Since our inputs are images, it makes sense to use convolutional neural networks (convnets) as encoders and decoders. In practical settings, autoencoders applied to images are always convolutional autoencoders --they simply perform much better.
Let's implement one. The encoder will consist in a stack of Conv2D and MaxPooling2D layers (max pooling being used for spatial down-sampling), while the decoder will consist in a stack of Conv2D and UpSampling2D layers.
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from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
import numpy as np
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from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K
input_img = Input(shape=(32, 32, 3)) # adapt this if using `channels_first` image data format
x1 = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x2 = MaxPooling2D((2, 2), padding='same')(x1)
x3 = Conv2D(8, (6, 6), activation='relu', padding='same')(x2)
x4 = MaxPooling2D((2, 2), padding='same')(x3)
x5 = Conv2D(8, (9, 9), activation='relu', padding='same')(x4)
encoded = MaxPooling2D((2, 2), padding='same')(x5)
# at this point the representation is (4, 4, 8) i.e. 128-dimensional
x6 = Conv2D(8, (9, 9), activation='relu', padding='same')(encoded)
x7 = UpSampling2D((2, 2))(x6)
x8 = Conv2D(8, (6, 6), activation='relu', padding='same')(x7)
x9 = UpSampling2D((2, 2))(x8)
x10 = Conv2D(16, (3, 3), activation='relu', padding='same')(x9)
x11 = UpSampling2D((2, 2))(x10)
decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x11)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adagrad', loss='binary_crossentropy')
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from keras.datasets import cifar10
import numpy as np
(x_train, _), (x_test, _) = cifar10.load_data()
x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = np.reshape(x_train, (len(x_train), 32, 32, 3)) # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), 32, 32, 3)) # adapt this if using `channels_first` image data format
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autoencoder.fit(x_train, x_train,
epochs=50,
batch_size=128,
shuffle=True,
validation_data=(x_test, x_test))
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from keras.models import load_model
#autoencoder.save('cifar10_autoencoders.h5') # creates a HDF5 file 'my_model.h5'
#del model # deletes the existing model.
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# returns a compiled model
# identical to the previous one
autoencoder = load_model('cifar10_autoencoders.h5')
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import matplotlib.pyplot as plt
decoded_imgs = autoencoder.predict(x_test)
n = 10
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(32, 32, 3))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + n + 1)
plt.imshow(decoded_imgs[i].reshape(32, 32, 3))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
In [16]:
import matplotlib.pyplot as plt
n = 8
for i in range(n):
fig = plt.figure(figsize=(1,1))
conv_1 = np.asarray(autoencoder.layers[1].get_weights())[0][:,:,0,i]
ax = fig.add_subplot(111)
plt.imshow(conv_1.transpose(), cmap = 'gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
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autoencoder.layers[3].get_weights()
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In [42]:
from keras import backend as K
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# K.learning_phase() is a flag that indicates if the network is in training or
# predict phase. It allow layer (e.g. Dropout) to only be applied during training
inputs = [K.learning_phase()] + autoencoder.inputs
_layer1_f = K.function(inputs, [x2])
def convout1_f(X):
# The [0] is to disable the training phase flag
return _layer1_f([0] + [X])
#_lay_f = K.function(inputs, [x1])
#def convout1_f(X):
# The [0] is to disable the training phase flag
# return _layer1_f([0] + [X])
_layer2_f = K.function(inputs, [x4])
def convout2_f(X):
# The [0] is to disable the training phase flag
return _layer2_f([0] + [X])
_layer3_f = K.function(inputs, [encoded])
def convout3_f(X):
# The [0] is to disable the training phase flag
return _layer3_f([0] + [X])
_up_layer1_f = K.function(inputs, [x6])
def convout4_f(X):
# The [0] is to disable the training phase flag
return _up_layer1_f([0] + [X])
_up_layer2_f = K.function(inputs, [x8])
def convout5_f(X):
# The [0] is to disable the training phase flag
return _up_layer2_f([0] + [X])
_up_layer3_f = K.function(inputs, [x10])
def convout6_f(X):
# The [0] is to disable the training phase flag
return _up_layer3_f([0] + [X])
_up_layer4_f = K.function(inputs, [decoded])
def convout7_f(X):
# The [0] is to disable the training phase flag
return _up_layer4_f([0] + [X])
