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| * | ||
| !.gitignore |
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| from __future__ import print_function | ||
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| from keras.datasets import mnist | ||
| from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply, concatenate | ||
| from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D, Lambda | ||
| from keras.layers.advanced_activations import LeakyReLU | ||
| from keras.layers.convolutional import UpSampling2D, Conv2D | ||
| from keras.models import Sequential, Model | ||
| from keras.optimizers import Adam | ||
| from keras.utils import to_categorical | ||
| import keras.backend as K | ||
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| import matplotlib.pyplot as plt | ||
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| import numpy as np | ||
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| class INFOGAN(): | ||
| def __init__(self): | ||
| self.img_rows = 28 | ||
| self.img_cols = 28 | ||
| self.channels = 1 | ||
| self.num_classes = 10 | ||
| self.img_shape = (self.img_rows, self.img_cols, self.channels) | ||
| self.latent_dim = 74 | ||
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| optimizer = Adam(0.0002, 0.5) | ||
| losses = ['binary_crossentropy', 'categorical_crossentropy', self.gaussian_loss] | ||
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| # Build and compile the discriminator | ||
| self.discriminator = self.build_discriminator() | ||
| self.discriminator.compile(loss=losses, | ||
| optimizer=optimizer, | ||
| metrics=['accuracy']) | ||
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| # Build and compile the generator | ||
| self.generator = self.build_generator() | ||
| self.generator.compile(loss=['binary_crossentropy'], | ||
| optimizer=optimizer) | ||
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| # The generator takes noise and the target label as input | ||
| # and generates the corresponding digit of that label | ||
| gen_input = Input(shape=(self.latent_dim,)) | ||
| img = self.generator(gen_input) | ||
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| # For the combined model we will only train the generator | ||
| self.discriminator.trainable = False | ||
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| # The discriminator takes generated image as input and determines validity | ||
| # and the label of that image | ||
| valid, target_label, target_cont = self.discriminator(img) | ||
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| # The combined model (stacked generator and discriminator) takes | ||
| # noise as input => generates images => determines validity | ||
| self.combined = Model(gen_input, [valid, target_label, target_cont]) | ||
| self.combined.compile(loss=losses, | ||
| optimizer=optimizer) | ||
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| # Reference: https://github.com/tdeboissiere/DeepLearningImplementations/blob/master/InfoGAN/ | ||
| def gaussian_loss(self, y_true, y_pred): | ||
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| mean = y_pred[0] | ||
| log_stddev = y_pred[1] | ||
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| y_true = y_true[0] | ||
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| epsilon = (y_true - mean) / (K.exp(log_stddev) + K.epsilon()) | ||
| loss = (log_stddev + 0.5 * K.square(epsilon)) | ||
| loss = K.mean(loss) | ||
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| return loss | ||
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| def build_generator(self): | ||
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| model = Sequential() | ||
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| model.add(Dense(1024, activation='relu', input_dim=self.latent_dim)) | ||
| model.add(BatchNormalization(momentum=0.8)) | ||
| model.add(Dense(128 * 7 * 7, activation="relu")) | ||
| model.add(BatchNormalization(momentum=0.8)) | ||
| model.add(Reshape((7, 7, 128))) | ||
| model.add(UpSampling2D()) | ||
| model.add(Conv2D(64, kernel_size=4, padding="same")) | ||
| model.add(Activation("relu")) | ||
| model.add(BatchNormalization(momentum=0.8)) | ||
| model.add(UpSampling2D()) | ||
| model.add(Conv2D(self.channels, kernel_size=4, padding='same')) | ||
| model.add(Activation("tanh")) | ||
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| model.summary() | ||
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| gen_input = Input(shape=(self.latent_dim,)) | ||
| img = model(gen_input) | ||
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| return Model(gen_input, img) | ||
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| def build_discriminator(self): | ||
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| model = Sequential() | ||
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| model.add(Conv2D(64, kernel_size=4, strides=2, input_shape=self.img_shape, padding="same")) | ||
| model.add(LeakyReLU(alpha=0.2)) | ||
| model.add(Dropout(0.25)) | ||
| model.add(Conv2D(128, kernel_size=4, strides=2, padding="same")) | ||
| model.add(LeakyReLU(alpha=0.2)) | ||
| model.add(BatchNormalization(momentum=0.8)) | ||
| model.add(Dropout(0.25)) | ||
| model.add(Flatten()) | ||
| model.add(Dense(1024)) | ||
| model.add(LeakyReLU(alpha=0.2)) | ||
| model.add(BatchNormalization(momentum=0.8)) | ||
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| model.summary() | ||
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| img = Input(shape=self.img_shape) | ||
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| features = model(img) | ||
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| validity = Dense(1, activation="sigmoid")(features) | ||
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| def linmax(x): | ||
| return K.maximum(x, -16) | ||
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| def linmax_shape(input_shape): | ||
| return input_shape | ||
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| label_model = Dense(128)(features) | ||
| label_model = LeakyReLU(alpha=0.2)(label_model) | ||
| label_model = BatchNormalization(momentum=0.8)(label_model) | ||
