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I’m encountering difficulties in training a deep recurrent neural network (RNN) using TensorFlow for a natural language processing (NLP) project. My goal is to generate coherent text based on an input text dataset.

Here’s a snippet of my implementation:

import tensorflow as tf

Define the RNN model architecture

def build_rnn_model():
# Define the LSTM layer
lstm_layer = tf.keras.layers.LSTM(units=256, return_sequences=True)

# Define the output layer
output_layer = tf.keras.layers.Dense(vocabulary_size)

# Combine the layers into a sequential model
model = tf.keras.Sequential([lstm_layer, output_layer])

return model

Initialize the RNN model

rnn_model = build_rnn_model()

Compile the model

rnn_model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’)`

Despite my efforts, I’m facing the following challenges:

Training Divergence: The training loss seems to diverge rapidly instead of converging towards a minimum value. I’ve experimented with adjusting the learning rate and other hyperparameters, but haven’t been able to stabilize the training process.

Incoherent Text Generation: When attempting to generate text from the trained model, the generated text often lacks coherence and proper grammatical structure. This occurs even after extensive training and hyperparameter tuning.

I’m seeking advanced strategies to address these challenges and enhance the performance and quality of text generation with the RNN. Are there specific preprocessing techniques, network architectures, or regularization methods that could be beneficial in this context?

Any insights, advice, or shared experiences would be greatly appreciated in resolving these challenges and improving the effectiveness of my RNN implementation with TensorFlow for text generation.

I’m encountering difficulties in training a deep recurrent neural network (RNN) using TensorFlow for a natural language processing (NLP) project. My goal is to generate coherent text based on an input text dataset.

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edited Mar 3 at 16:34
Maria K’s user avatar
Maria K
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asked Feb 28 at 8:43
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Your question, as written, is off-topic on Stack Overflow. Seeking strategies/advice/insights/shared opinions is all worded as if you are looking for a discussion about text generation with RNNs, and are not looking for an answer to a specific problem. You should probably take a look at How to Ask an

Incoherent Text Generation: When attempting to generate text from the trained model, the generated text often lacks coherence and proper grammatical structure. This occurs even after extensive training and hyperparameter tuning.

I’m seeking advanced strategies to address these challenges and enhance the performance and quality of text generation with the RNN. Are there specific preprocessing techniques, network architectures, or regularization methods that could be beneficial in this context?