autoencoder/denoisingAutoencoder.py at master · floperry/autoencoder · GitHub

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'''
This is the implementation of denoising autoencoder realized by keras.
The input of the model should be a vector, not a matrix.
The final loss is 0.1265 on the test set after 50 epochs, poorer than
convolution denoising autoencoder.
'''
# coding: utf-8

# In[1]:

from keras.datasets import mnist
from keras.layers import Input, Dense
from keras.models import Model
import matplotlib.pyplot as plt
import numpy as np


# In[2]:

(x_train, _), (x_test, _) = mnist.load_data()
# data preprocessing
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
print(x_train.shape, x_test.shape)


# In[3]:

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


# In[4]:

x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_train.shape[1:])))
print(x_train.shape, x_test.shape)


# In[5]:

noise_factor = 0.5
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, 0.0, 1.0)
x_test_noisy = np.clip(x_test_noisy, 0.0, 1.0)
print(x_train_noisy.shape, x_test_noisy.shape)


# In[6]:

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


# In[12]:

encoding_dim = 32

input_img = Input(shape=(784,))
encoded = Dense(encoding_dim, activation='relu')(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)

autoencoder = Model(inputs=input_img, outputs=decoded)
encoder = Model(inputs=input_img, outputs=decoded)

decoded_input = Input(shape=(encoding_dim,))
decoder_layer = autoencoder.layers[-1]
decoder = Model(inputs=decoded_input, outputs=decoder_layer(decoded_input))


# In[13]:

autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')


# In[14]:

history = autoencoder.fit(x_train_noisy, x_train,
                          batch_size=128,
                          epochs=50,
                          shuffle=True,
                          validation_data=(x_test_noisy, x_test))


# In[15]:

print(history.history.keys())


# In[25]:

plt.figure(figsize=(5, 5))
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.grid()
plt.title('model loss')
plt.xlabel('epochs')
plt.ylabel('loss')
plt.legend(['train', 'test'], loc='upper right')
plt.show()


# In[21]:

x_rec = autoencoder.predict(x_test_noisy)
print(x_rec.shape)


# In[31]:

plt.figure(figsize=(10, 3))
for i in range(n):
    ax = plt.subplot(3, n, i + 1)
    plt.imshow(x_test[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)

    ax = plt.subplot(3, n, 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(3, n, 2 * n + i + 1)
    plt.imshow(x_rec[i].reshape(28, 28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()