def __init__(self,
root="../data/mnist.npz",
train=True,
transform=None,
target_transform=None):
self.root = root
self.transform = transform
self.target_transform = target_transform
self.train = train
(self.train_data,
self.train_labels), (self.test_data,
self.test_labels) = reader.read_mnist(root)
from keras import models as KModels
from keras import layers as KLayers
from keras import initializers as KInits
from keras import optimizers as KOpts
import numpy as np
import pickle
from matplotlib import pyplot as plt
PHRASE = "TRAIN"
cfg = config.MNISTConfig()
if PHRASE == "TRAIN":
np.random.seed(1000)
randomDim = 100
(X_train, y_train), (X_test, y_test) = reader.read_mnist("../data/mnist.npz")
X_train = (X_train.astype(np.float32) - 127.5)/127.5
X_train = X_train.reshape(60000, 784)
adam = KOpts.Adam(lr=0.0002, beta_1=0.5)
def build_generator(randomDim):
model = KModels.Sequential()
model.add(KLayers.Dense(128 * 7 * 7, input_dim=randomDim)) # ?,100 => ?, 128*7*7
model.add(KLayers.LeakyReLU(alpha=0.2))
model.add(KLayers.Reshape((7,7,128))) # ?, 128*7*7 => ?,7,7,128
model.add(KLayers.BatchNormalization(momentum=0.8))
model.add(KLayers.UpSampling2D()) # ?,7,7,128 => ?,14,14,128
model.add(KLayers.Conv2D(128, kernel_size=3, padding='same')) # ?,14,14,128 => ?,14,14,128
model.add(KLayers.LeakyReLU(alpha=0.2))
onehot = np.eye(10)
temp_z_ = np.random.normal(0, 1, (10, 100))
fixed_z_ = temp_z_
fixed_y_ = np.zeros((10, 1))
for i in range(9):
fixed_z_ = np.concatenate([fixed_z_, temp_z_], 0)
temp = np.ones((10, 1)) + i
fixed_y_ = np.concatenate([fixed_y_, temp], 0)
fixed_y_ = onehot[fixed_y_.astype(np.int32)].squeeze()
batch_size = 100
lr = 0.0002
train_epoch = 10
adam = KOpts.Adam(lr=0.0002, beta_1=0.5)
(x_train, y_train), (x_test, y_test) = reader.read_mnist()
train_set = (x_train - 0.5) / 0.5
train_label = y_train
discriminator = build_discriminator()
discriminator.trainable = False
ganInput = KLayers.Input(shape=(100, ))
ganInputlabel = KLayers.Input(shape=(10, ))
generator = build_generator()
x = generator([ganInput, ganInputlabel])
ganOutput = discriminator([x, ganInputlabel])
gan = KModels.Model(inputs=[ganInput, ganInputlabel], outputs=ganOutput)
gan.compile(loss='binary_crossentropy', optimizer=adam)
'binary_crossentropy', 'categorical_crossentropy', gaussian_loss
],
optimizer=adam)
return model
def sample_generator_input(batch_size):
sampled_noise = np.random.normal(0, 1, (batch_size, 62))
sampled_labels = np.random.randint(0, 10, batch_size).reshape(-1, 1)
sampled_labels = KUtils.to_categorical(sampled_labels, num_classes=10)
sampled_cont = np.random.uniform(-1, 1, size=(batch_size, 2))
return sampled_noise, sampled_labels, sampled_cont
adam = KOpts.Adam(lr=0.0002, beta_1=0.5)
epochs = 6000
batch_size = 100
(x_train, y_train), (_, _) = reader.read_mnist()
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)
discriminator = build_discriminator()
discriminator.trainable = False
ganInput = KLayers.Input(shape=(74, ))
generator = build_generator()
x = generator(ganInput)
valid, target_label, target_count = discriminator(x)
gan = KModels.Model(inputs=ganInput,
outputs=[valid, target_label, target_count])
gan.compile(loss=[
discriminator = build_discriminator()
discriminator.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
encoded_repr, reconstructed_img = generator(img)
discriminator.trainable = False
validity = discriminator(encoded_repr)
adversarial_autoencoder = Model(img, [reconstructed_img, validity])
adversarial_autoencoder.compile(loss=['mse', 'binary_crossentropy'],
loss_weights=[0.999, 0.001],
optimizer=optimizer)
(x_train, y_train), (x_test, y_test) = reader.read_mnist('../data/mnist.npz')
x_train = (x_train.astype(np.float32) - 127.5) / 127.5
x_train = np.expand_dims(x_train, axis=3)
half_batch = int(cfg.BATCH_SIZE / 2)
samples_image = []
progressBar = bar.ProgressBarGAN(
1, cfg.EPOCH_NUM, "D loss: %.3f, acc: %.2f%% - G loss: %.3f, mse: %.2f")
for epoch in range(cfg.EPOCH_NUM):
# Select a random half batch of images
idx = np.random.randint(0, x_train.shape[0], half_batch)
imgs = x_train[idx]
# Generate a half batch of new images
latent_fake, gen_imgs = generator.predict(imgs)