Loading deepchem/models/tensorgraph/models/gan.py +88 −44 Original line number Diff line number Diff line Loading @@ -52,7 +52,7 @@ class GAN(TensorGraph): get_noise_batch() """ def __init__(self, **kwargs): def __init__(self, n_generators=1, n_discriminators=1, **kwargs): """Construct a GAN. This class accepts all the keyword arguments from TensorGraph. Loading @@ -69,40 +69,49 @@ class GAN(TensorGraph): for shape in self.get_conditional_input_shapes(): self.conditional_inputs.append(layers.Feature(shape=shape)) # Create the generator. # Create the generators. self.generator = self.create_generator(self.noise_input, self.generators = [] for i in range(n_generators): generator = self.create_generator(self.noise_input, self.conditional_inputs) if not isinstance(self.generator, Sequence): if not isinstance(generator, Sequence): raise ValueError('create_generator() must return a list of Layers') if len(self.generator) != len(self.data_inputs): if len(generator) != len(self.data_inputs): raise ValueError( 'The number of generator outputs must match the number of data inputs' ) for g, d in zip(self.generator, self.data_inputs): for g, d in zip(generator, self.data_inputs): if g.shape != d.shape: raise ValueError( 'The shapes of the generator outputs must match the shapes of the data inputs' ) for g in self.generator: for g in generator: self.add_output(g) self.generators.append(generator) # Create the discriminator. # Create the discriminators. self.discrim_train = self.create_discriminator(self.data_inputs, self.discrim_train = [] self.discrim_gen = [] for i in range(n_discriminators): discrim_train = self.create_discriminator(self.data_inputs, self.conditional_inputs) self.discrim_train.append(discrim_train) # Make a copy of the discriminator that takes the generator's output as # Make a copy of the discriminator that takes each generator's output as # its input. for generator in self.generators: replacements = {} for g, d in zip(self.generator, self.data_inputs): for g, d in zip(generator, self.data_inputs): replacements[d] = g for c in self.conditional_inputs: replacements[c] = c self.discrim_gen = self.discrim_train.copy(replacements, shared=True) discrim_gen = discrim_train.copy(replacements, shared=True) self.discrim_gen.append(discrim_gen) # Make a list of all layers in the generator and discriminator. # Make a list of all layers in the generators and discriminators. def add_layers_to_set(layer, layers): if layer not in layers: Loading @@ -111,21 +120,50 @@ class GAN(TensorGraph): add_layers_to_set(i, layers) gen_layers = set() for layer in self.generator: for generator in self.generators: for layer in generator: add_layers_to_set(layer, gen_layers) discrim_layers = set() add_layers_to_set(self.discrim_train, discrim_layers) for discriminator in self.discrim_train: add_layers_to_set(discriminator, discrim_layers) discrim_layers -= gen_layers # Create submodels for training the generator and discriminator. # Compute the loss functions. gen_losses = [self.create_generator_loss(d) for d in self.discrim_gen] 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])) if n_generators == 1 and n_discriminators == 1: total_gen_loss = gen_losses[0] total_discrim_loss = discrim_losses[0] else: # Create learnable weights for the generators and discriminators. gen_alpha = layers.Variable(np.ones((n_generators, 1))) gen_weights = layers.SoftMax(gen_alpha) discrim_alpha = layers.Variable(np.ones((n_discriminators, 1))) discrim_weights = layers.SoftMax(discrim_alpha) # Compute the weighted errors 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) self.gen_alpha = gen_alpha self.discrim_alpha = discrim_alpha # Create submodels for training the generators and discriminators. gen_loss = self.create_generator_loss(self.discrim_gen) discrim_loss = self.create_discriminator_loss(self.discrim_train, self.discrim_gen) self.generator_submodel = self.create_submodel( layers=gen_layers, loss=gen_loss) layers=gen_layers, loss=total_gen_loss) self.discriminator_submodel = self.create_submodel( layers=discrim_layers, loss=discrim_loss) layers=discrim_layers, loss=total_discrim_loss) def get_noise_input_shape(self): """Get the shape of the generator's noise input layer. Loading Loading @@ -366,11 +404,14 @@ class GAN(TensorGraph): saver.save(self.session, self.save_file, global_step=self.global_step) time2 = time.time() print("TIMING: model fitting took %0.3f s" % (time2 - time1)) print(self.session.run(self.gen_alpha)) print(self.session.run(self.discrim_alpha)) def predict_gan_generator(self, batch_size=1, noise_input=None, conditional_inputs=[]): conditional_inputs=[], generator_index=0): """Use the GAN to generate a batch of samples. Parameters Loading @@ -386,6 +427,9 @@ class GAN(TensorGraph): conditional_inputs: list of arrays the values to use for all conditional inputs. This must be specified if the GAN has any conditional inputs. generator_index: int the index of the generator (between 0 and n_generators-1) to use for generating the samples. Returns ------- Loading @@ -402,7 +446,7 @@ 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]) 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.""" Loading deepchem/models/tensorgraph/tests/test_gan.py +36 −17 Original line number Diff line number Diff line Loading @@ -20,11 +20,6 @@ def generate_data(gan, batches, batch_size): yield batch class TestGAN(unittest.TestCase): def test_cgan(self): """Test fitting a conditional GAN.""" class ExampleGAN(dc.models.GAN): def get_noise_input_shape(self): Loading @@ -45,6 +40,12 @@ class TestGAN(unittest.TestCase): dense = layers.Dense(10, in_layers=discrim_in, activation_fn=tf.nn.relu) return layers.Dense(1, in_layers=dense, activation_fn=tf.sigmoid) class TestGAN(unittest.TestCase): def test_cgan(self): """Test fitting a conditional GAN.""" gan = ExampleGAN(learning_rate=0.003) gan.fit_gan( generate_data(gan, 5000, 100), Loading @@ -59,6 +60,24 @@ class TestGAN(unittest.TestCase): assert abs(np.mean(deltas)) < 1.0 assert np.std(deltas) > 1.0 def test_mix_gan(self): """Test a GAN with multiple generators and discriminators.""" gan = ExampleGAN(n_generators=2, n_discriminators=2, learning_rate=0.003) gan.fit_gan( generate_data(gan, 5000, 100), generator_steps=0.5, checkpoint_interval=0) # See if it has done a plausible job of learning the distribution. means = 10 * np.random.random([1000, 1]) for i in range(2): 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 @flaky def test_wgan(self): """Test fitting a conditional WGAN.""" Loading Loading
deepchem/models/tensorgraph/models/gan.py +88 −44 Original line number Diff line number Diff line Loading @@ -52,7 +52,7 @@ class GAN(TensorGraph): get_noise_batch() """ def __init__(self, **kwargs): def __init__(self, n_generators=1, n_discriminators=1, **kwargs): """Construct a GAN. This class accepts all the keyword arguments from TensorGraph. Loading @@ -69,40 +69,49 @@ class GAN(TensorGraph): for shape in self.get_conditional_input_shapes(): self.conditional_inputs.append(layers.Feature(shape=shape)) # Create the generator. # Create the generators. self.generator = self.create_generator(self.noise_input, self.generators = [] for i in range(n_generators): generator = self.create_generator(self.noise_input, self.conditional_inputs) if not isinstance(self.generator, Sequence): if not isinstance(generator, Sequence): raise ValueError('create_generator() must return a list of Layers') if len(self.generator) != len(self.data_inputs): if len(generator) != len(self.data_inputs): raise ValueError( 'The number of generator outputs must match the number of data inputs' ) for g, d in zip(self.generator, self.data_inputs): for g, d in zip(generator, self.data_inputs): if g.shape != d.shape: raise ValueError( 'The shapes of the generator outputs must match the shapes of the data inputs' ) for g in self.generator: for g in generator: self.add_output(g) self.generators.append(generator) # Create the discriminator. # Create the discriminators. self.discrim_train = self.create_discriminator(self.data_inputs, self.discrim_train = [] self.discrim_gen = [] for i in range(n_discriminators): discrim_train = self.create_discriminator(self.data_inputs, self.conditional_inputs) self.discrim_train.append(discrim_train) # Make a copy of the discriminator that takes the generator's output as # Make a copy of the discriminator that takes each generator's output as # its input. for generator in self.generators: replacements = {} for g, d in zip(self.generator, self.data_inputs): for g, d in zip(generator, self.data_inputs): replacements[d] = g for c in self.conditional_inputs: replacements[c] = c self.discrim_gen = self.discrim_train.copy(replacements, shared=True) discrim_gen = discrim_train.copy(replacements, shared=True) self.discrim_gen.append(discrim_gen) # Make a list of all layers in the generator and discriminator. # Make a list of all layers in the generators and discriminators. def add_layers_to_set(layer, layers): if layer not in layers: Loading @@ -111,21 +120,50 @@ class GAN(TensorGraph): add_layers_to_set(i, layers) gen_layers = set() for layer in self.generator: for generator in self.generators: for layer in generator: add_layers_to_set(layer, gen_layers) discrim_layers = set() add_layers_to_set(self.discrim_train, discrim_layers) for discriminator in self.discrim_train: add_layers_to_set(discriminator, discrim_layers) discrim_layers -= gen_layers # Create submodels for training the generator and discriminator. # Compute the loss functions. gen_losses = [self.create_generator_loss(d) for d in self.discrim_gen] 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])) if n_generators == 1 and n_discriminators == 1: total_gen_loss = gen_losses[0] total_discrim_loss = discrim_losses[0] else: # Create learnable weights for the generators and discriminators. gen_alpha = layers.Variable(np.ones((n_generators, 1))) gen_weights = layers.SoftMax(gen_alpha) discrim_alpha = layers.Variable(np.ones((n_discriminators, 1))) discrim_weights = layers.SoftMax(discrim_alpha) # Compute the weighted errors 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) self.gen_alpha = gen_alpha self.discrim_alpha = discrim_alpha # Create submodels for training the generators and discriminators. gen_loss = self.create_generator_loss(self.discrim_gen) discrim_loss = self.create_discriminator_loss(self.discrim_train, self.discrim_gen) self.generator_submodel = self.create_submodel( layers=gen_layers, loss=gen_loss) layers=gen_layers, loss=total_gen_loss) self.discriminator_submodel = self.create_submodel( layers=discrim_layers, loss=discrim_loss) layers=discrim_layers, loss=total_discrim_loss) def get_noise_input_shape(self): """Get the shape of the generator's noise input layer. Loading Loading @@ -366,11 +404,14 @@ class GAN(TensorGraph): saver.save(self.session, self.save_file, global_step=self.global_step) time2 = time.time() print("TIMING: model fitting took %0.3f s" % (time2 - time1)) print(self.session.run(self.gen_alpha)) print(self.session.run(self.discrim_alpha)) def predict_gan_generator(self, batch_size=1, noise_input=None, conditional_inputs=[]): conditional_inputs=[], generator_index=0): """Use the GAN to generate a batch of samples. Parameters Loading @@ -386,6 +427,9 @@ class GAN(TensorGraph): conditional_inputs: list of arrays the values to use for all conditional inputs. This must be specified if the GAN has any conditional inputs. generator_index: int the index of the generator (between 0 and n_generators-1) to use for generating the samples. Returns ------- Loading @@ -402,7 +446,7 @@ 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]) 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.""" Loading
deepchem/models/tensorgraph/tests/test_gan.py +36 −17 Original line number Diff line number Diff line Loading @@ -20,11 +20,6 @@ def generate_data(gan, batches, batch_size): yield batch class TestGAN(unittest.TestCase): def test_cgan(self): """Test fitting a conditional GAN.""" class ExampleGAN(dc.models.GAN): def get_noise_input_shape(self): Loading @@ -45,6 +40,12 @@ class TestGAN(unittest.TestCase): dense = layers.Dense(10, in_layers=discrim_in, activation_fn=tf.nn.relu) return layers.Dense(1, in_layers=dense, activation_fn=tf.sigmoid) class TestGAN(unittest.TestCase): def test_cgan(self): """Test fitting a conditional GAN.""" gan = ExampleGAN(learning_rate=0.003) gan.fit_gan( generate_data(gan, 5000, 100), Loading @@ -59,6 +60,24 @@ class TestGAN(unittest.TestCase): assert abs(np.mean(deltas)) < 1.0 assert np.std(deltas) > 1.0 def test_mix_gan(self): """Test a GAN with multiple generators and discriminators.""" gan = ExampleGAN(n_generators=2, n_discriminators=2, learning_rate=0.003) gan.fit_gan( generate_data(gan, 5000, 100), generator_steps=0.5, checkpoint_interval=0) # See if it has done a plausible job of learning the distribution. means = 10 * np.random.random([1000, 1]) for i in range(2): 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 @flaky def test_wgan(self): """Test fitting a conditional WGAN.""" Loading
