Learning-to-Attack-Federated-Learning/denoise.py at main · SliencerX/Learning-to-Attack-Federated-Learning · GitHub

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from keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader, Subset
from sklearn.model_selection import train_test_split
from keras.models import Model, load_model


sample_index = [i for i in range(200)]


#Loading MNIST data
apply_transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST(root='./data', train=True, download=True, transform=apply_transform)


#Loading FashionMNIST data
# apply_transform = transforms.Compose([
#         transforms.ToTensor()],
#         transforms.Normalize((0.2859,), (0.3530,))])
# train_dataset = datasets.FashionMNIST(root='./data', train=True, download=True, transform=apply_transform)

#Loading EMNIST data
#train_dataset = datasets.EMNIST(root='./data', split='balanced', train=True, download=True, transform=transforms.ToTensor())


train_set = Subset(train_dataset, sample_index[:180])
val_set = Subset(train_dataset, sample_index[180:])

x_train = []
x_test = []
for i in train_set:
    x_train.append(i[0].numpy().reshape(28,28,1))

for i in val_set:
    x_test.append(i[0].numpy().reshape(28,28,1))

x_train = np.asarray(x_train)
x_test = np.asarray(x_test)

noise_factor = 0.3

x_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)
x_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)


x_train_noisy = np.clip(x_train_noisy, 0., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)

n = 10
plt.figure(figsize=(20, 2))
for i in range(n):
    ax = plt.subplot(1, n, i+1)
    plt.imshow(x_test_noisy[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model, load_model

input_img = Input(shape=(28, 28, 1,))

x = Conv2D(32, (3, 3), padding='same', activation='relu')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), padding='same', activation='relu')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), padding='same', activation='relu')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), padding='same', activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), padding='same', activation='sigmoid')(x)


autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
autoencoder.fit(x_train_noisy, x_train,
                epochs=10000,
                batch_size=32,
                shuffle=True,
                validation_data=(x_test_noisy, x_test))


autoencoder.save('autoencoder_mnist')
decoded_imgs = autoencoder.predict(x_test_noisy)

n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
    ax = plt.subplot(2, n, i+1)
    plt.imshow(x_test_noisy[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    ax = plt.subplot(2, n, i+1+n)
    plt.imshow(decoded_imgs[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()