CODEWITHSUNDEEP
tensorflowkerasdeep-learningconv-neural-networkautoencoder
Benyamin Jami
Benyamin Jami

Reputation: 13

keras predict only gives 1 but loss value decrease

I'm trying to implement a JSCC autoencoder using Keras on CIfar-10 dataset. but the values of the output image is always just 1.

I'm new to Keras and I didn't find out how to fix this.

model = Sequential()

model.add(Conv2D(16,(5,5),padding = 'same', strides = 2, input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32,(5,5),padding = 'same', strides = 2))
model.add(Activation('relu'))
model.add(Conv2D(32,(5,5),padding = 'same'))
model.add(Activation('relu'))
model.add(Conv2D(32,(5,5),padding = 'same'))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(100))
model.add(Activation('relu'))
model.add(keras.layers.GaussianNoise(0.1))
model.add(Dense(2048))
model.add(Activation('relu'))
model.add(Reshape((8,8,32)))

model.add(Conv2DTranspose(32,(5,5), padding = 'same'))
model.add(Activation('relu'))
model.add(Conv2DTranspose(32,(5,5), padding = 'same'))
model.add(Activation('relu'))
model.add(Conv2DTranspose(32,(5,5), strides = 2 ,padding = 'same'))
model.add(Activation('relu'))
model.add(Conv2DTranspose(3,(5,5), strides = 2 ,padding = 'same'))
model.add(Activation('sigmoid'))

model.compile(loss='mse', optimizer='adam')

model.fit(X_train_norm, X_train_norm,
              batch_size=128,
              epochs=20,
              validation_data=(X_test_norm, X_test_norm),
              shuffle=True)

this model compresses the image to a vector with length of 100 and it adds up with a gaussian noise and then it upsamples the vector to original input.

Train on 50000 samples, validate on 10000 samples
Epoch 1/20
50000/50000 [==============================] - 7s 138us/step - loss: 0.0245 - val_loss: 0.0226
Epoch 2/20
50000/50000 [==============================] - 6s 120us/step - loss: 0.0225 - val_loss: 0.0222
Epoch 3/20
50000/50000 [==============================] - 6s 121us/step - loss: 0.0220 - val_loss: 0.0216
Epoch 4/20
50000/50000 [==============================] - 6s 121us/step - loss: 0.0214 - val_loss: 0.0211
Epoch 5/20
50000/50000 [==============================] - 6s 119us/step - loss: 0.0208 - val_loss: 0.0207
...

>>>model.predict(X_train[:32])
array([[[[1., 1., 1.],
         [1., 1., 1.],
         [1., 1., 1.],
         ...,

Upvotes: 1

Views: 58

Answers (1)

Stewart_R
Stewart_R

Reputation: 14495

You used the normalised data during training but the raw data at prediction.

instead of: 

model.predict(X_train[:32])

use: 

model.predict(X_train_norm[:32])

Upvotes: 1

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