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x2
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In [45]:
i = 1
x = x_test[i:i+1]
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np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0).shape
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In [81]:
#Plotting conv_1
for i in range(4):
#i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)
temp = x[0,:,:,:]
fig, axes = plt.subplots(1, 1, figsize=(3, 3))
plt.imshow(temp)
plt.show()
k = 0
while k < check.shape[2]:
#plt.figure()
#plt.subplot(231 + i)
fig, axes = plt.subplots(4, 4, figsize=(5, 5))
for i in range(4):
for j in range(4):
axes[i,j].imshow(check[:,:,k], cmap = 'gray')
k += 1
#axes[0, 0].imshow(R, cmap='jet')
#plt.imshow(check[:,:,i])
plt.show()
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check.shape
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i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout2_f(x)),0),0)
check.shape
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In [82]:
#Plotting conv_2
for i in range(4):
#i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)
temp = x[0,:,:,:]
fig, axes = plt.subplots(1, 1, figsize=(3, 3))
plt.imshow(temp)
plt.show()
k = 0
while k < check.shape[2]:
#plt.figure()
#plt.subplot(231 + i)
fig, axes = plt.subplots(2, 4, figsize=(5, 5))
for i in range(2):
for j in range(4):
axes[i,j].imshow(check[:,:,k])
k += 1
#axes[0, 0].imshow(R, cmap='jet')
#plt.imshow(check[:,:,i])
plt.show()
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i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout3_f(x)),0),0)
check.shape
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In [83]:
#Plotting conv_3
for i in range(4):
#i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)
temp = x[0,:,:,:]
fig, axes = plt.subplots(1, 1, figsize=(3, 3))
plt.imshow(temp)
plt.show()
k = 0
while k < check.shape[2]:
#plt.figure()
#plt.subplot(231 + i)
fig, axes = plt.subplots(2, 4, figsize=(5, 5))
for i in range(2):
for j in range(4):
axes[i,j].imshow(check[:,:,k])
k += 1
#axes[0, 0].imshow(R, cmap='jet')
#plt.imshow(check[:,:,i])
plt.show()
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i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout4_f(x)),0),0)
check.shape
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In [84]:
#Plotting conv_4
for i in range(4):
#i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)
temp = x[0,:,:,:]
fig, axes = plt.subplots(1, 1, figsize=(3, 3))
plt.imshow(temp)
plt.show()
k = 0
while k < check.shape[2]:
#plt.figure()
#plt.subplot(231 + i)
fig, axes = plt.subplots(2, 4, figsize=(5, 5))
for i in range(2):
for j in range(4):
axes[i,j].imshow(check[:,:,k])
k += 1
#axes[0, 0].imshow(R, cmap='jet')
#plt.imshow(check[:,:,i])
plt.show()
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i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout5_f(x)),0),0)
check.shape
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In [85]:
#Plotting conv_5
for i in range(4):
#i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)
temp = x[0,:,:,:]
fig, axes = plt.subplots(1, 1, figsize=(3, 3))
plt.imshow(temp)
plt.show()
k = 0
while k < check.shape[2]:
#plt.figure()
#plt.subplot(231 + i)
fig, axes = plt.subplots(2, 4, figsize=(5, 5))
for i in range(2):
for j in range(4):
axes[i,j].imshow(check[:,:,k])
k += 1
#axes[0, 0].imshow(R, cmap='jet')
#plt.imshow(check[:,:,i])
plt.show()
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i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout6_f(x)),0),0)
check.shape
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In [86]:
#Plotting conv_6
for i in range(4):
#i = 3
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout1_f(x)),0),0)
temp = x[0,:,:,:]
fig, axes = plt.subplots(1, 1, figsize=(3, 3))
plt.imshow(temp)
plt.show()
k = 0
while k < check.shape[2]:
#plt.figure()
#plt.subplot(231 + i)
fig, axes = plt.subplots(4, 4, figsize=(5, 5))
for i in range(4):
for j in range(4):
axes[i,j].imshow(check[:,:,k])
k += 1
#axes[0, 0].imshow(R, cmap='jet')
#plt.imshow(check[:,:,i])
plt.show()
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i = 1
x = x_test[i:i+1]
check = np.squeeze(np.squeeze(np.array(convout7_f(x)),0),0)
check.shape
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In [80]:
#Plot final decoded layer
decoded_imgs = autoencoder.predict(x_test)
n = 4
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(32, 32, 3))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# display reconstruction
ax = plt.subplot(2, n, i + n + 1)
plt.imshow(decoded_imgs[i].reshape(32, 32, 3))
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
plt.show()
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