CODEWITHSUNDEEP
pythonkerastensor
Chan
Chan

Reputation: 4301

How to get a tensor value in Keras?

I want to compare 2 images.

The approach I adopt is to encode them.

The angle between the two encoded vectors is then calculated for similarity measure.

The code below is used to encode and then decode images using CNN with Keras.

However, I need to get the value of the tensor encoded.

How to achieve it?

Thank you very much.

from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D
from keras.models import Model
from keras import backend as K

input_img = Input(shape=(28, 28, 1))  
x = Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
#----------------------------------------------------------------#
# How to get the values of the tensor "encoded"?                   #
#----------------------------------------------------------------#
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

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

.....

autoencoder.fit(x_train, x_train,
                epochs=50,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test, x_test),
                callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

Upvotes: 2

Views: 3909

Answers (2)

Daniel Tremer
Daniel Tremer

Reputation: 200

If I understand your question correctly you would like to get the 128 dimensional encoded representation, from the convolutional autoencoder, for image comparison?

What you could do is create a reference on the encoder part of the network, train the whole autoencoder and then encode the images with the weights of the encoder reference.

Put this:

self.autoencoder = autoencoder self.encoder = Model(inputs=self.autoencoder.input, outputs=self.autoencoder.get_layer('encoded').output)

after autoencoder.compile()

and create encodings with:

encoded_img = self.encoder.predict(input)

Upvotes: 2

nuric
nuric

Reputation: 11225

In order to get an intermediate output, you need to create a separate model that contains the computation graph up to that point. In your case, you can:

encoder = Model(input_img, encoded)

After training with autoencoder is complete, you can encoder.predict which will return you the intermediate encoded result. You can also save the models separately as you would any other model and not have to train every time. In short, a Model is container for layers that construct a computation graph.

Upvotes: 3

Related Questions