【Python】 KerasでConditional DCGANを実装する
前回DCGANを実装しましたが、今回はConditional DCGAN（[1411.1784] Conditional Generative Adversarial Nets）を実装します。
DCGANの例は入力からどのような数字が生成されるかコントロールできませんでしたが、Conditional DCGANは付加情報を足すことで生成する数字をコントロールできるようになります（下図のyが付加情報）。
実装にあたりzとyはベクトルなのでGenerator側の実装はイメージが付くのですが、xは画像なのでDiscriminator側の実装が分かりませんでした。そんな時に参考になったのが下記サイトでした。
--- 学習環境 --- Windows10 Home Python 3.5.2 Keras 2.1.2 tensorflow-gpu 1.2.0
実装に移りますが、以下をconditional_dcgan.pyに記述します。
import numpy as np import keras from keras.layers import Input, Dense, Activation, BatchNormalization, Reshape, UpSampling2D, Conv2D, MaxPool2D, Flatten, concatenate, multiply from keras.models import Model class Generator(object): def __init__(self, latent_dim, condition_dim): # latent vector input generator_input1 = Input(shape=(latent_dim,)) # condition input generator_input2 = Input(shape=(condition_dim,)) # concat 2 inputs generator_input = concatenate([generator_input1, generator_input2]) x = Dense(1024)(generator_input) x = Activation('tanh')(x) x = Dense(128*7*7)(x) x = BatchNormalization()(x) x = Activation('tanh')(x) x = Reshape((7, 7, 128))(x) x = UpSampling2D(size=(2, 2))(x) x = Conv2D(64, 5, padding='same')(x) x = Activation('tanh')(x) x = UpSampling2D(size=(2, 2))(x) x = Conv2D(1, 5, padding='same')(x) x = Activation('tanh')(x) # pass condition input to output so we can give it to discriminator self.generator = Model(inputs=[generator_input1, generator_input2], outputs=[x, generator_input2]) def get_model(self): return self.generator class Discriminator(object): def __init__(self, height, width, channels, condition_dim): # real or fake image discriminator_input1 = Input(shape=(height, width, channels)) # condition input from generator discriminator_input2 = Input(shape=(condition_dim,)) # expand dimension from (batch, channel) to (batch, height, width, channel) di2 = Reshape((1, 1, condition_dim))(discriminator_input2) # expand height and width from (1, 1) to (height, width) di2 = UpSampling2D((height, width))(di2) # concat 2 inputs discriminator_input = concatenate([discriminator_input1, di2]) x = Conv2D(64, 5, padding='same')(discriminator_input) x = Activation('tanh')(x) x = MaxPool2D()(x) x = Conv2D(128, 5)(x) x = Activation('tanh')(x) x = MaxPool2D()(x) x = Flatten()(x) x = Dense(1024)(x) x = Activation('tanh')(x) x = Dense(1, activation='sigmoid')(x) self.discriminator = Model(inputs=[discriminator_input1, discriminator_input2], outputs=x) def get_model(self): return self.discriminator class ConditionalDCGAN(object): def __init__(self, latent_dim, height, width, channels, condition_dim): # set generator self._latent_dim = latent_dim g = Generator(latent_dim, condition_dim) self._generator = g.get_model() # set discriminator d = Discriminator(height, width, channels, condition_dim) self._discriminator = d.get_model() # compile discriminator discriminator_optimizer = keras.optimizers.SGD(lr=0.0005, momentum=0.9, nesterov=True) self._discriminator.compile(optimizer=discriminator_optimizer, loss='binary_crossentropy') # disable training when combined with generator self._discriminator.trainable = False # set DCGAN dcgan_input1 = Input(shape=(latent_dim,)) dcgan_input2 = Input(shape=(condition_dim,)) dcgan_output = self._discriminator(self._generator([dcgan_input1, dcgan_input2])) self._dcgan = Model([dcgan_input1, dcgan_input2], dcgan_output) # compile DCGAN dcgan_optimizer = keras.optimizers.SGD(lr=0.0005, momentum=0.9, nesterov=True) self._dcgan.compile(optimizer=dcgan_optimizer, loss='binary_crossentropy') def train(self, real_images, conditions, batch_size): # Train discriminator so it can detect fake random_latent_vectors = np.random.normal(size=(batch_size, self._latent_dim)) generated_images = self._generator.predict([random_latent_vectors, conditions]) labels = np.ones((batch_size, 1)) labels += 0.05 * np.random.random(labels.shape) d_loss1 = self._discriminator.train_on_batch(generated_images, labels) # Train discriminator so it can detect real labels = np.zeros((batch_size, 1)) labels += 0.05 * np.random.random(labels.shape) d_loss2 = self._discriminator.train_on_batch([real_images, conditions], labels) d_loss = (d_loss1 + d_loss2)/2.0 # Train generator so it can fool discriminator random_latent_vectors = np.random.normal(size=(batch_size, self._latent_dim)) misleading_targets = np.zeros((batch_size, 1)) g_loss = self._dcgan.train_on_batch([random_latent_vectors, conditions], misleading_targets) return d_loss, g_loss def predict(self, latent_vector, condition): # return only image (remember generator returns condition too) return self._generator.predict([latent_vector, condition])[0] def load_weights(self, file_path, by_name=False): self._dcgan.load_weights(file_path, by_name) def save_weights(self, file_path, overwrite=True): self._dcgan.save_weights(file_path, overwrite)
次に以下をmain.pyに記述します（予めmain.pyと同じフォルダにgeneratedというフォルダを作成してください）。
import os import numpy as np import keras from conditional_dcgan import ConditionalDCGAN from keras.preprocessing import image from keras.utils.np_utils import to_categorical def normalize(X): return (X - 127.5)/127.5 def denormalize(X): return (X + 1.0)*127.5 def train(latent_dim, height, width, channels, num_class): (X_train, Y_train), (_, _) = keras.datasets.mnist.load_data() Y_train = to_categorical(Y_train, num_class) X_train = X_train.reshape((X_train.shape[0],) + (height, width, channels)).astype('float32') X_train = normalize(X_train) epochs = 50 batch_size = 128 iterations = X_train.shape[0]//batch_size dcgan = ConditionalDCGAN(latent_dim, height, width, channels, num_class) for epoch in range(epochs): for iteration in range(iterations): real_images = X_train[iteration*batch_size:(iteration+1)*batch_size] conditions = Y_train[iteration*batch_size:(iteration+1)*batch_size] d_loss, g_loss = dcgan.train(real_images, conditions, batch_size) if (iteration + 1)%10 == 0: print('discriminator loss:', d_loss) print('generator loss:', g_loss) print() with open('loss.txt', 'a') as f: f.write(str(d_loss) + ',' + str(g_loss) + '\r') if (epoch + 1)%5 == 0: dcgan.save_weights('gan' + '_epoch' + str(epoch + 1) + '.h5') random_latent_vectors = np.random.normal(size=(batch_size, latent_dim)) generated_images = dcgan.predict(random_latent_vectors, conditions) for i, generated_image in enumerate(generated_images): img = denormalize(generated_image) img = image.array_to_img(img, scale=False) condition = np.argmax(conditions[i]) img.save(os.path.join('generated', str(epoch) + '_' + str(condition) + '.png')) print('epoch' + str(epoch) + ' end') print() def predict(latent_dim, height, width, channels, num_class): dcgan = ConditionalDCGAN(latent_dim, height, width, channels, num_class) dcgan.load_weights('gan_epoch50.h5') for num in range(num_class): for id in range(10): random_latent_vectors = np.random.normal(size=(1, latent_dim)) condition = np.zeros((1, num_class), dtype=np.float32) condition[0, num] = 1 generated_images = dcgan.predict(random_latent_vectors, condition) img = image.array_to_img(denormalize(generated_images[0]), scale=False) img.save(os.path.join('generated', str(num) + '_' + str(id) + '.png')) if __name__ == '__main__': latent_dim = 100 height = 28 width = 28 channels = 1 num_class = 10 train(latent_dim, height, width, channels, num_class) predict(latent_dim, height, width, channels, num_class)
生成された画像は以下のようになります。conditionを付加したことで生成する文字をコントロールできています。
最後に学習時のDiscriminatorとGeneratorのロス値の推移も貼っておきます。