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| label = Dense(self.num_classes, activation="softmax")(label_model) | ||
| mean = Dense(1, activation="linear")(label_model) | ||
| log_stddev = Dense(1)(label_model) | ||
| log_stddev = Lambda(linmax, output_shape=linmax_shape)(log_stddev) | ||
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| cont = concatenate([mean, log_stddev], axis=1) | ||
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| return Model(img, [validity, label, cont]) | ||
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| def sample_generator_input(self, batch_size): | ||
| # Generator inputs | ||
| sampled_noise = np.random.normal(0, 1, (batch_size, 62)) | ||
| sampled_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1) | ||
| sampled_labels = to_categorical(sampled_labels, num_classes=self.num_classes) | ||
| sampled_cont = np.random.uniform(-1, 1, size=(batch_size, 2)) | ||
| return sampled_noise, sampled_labels, sampled_cont | ||
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| def train(self, epochs, batch_size=128, save_interval=50): | ||
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| # Load the dataset | ||
| (X_train, y_train), (_, _) = mnist.load_data() | ||
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| # Rescale -1 to 1 | ||
| X_train = (X_train.astype(np.float32) - 127.5) / 127.5 | ||
| X_train = np.expand_dims(X_train, axis=3) | ||
| y_train = y_train.reshape(-1, 1) | ||
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| half_batch = int(batch_size / 2) | ||
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| for epoch in range(epochs): | ||
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| # --------------------- | ||
| # Train Discriminator | ||
| # --------------------- | ||
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| # Train discriminator on generator output | ||
| sampled_noise, sampled_labels, sampled_cont = self.sample_generator_input(half_batch) | ||
| gen_input = np.concatenate((sampled_noise, sampled_labels, sampled_cont), axis=1) | ||
| # Generate a half batch of new images | ||
| gen_imgs = self.generator.predict(gen_input) | ||
| fake = np.zeros((half_batch, 1)) | ||
| d_loss_fake = self.discriminator.train_on_batch(gen_imgs, [fake, sampled_labels, sampled_cont]) | ||
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| # Train discriminator on real data | ||
| # Select a random half batch of images | ||
| idx = np.random.randint(0, X_train.shape[0], half_batch) | ||
| imgs = X_train[idx] | ||
| labels = to_categorical(y_train[idx], num_classes=self.num_classes) | ||
| valid = np.ones((half_batch, 1)) | ||
| d_loss_real = self.discriminator.train_on_batch(imgs, [valid, labels, sampled_cont]) | ||
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| # Avg. loss | ||
| d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) | ||
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| # --------------------- | ||
| # Train Generator | ||
| # --------------------- | ||
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| valid = np.ones((batch_size, 1)) | ||
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| # Generator inputs | ||
| sampled_noise = np.random.normal(0, 1, (batch_size, 62)) | ||
| sampled_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1) | ||
| sampled_labels = to_categorical(sampled_labels, num_classes=self.num_classes) | ||
| sampled_cont = np.random.uniform(-1, 1, size=(batch_size, 2)) | ||
| gen_input = np.concatenate((sampled_noise, sampled_labels, sampled_cont), axis=1) | ||
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| # Train the generator | ||
| g_loss = self.combined.train_on_batch(gen_input, [valid, sampled_labels, sampled_cont]) | ||
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| # Plot the progress | ||
| print ("%d [D loss: %.2f, acc.: %.2f%%, label_acc: %.2f%%] [G loss: %.2f]" % (epoch, d_loss[0], 100*d_loss[4], 100*d_loss[5], g_loss[0])) | ||
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| # If at save interval => save generated image samples | ||
| if epoch % save_interval == 0: | ||
| self.save_imgs(epoch) | ||
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| def save_imgs(self, epoch): | ||
| r, c = 10, 10 | ||
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| fig, axs = plt.subplots(r, c) | ||
| for i in range(r): | ||
| sampled_noise = np.random.normal(0, 1, (c, 62)) | ||
| sampled_labels = np.arange(0, 10).reshape(-1, 1) | ||
| sampled_labels = to_categorical(sampled_labels, num_classes=self.num_classes) | ||
| sampled_cont = np.repeat(np.expand_dims(np.linspace(-1, 1, num=c), axis=1), 2, axis=1) | ||
| gen_input = np.concatenate((sampled_noise, sampled_labels, sampled_cont), axis=1) | ||
| gen_imgs = self.generator.predict(gen_input) | ||
| gen_imgs = 0.5 * gen_imgs + 0.5 | ||
| for j in range(c): | ||
| axs[i,j].imshow(gen_imgs[j,:,:,0], cmap='gray') | ||
| axs[i,j].axis('off') | ||
| fig.savefig("./infogan/images/mnist_%d.png" % epoch) | ||
| plt.close() | ||
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| def save_model(self): | ||
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| def save(model, model_name): | ||
| model_path = "infogan/saved_model/%s.json" % model_name | ||
| weights_path = "infogan/saved_model/%s_weights.hdf5" % model_name | ||
| options = {"file_arch": model_path, | ||
| "file_weight": weights_path} | ||
| json_string = model.to_json() | ||
| open(options['file_arch'], 'w').write(json_string) | ||
| model.save_weights(options['file_weight']) | ||
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| save(self.generator, "generator") | ||
| save(self.discriminator, "discriminator") | ||
| save(self.combined, "adversarial") | ||
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| if __name__ == '__main__': | ||
| infogan = INFOGAN() | ||
| infogan.train(epochs=6000, batch_size=32, save_interval=50) | ||
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This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| * | ||
| !.gitignore |
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