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ssgan.py
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ssgan.py
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from __future__ import print_function, division
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply, GaussianNoise
from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D
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 import losses
from keras.utils import to_categorical
import keras.backend as K
import matplotlib.pyplot as plt
import numpy as np
class SGAN:
def __init__(self):
self.img_rows = 28
self.img_cols = 28
self.channels = 1
self.img_shape = (self.img_rows, self.img_cols, self.channels)
self.num_classes = 10
self.latent_dim = 100
optimizer = Adam(0.0002, 0.5)
# Build and compile the discriminator
self.discriminator = self.build_discriminator()
self.discriminator.compile(
loss=['binary_crossentropy', 'categorical_crossentropy'],
loss_weights=[0.5, 0.5],
optimizer=optimizer,
metrics=['accuracy']
)
# Build the generator
self.generator = self.build_generator()
# The generator takes noise as input and generates imgs
noise = Input(shape=(100,))
img = self.generator(noise)
# For the combined model we will only train the generator
self.discriminator.trainable = False
# The valid takes generated images as input and determines validity
valid, _ = self.discriminator(img)
# The combined model (stacked generator and discriminator)
# Trains generator to fool discriminator
self.combined = Model(noise, valid)
self.combined.compile(loss=['binary_crossentropy'], optimizer=optimizer)
def build_generator(self):
model = Sequential()
model.add(Dense(128 * 7 * 7, activation="relu", input_dim=self.latent_dim))
model.add(Reshape((7, 7, 128)))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(1, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
model.summary()
noise = Input(shape=(self.latent_dim,))
img = model(noise)
return Model(noise, img)
def build_discriminator(self):
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2, input_shape=self.img_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(BatchNormalization(momentum=0.8))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Flatten())
model.summary()
img = Input(shape=self.img_shape)
features = model(img)
valid = Dense(1, activation="sigmoid")(features)
label = Dense(self.num_classes+1, activation="softmax")(features)
return Model(img, [valid, label])
#
def train(self, iterations=2000, batch_size=32, interval=100):
# Load the dataset
(X_train, y_train), (_, _) = mnist.load_data()
# 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)
#60000枚から10000枚に
X_train=X_train[0:10000]
y_train=y_train[0:10000]
# Adversarial ground truths
real = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for iteration in range(iterations):
# Select a random batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size)
real_imgs = X_train[idx]
# Sample noise and generate a batch of new images
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
gen_imgs = self.generator.predict(noise)#生成された28*28の画像
# One-hot encoding of labels
#実際の画像データの正解ラベル
real_labels = to_categorical(y_train[idx], num_classes=self.num_classes+1)
#生成されたデータの正解ラベル(n+1番目が1)
fake_labels = to_categorical(np.full((batch_size, 1), self.num_classes), num_classes=self.num_classes+1)
imgs=np.concatenate([real_imgs,gen_imgs])#discriminatorへの入力
valid=np.concatenate([real,fake])#入力画像の真偽
labels=np.concatenate([real_labels,fake_labels])#入力画像の正解ラベル
# ---------------------
# Train Discriminator
# ---------------------
d_loss=self.discriminator.train_on_batch(imgs, [valid, labels])
# ---------------------
# Train Generator
# ---------------------
g_loss = self.combined.train_on_batch(noise, real)
# If at save interval => save generated image samples
if iteration % interval == 0:
print ("%d [D loss: %f, acc: %.2f%%, op_acc: %.2f%%] [G loss: %f]" % (iteration, d_loss[0], 100*d_loss[3], 100*d_loss[4], g_loss))
self.sample_images(iteration)
def sample_images(self, iteration):
r, c = 5, 5
noise = np.random.normal(0, 1, (r * c, self.latent_dim))
gen_imgs = self.generator.predict(noise)
# Rescale images 0 - 1
gen_imgs = 0.5 * gen_imgs + 0.5
fig, axs = plt.subplots(r, c)
cnt = 0
for i in range(r):
for j in range(c):
axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')
axs[i,j].axis('off')
cnt += 1
fig.savefig("images/mnist_%d.png" % iteration)
plt.close()
def save_model(self):
def save(model, model_name):
model_path = "saved_model/%s.json" % model_name
weights_path = "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'])
save(self.generator, "mnist_sgan_generator")
save(self.discriminator, "mnist_sgan_discriminator")
save(self.combined, "mnist_sgan_adversarial")
if __name__ == '__main__':
sgan = SGAN()
sgan.train()
sgan.save_model()