In [12]:
import numpy as np
from keras.datasets import mnist
from keras.layers import Input, Dense
from keras.models import Model
from sklearn import decomposition
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
# this is our input placeholder
input_img = Input(shape=(784,))
n_feature=50
encoded = Dense(n_feature, activation='relu')(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
In [3]:
# We import the training data (MNIST)
(x_train, _), (x_test, _) = mnist.load_data()
# We normalize the data between 0 and 1
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# We reshape the images 28X28 to vectors of 784
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_test.shape[1:])))
In [ ]:
# We train the model
autoencoder.fit(x_train, x_train,epochs=50,batch_size=256,shuffle=True,validation_data=(x_test, x_test))
Epoch 50/50 60000/60000 [==============================] - 4s - loss: 0.0958 - val_loss: 0.0942
In [6]:
decoded_imgs = autoencoder.predict(x_test)
In [8]:
n = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, 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)
# display reconstruction
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()
In [ ]:
# this is our input placeholder
input_img = Input(shape=(784,))
n_feature=2
encoded = Dense(n_feature, activation='relu')(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)
# this model maps an input to its encoded representation
encoder = Model(input_img, encoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
# We import the training data (MNIST)
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# We normalize the data between 0 and 1
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
# We reshape the images 28X28 to vectors of 784
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_test.shape[1:])))
# We train the model
autoencoder.fit(x_train, x_train,epochs=50,batch_size=256,shuffle=True,validation_data=(x_test, x_test))
Epoch 50/50 60000/60000 [==============================] - 3s - loss: 0.2406 - val_loss: 0.2398
In [19]:
encoded_imgs = encoder.predict(x_test)
decoded_imgs = autoencoder.predict(x_test)
plt.scatter(encoded_imgs[:,0],encoded_imgs[:,1],c=y_test,cmap='jet')
plt.colorbar()
plt.title('Deep autoencoder');
In [20]:
n = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test[i].reshape(28, 28),cmap='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[i].reshape(28, 28),cmap='gray')
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
In [42]:
(x_train, _), (x_test, _) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
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_test.shape[1:])))
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., 1.)
x_test_noisy = np.clip(x_test_noisy, 0., 1.)
In [43]:
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),cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
In [ ]:
# Shallow autoencoder
input_img = Input(shape=(784,))
n_feature=50
encoded = Dense(n_feature, activation='relu')(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
# We train the model
autoencoder.fit(x_train_noisy, x_train,epochs=50,batch_size=256,shuffle=True,validation_data=(x_test, x_test))
Epoch 50/50 60000/60000 [==============================] - 4s - loss: 0.1325 - val_loss: 0.3271
In [45]:
decoded_imgs = autoencoder.predict(x_test_noisy)
n = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(2, n, i + 1)
plt.imshow(x_test_noisy[i].reshape(28, 28),cmap='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[i].reshape(28, 28),cmap='gray')
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
In [ ]:
# this is our input placeholder
input_img = Input(shape=(784,))
encoded = Dense(1000, activation='relu')(input_img)
encoded = Dense(500, activation='relu')(encoded)
encoded = Dense(250, activation='relu')(encoded)
encoded = Dense(125, activation='relu')(encoded)
encoded = Dense(2, activation='relu')(encoded)
decoded = Dense(125, activation='relu')(encoded)
decoded = Dense(250, activation='relu')(decoded)
decoded = Dense(500, activation='relu')(decoded)
decoded = Dense(1000, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)
# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)
# this model maps an input to its encoded representation
encoder = Model(input_img, encoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
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_test.shape[1:])))
autoencoder.fit(x_train, x_train,epochs=50,batch_size=256,\
shuffle=True,validation_data=(x_test, x_test))
encoded_imgs = encoder.predict(x_test)
recon_imgs=autoencoder.predict(x_test)
Epoch 50/50 60000/60000 [==============================] - 31s - loss: 0.1860 - val_loss: 0.1863
In [51]:
pca = decomposition.PCA(n_components=2)
pca.fit(x_test)
x_red = pca.transform(x_test)
x_pca = pca.inverse_transform(x_red)
plt.figure()
plt.subplot(1,2,1)
plt.scatter(encoded_imgs[:,0],encoded_imgs[:,1],c=y_test,cmap='jet')
plt.title('Deep autoencoder')
plt.subplot(1,2,2)
plt.scatter(x_red[:,0],x_red[:,1],c=y_test,cmap='jet')
plt.title('PCA')
plt.colorbar()
plt.figure()
n = 10 # how many digits we will display
plt.figure(figsize=(20, 4))
for i in range(n):
# display original
ax = plt.subplot(3, n, i + 1)
plt.imshow(x_test[i].reshape(28, 28),cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(3, n, i + 1 + n)
plt.imshow(recon_imgs[i,:].reshape(28, 28),cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(3, n, i + 1 + 2*n)
plt.imshow(x_pca[i,:].reshape(28, 28),cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)