# Compute the weighted errors

		weight_products = layers.Reshape((n_generatorsn_discriminators,), in_layers=layers.Reshape((n_discriminators, 1), in_layers=discrim_weights) layers.Reshape((1, n_generators), in_layers=gen_weights))
		total_gen_loss = layers.WeightedError((layers.Stack(gen_losses, axis=0), weight_products))
		total_discrim_loss = layers.WeightedError((layers.Stack(discrim_losses, axis=0), weight_products))
		weight_products = layers.Reshape(
		(n_generators * n_discriminators,),
		in_layers=layers.Reshape(
		(n_discriminators,
		1), in_layers=discrim_weights) * layers.Reshape(
		(1, n_generators), in_layers=gen_weights))
		total_gen_loss = layers.WeightedError((layers.Stack(gen_losses, axis=0),
		weight_products))
		total_discrim_loss = layers.WeightedError((layers.Stack(
		discrim_losses, axis=0), weight_products))
		gen_layers.add(gen_alpha)
		discrim_layers.add(gen_alpha)
		discrim_layers.add(discrim_alpha)

		# Add an entropy term to the loss.

		entropy = -(layers.ReduceSum(layers.Log(gen_weights))/n_generators + layers.ReduceSum(layers.Log(discrim_weights))/n_discriminators)
		entropy = -(
		layers.ReduceSum(layers.Log(gen_weights)) / n_generators +
		layers.ReduceSum(layers.Log(discrim_weights)) / n_discriminators)
		total_discrim_loss += entropy

		# Create submodels for training the generators and discriminators.
		@@ -464,7 +476,8 @@ class GAN(TensorGraph):
		batch[self.noise_input] = noise_input
		for layer, value in zip(self.conditional_inputs, conditional_inputs):
		batch[layer] = value
		return self.predict_on_generator([batch], outputs=self.generators[generator_index])
		return self.predict_on_generator(
		[batch], outputs=self.generators[generator_index])

		def _set_empty_inputs(self, feed_dict, layers):
		"""Set entries in a feed dict corresponding to a batch size of 0."""

deepchem/models/tensorgraph/tests/test_gan.py

+2 −1

Original line number	Diff line number	Diff line
		@@ -73,7 +73,8 @@ class TestGAN(unittest.TestCase):

		means = 10 * np.random.random([1000, 1])
		for i in range(2):
		values = gan.predict_gan_generator(conditional_inputs=[means], generator_index=i)
		values = gan.predict_gan_generator(
		conditional_inputs=[means], generator_index=i)
		deltas = values - means
		assert abs(np.mean(deltas)) < 1.0
		assert np.std(deltas) > 1.0

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