autoencoder

问题 I have a Seq2Seq model. I am interested to print out the matrix value of the output of the encoder per iteration. So for example as the dimension of the matrix in the encoder is (?,20) and the epoch =5 and in each epoch, there are 10 iteration, I would like to see 10 matrix of the dimension (?,20) per epoch . I have gone to several links as here but it still does not print out the value matrix. With this code as mentioned in the aboved link: import keras.backend as K k_value = K.print_tensor

来源： https://stackoverflow.com/questions/63458668/tensorflow-image-dataset-from-directory-for-input-dataset-and-output-dataset

来源： https://stackoverflow.com/questions/63581973/keep-same-dataset-augmentation-for-input-and-output-in-tensorflow

来源： https://stackoverflow.com/questions/63987125/keras-lstm-vae-variational-autoencoder-for-time-series-anamoly-detection

来源： https://stackoverflow.com/questions/63987125/keras-lstm-vae-variational-autoencoder-for-time-series-anamoly-detection

来源： https://stackoverflow.com/questions/63623780/how-to-excluded-layers-weight-and-only-keep-the-weights-i-want-and-predict-model

来源： https://stackoverflow.com/questions/63623780/how-to-excluded-layers-weight-and-only-keep-the-weights-i-want-and-predict-model

问题 So I am trying to build an LSTM based autoencoder, which I want to use for the time series data. These are spitted up to sequences of different lengths. Input to the model has thus shape [None, None, n_features], where the first None stands for number of samples and the second for time_steps of the sequence. The sequences are processed by LSTM with argument return_sequences = False, coded dimension is then recreated by function RepeatVector and ran through LSTM again. In the end I would like

问题 I'm using keras in my ML project with the TensorBoard callback. I have an image autoencoder and I want to visualize its progress in reconstructing some images. So I sub-classed the TensorBoard class as such: class Monitor(TensorBoard): def on_train_begin(self, logs=None): super().on_train_begin(logs) def on_epoch_begin(self, epoch, logs=None): # 1. Get the reconstructed images reconstructions = Autoencoder.predict(validation[0]) # 2. Generate a summary summary = tf.summary.image(

问题 How one construct decoder part of convolutional autoencoder? Suppose I have this (input -> conv2d -> maxpool2d -> maxunpool2d -> convTranspose2d -> output) : # CIFAR images shape = 3 x 32 x 32 class ConvDAE(nn.Module): def __init__(self): super().__init__() # input: batch x 3 x 32 x 32 -> output: batch x 16 x 16 x 16 self.encoder = nn.Sequential( nn.Conv2d(3, 16, 3, stride=1, padding=1), # batch x 16 x 32 x 32 nn.ReLU(), nn.BatchNorm2d(16), nn.MaxPool2d(2, stride=2) # batch x 16 x 16 x 16 ) #