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I am doing the convolutional autoencoder tutorial written by the author of the Keras library: https://blog.keras.io/building-autoencoders-in-keras.html
However, when I launch exactly the same code, and analyse the network's architecture with summary(), it seems that the output size is not compatible with the input one (necessary in case of autoencoders). Here is the output of summary():
**____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 1, 28, 28) 0
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 16, 28, 28) 160 input_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 16, 14, 14) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 8, 14, 14) 1160 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D) (None, 8, 7, 7) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, 8, 7, 7) 584 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
maxpooling2d_3 (MaxPooling2D) (None, 8, 3, 3) 0 convolution2d_3[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, 8, 3, 3) 584 maxpooling2d_3[0][0]
____________________________________________________________________________________________________
upsampling2d_1 (UpSampling2D) (None, 8, 6, 6) 0 convolution2d_4[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, 8, 6, 6) 584 upsampling2d_1[0][0]
____________________________________________________________________________________________________
upsampling2d_2 (UpSampling2D) (None, 8, 12, 12) 0 convolution2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, 16, 10, 10) 1168 upsampling2d_2[0][0]
____________________________________________________________________________________________________
upsampling2d_3 (UpSampling2D) (None, 16, 20, 20) 0 convolution2d_6[0][0]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D) (None, 1, 20, 20) 145 upsampling2d_3[0][0]
====================================================================================================
Total params: 4385
____________________________________________________________________________________________________**
486
Autoencoders are a deep learning model for transforming data from a high-dimensional space to a lower-dimensional space. They work by encoding the data, whatever its size, to a 1-D vector. This vector can then be decoded to reconstruct the original data (in this case, an image).
Autoencoders are a class of Unsupervised Networks that consist of two major networks: Encoders and Decoders. An Unsupervised Network is a network that learns patterns from data without any training labels. The network finds its patterns in the data without being told what the patterns should be.
Essentially, an autoencoder learns a clustering of the data. In contrast, the term CNN refers to a type of neural network which uses the convolution operator (often the 2D convolution when it is used for image processing tasks) to extract features from the data.
CNN also can be used as an autoencoder for image noise reduction or coloring. When CNN is used for image noise reduction or coloring, it is applied in an Autoencoder framework, i.e, the CNN is used in the encoding and decoding parts of an autoencoder.
Please notice that you are missing a border_mode option in pre-last convolution layer.
from keras.layers import Input, Dense, Convolution2D, MaxPooling2D, UpSampling2D
from keras.models import Model
input_img = Input(shape=(1, 28, 28))
x = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(input_img)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
x = MaxPooling2D((2, 2), border_mode='same')(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
encoded = MaxPooling2D((2, 2), border_mode='same')(x)
# at this point the representation is (8, 4, 4) i.e. 128-dimensional
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(8, 3, 3, activation='relu', border_mode='same')(x)
x = UpSampling2D((2, 2))(x)
x = Convolution2D(16, 3, 3, activation='relu', border_mode='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Convolution2D(1, 3, 3, activation='sigmoid', border_mode='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')
This should work fine
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