Category "recurrent-neural-network"

Custom GRU model (OGRU)

I want to customise the GRU-RNN cell from tensorflow. But i dont know which function i need to change from standart GRU from tensorflow. i want to modify GRU ce

Difference between hidden dimension and n_layers in rnn using pytorch

I am stuck between hidden dimension and n_layers. What I understood so far, is that n_layers in the parameters of RNN using pytorch, is number of hidden layers.

How to setup LSTM to use n-grams instead of sequence length?

I currently have an LSTM which uses sequence length as input, but this only allows the LSTM to predict when the input length is equal to the used sequence lengt

Tensorflow error: ValueError: Shapes (128, 100) and (128, 100, 139) are incompatible

I try to use Functional API for my model, but i don't understand why i have error: ValueError: Shapes (128, 100) and (128, 100, 139) are incompatible My code:

I'm using Gradio for making UI for my model

def detect(audio): chime_threshold = 0.5 prediction = detect_triggerword(audio) chime_on_activate(your_filename, prediction, chime_threshold) re

PCA for Recurrent Neural Networks (LSTM) - Shall I use PCA for target variables too?

I have a seasonal timeseries dataset containing 3 target variables and n feature variables. I am trying to apply a PCA algorithm before feeding the data to a si

Tensorflow-addons seq2seq - start and end tokens in BaseDecoder or BasicDecoder

I am writing code inspired from https://www.tensorflow.org/addons/api_docs/python/tfa/seq2seq/BasicDecoder. In the translation/generation we instantiate a Basic

logits and labels must be broadcastable error in Tensorflow RNN

I am new to Tensorflow and deep leaning. I am trying to see how the loss decreases over 10 epochs in my RNN model that I created to read a dataset from kaggle w

which algorithm does google keyboard uses for automatic suggestions (personal vocab included)?

I am confused since google cannnot train their text generation models with each individuals personal vocabulary. I was trying to develop something similar but

How does calculation in a GRU layer take place

So I want to understand exactly how the outputs and hidden state of a GRU cell are calculated. I obtained the pre-trained model from here and the GRU layer has

AttributeError: module 'tensorflow.python.pywrap_tensorflow' has no attribute 'TFE_Py_RegisterExceptionClass'

I am trying to develop some time-series sequence prediction, using the latest resources available. To that end, I did check the example code from TensorFlow tim

LSTM/GRU setting states to random noise instead or resetting to zero

I train the following model based on GRU, note that I am passing the argument stateful=True to the GRU builder. class LearningToSurpriseModel(tf.keras.Model):

making GRU/LSTM states trainable in Tensorflow/Keras and add some random noise

I train the following model based on GRU, note that I am passing the argument stateful=True to the GRU builder. class LearningToSurpriseModel(tf.keras.Model):

Tensorflow LSTM/GRU reset states once per epoch and not for each new batch

I train the following model based on GRU, note that I am passing the argument stateful=True to the GRU builder. class LearningToSurpriseModel(tf.keras.Model):

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