I am trying to implement an autoencoder for prediction of multiple labels using Keras. This is a snippet:
input = Input(shape=(768,)) hidden1 = Dense(512, activation='relu')(input) compressed = Dense(256, activation='relu', activity_regularizer=l1(10e-6))(hidden1) hidden2 = Dense(512, activation='relu')(compressed) output = Dense(768, activation='sigmoid')(hidden2) # sigmoid is used because output of autoencoder is a set of probabilities model = Model(input, output) model.compile(optimizer='adam', loss='categorical_crossentropy') # categorical_crossentropy is used because it's prediction of multiple labels history = model.fit(x_train, x_train, epochs=100, batch_size=50, validation_split=0.2)
I ran this in Jupyter Notebook (CPU) and I am getting loss and validation loss as:
loss: 193.8085 - val_loss: 439.7132
but when I ran it in Google Colab (GPU), I am getting very high loss and validation loss:
loss: 28383285849773932.0000 - val_loss: 26927464965996544.0000.
What could be the reason for this behavior?