Updated GAN example notebook (55d1ff20) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/gan.py

+20 −10

Original line number	Diff line number	Diff line
		@@ -95,9 +95,10 @@ class GAN(KerasModel):
		for i in range(n_generators):
		generator = self.create_generator()
		self.generators.append(generator)
		self.gen_variables += generator.trainable_variables
		generator_outputs.append(
		generator([self.noise_input] + self.conditional_inputs))
		generator(
		_list_or_tensor([self.noise_input] + self.conditional_inputs)))
		self.gen_variables += generator.trainable_variables

		# Create the discriminators.

		@@ -108,14 +109,16 @@ class GAN(KerasModel):
		for i in range(n_discriminators):
		discriminator = self.create_discriminator()
		self.discriminators.append(discriminator)
		self.discrim_variables += discriminator.trainable_variables
		discrim_train_outputs.append(
		discriminator(self.data_inputs + self.conditional_inputs))
		discriminator(
		_list_or_tensor(self.data_inputs + self.conditional_inputs)))
		for gen_output in generator_outputs:
		if isinstance(gen_output, tf.Tensor):
		gen_output = [gen_output]
		discrim_gen_outputs.append(
		discriminator(gen_output + self.conditional_inputs))
		discriminator(
		_list_or_tensor(gen_output + self.conditional_inputs)))
		self.discrim_variables += discriminator.trainable_variables

		# Compute the loss functions.

		@@ -316,8 +319,8 @@ class GAN(KerasModel):
		gen_average_steps = 0
		time1 = time.time()
		if checkpoint_interval > 0:
		manager = tf.train.CheckpointManager(
		self._get_tf('Checkpoint'), self.model_dir, max_checkpoints_to_keep)
		manager = tf.train.CheckpointManager(self._checkpoint, self.model_dir,
		max_checkpoints_to_keep)
		for feed_dict in batches:
		# Every call to fit_generator() will increment global_step, but we only
		# want it to get incremented once for the entire batch, so record the
		@@ -367,7 +370,7 @@ class GAN(KerasModel):
		gen_loss = gen_error / max(1, gen_average_steps)
		print(
		'Ending global_step %d: generator average loss %g, discriminator average loss %g'
		% (self.global_step, gen_loss, discrim_loss))
		% (global_step, gen_loss, discrim_loss))
		discrim_error = 0.0
		gen_error = 0.0
		discrim_average_steps = 0
		@@ -381,7 +384,7 @@ class GAN(KerasModel):
		gen_loss = gen_error / gen_average_steps
		print(
		'Ending global_step %d: generator average loss %g, discriminator average loss %g'
		% (self.global_step, gen_loss, discrim_loss))
		% (global_step, gen_loss, discrim_loss))
		self._exec_with_session(lambda: manager.save())
		time2 = time.time()
		print("TIMING: model fitting took %0.3f s" % (time2 - time1))
		@@ -424,7 +427,8 @@ class GAN(KerasModel):
		inputs = [noise_input]
		inputs += conditional_inputs
		inputs = [i.astype(np.float32) for i in inputs]
		pred = self.generators[generator_index](inputs, training=False)
		pred = self.generators[generator_index](
		_list_or_tensor(inputs), training=False)
		if tf.executing_eagerly():
		pred = pred.numpy()
		else:
		@@ -432,6 +436,12 @@ class GAN(KerasModel):
		return pred


		def _list_or_tensor(inputs):
		if len(inputs) == 1:
		return inputs[0]
		return inputs


		class WGAN(GAN):
		"""Implements Wasserstein Generative Adversarial Networks.

examples/notebooks/MNIST_GAN.ipynb

+49 −35

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