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Tensorflow - LSTM RNN overfitting more on Dropout
I have developed a LSTM and applied a dropout rate of 0.2 to all layers (including input).
The model has massive overfitting, meaning that its performance on the training sets increases while the perf...
AnonRocketman
Votes: 0
Answers: 0
What is the proper way to implement a dropout layer in a CNN?
Is this proceeding correct?
My intention was to add a dropout layer after concatenation, but to do so i needed to adjust the concat layer's output to the appropriate shape (samples, timesteps, channel...
Luciano Dourado
Votes: 0
Answers: 2
" ValueError: Expecting KerasTensor which is from tf.keras.Input()". Error in prediction with dropout function
I am trying to predict uncertainty in a regression problem using Dropout during testing as per Yarin Gal's article. I created a class using Keras's backend function as provided by this stack overflo...
Anshul Nayak
Votes: 0
Answers: 1
Batchnormalize, Dropout and number of layers
I'm learning batchnormalisation and dropout. Saw this https://www.kaggle.com/ryanholbrook/dropout-and-batch-normalization.
The model
model = keras.Sequential([
layers.Dense(1024, activation='relu...
Data T
Votes: 0
Answers: 1