All credits to: https://blog.keras.io/building-autoencoders-in-keras.html. The following code is a mere rearrangement of the code from the great tutorial above.
In [1]:
from keras.layers import Input, Dense
from keras.models import Model
In [2]:
# this is the size of our encoded representations
encoding_dim = 32 # 32 floats -> compression of factor 24.5, assuming the input is 784 floats
# this is our input placeholder
input_img = Input(shape=(784,))
# "encoded" is the encoded representation of the input
encoded = Dense(encoding_dim, activation='relu')(input_img)
# "decoded" is the lossy reconstruction of the input given the encoded representation
decoded = Dense(784, activation='sigmoid')(encoded)
# this model maps an input to its reconstruction (whole thing)
autoencoder = Model(input_img, decoded)
In [3]:
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
In [4]:
from keras.datasets import mnist
import numpy as np
(x_train, _), (x_test, _) = mnist.load_data() #y_train and y_test not needed!
num_pixels = x_train.shape[1] * x_train.shape[2]
x_train = x_train.reshape((len(x_train), num_pixels))
x_test = x_test.reshape((len(x_test), num_pixels))
#Normalize
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
In [5]:
autoencoder.fit(x_train, x_train,
epochs=50,
batch_size=256,
shuffle=True,
validation_data=(x_test, x_test))
Out[5]:
In [6]:
decoded_imgs = autoencoder.predict(x_test)
In [7]:
import matplotlib.pyplot as plt
n = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
which = np.random.randint(1, len(x_test[0]))
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[which].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
plt.imshow(decoded_imgs[which].reshape(28, 28))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()