autoencoder

i'm trying to adapt the 2d convolutional autoencoder example from the keras website: https://blog.keras.io/building-autoencoders-in-keras.html to my own case where i use 1d inputs: from keras.layers import Input, Dense, Conv1D, MaxPooling1D, UpSampling1D from keras.models import Model from keras import backend as K import scipy as scipy import numpy as np mat = scipy.io.loadmat('edata.mat') emat = mat['edata'] input_img = Input(shape=(64,1)) # adapt this if using `channels_first` image data format x = Conv1D(32, (9), activation='relu', padding='same')(input_img) x = MaxPooling1D((4), padding=

I'm working with the Keras framework and I would like to implement an epoch dependent loss function (i.e the loss function isn't the same at each epoch) How would you do that ? Can you add an example, for instance based on the keras VAE tutorial ? Thank you for your help This can be accomplished by recompiling the network. The weights are saved not changed by the recompilation. So in essence something like this: for epoch in range(nb_epoch): loss_function = loss_for_epoch(epoch) model.compile(optimizer, loss_function, metrics) model.fit(X, y, nb_epoch=1) 来源： https://stackoverflow.com/questions

This is a follow-up question for this question Keras backend mean function: " 'float' object has no attribute 'dtype' "? I am trying to make a new regularizer for Keras. Here is my code import keras from keras import initializers from keras.models import Model, Sequential from keras.layers import Input, Dense, Activation from keras import regularizers from keras import optimizers from keras import backend as K kullback_leibler_divergence = keras.losses.kullback_leibler_divergence def kl_divergence_regularizer(inputs): means = K.mean((inputs)) rho=0.05 down = 0.05 * K.ones_like(means) up = (1 -