well it seems to be doing something atlest becouse all my memory is eaten up
If your code is consuming a significant amount of memory, you can try a few optimization strategies to alleviate the memory usage during training:

1. Use Generators: Instead of loading the entire dataset into memory at once, you can use generators to load data in batches during training. This allows you to process a smaller subset of the dataset at a time, reducing memory consumption. Keras provides the fit_generator() function to train the model using data generators.

2. Reduce the Sequence Length: Since your original sequence length is seq_length, consider reducing it to a smaller value. A shorter sequence length reduces the memory footprint since there are fewer input and target sequences to store during training.

3. Decrease the Batch Size: Reducing the batch size during training can help reduce memory consumption. However, keep in mind that a smaller batch size may affect training stability and convergence. Experiment with different batch sizes to find the optimal balance for your dataset.

4. Use Memory-efficient Data Types: Depending on your dataset size, you can consider converting your data to more memory-efficient data types. For example, if you are using a dtype of int32, you can switch to int16 to reduce memory usage.

5. Properly Clear Memory: Make sure to properly clear memory after model training or evaluation. Use Keras’ clear_session() function to clear the Keras session and release all memory associated with the model.

Applying these optimization strategies should help mitigate memory consumption during training and ensure smoother execution of your code.
apply some of the optimizations and also add more debug statements since i have no clue how far it is.

import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dense

# Read and preprocess the dataset
def read_dataset(file_path):
with open(file_path, “r”, encoding=“utf-8”) as file:
lines = file.read().split(“\n”)

text = " ".join(lines) # Concatenate all lines into a single string

# Create a set of unique characters and map them to numerical indices
chars = sorted(list(set(text)))
char_to_idx = {char: idx for idx, char in enumerate(chars)}
idx_to_char = {idx: char for idx, char in enumerate(chars)}

return text, chars, char_to_idx, idx_to_char

def prepare_sequences(text, seq_length, char_to_idx):
sequences = []
targets = []
for i in range(len(text) - seq_length):
sequence = text[i:i+seq_length]
target = text[i+seq_length]
sequences.append([char_to_idx[char] for char in sequence])
targets.append(char_to_idx[target])

return np.array(sequences), np.array(targets)

# Model configuration
seq_length = 10
hidden_units = 128

# Read and preprocess the dataset
file_path = “rockyou.txt”
text, chars, char_to_idx, idx_to_char = read_dataset(file_path)

# Prepare training sequences and targets
sequences, targets = prepare_sequences(text, seq_length, char_to_idx)

# Build the model
model = Sequential()
model.add(LSTM(hidden_units, input_shape=(seq_length, len(chars))))
model.add(Dense(len(chars), activation=‘softmax’))

# Configure training parameters
model.compile(optimizer=‘adam’, loss=‘sparse_categorical_crossentropy’)

# Train the model
model.fit(sequences, targets, batch_size=128, epochs=10)

# Generate new text
starting_sequence = “F”
generated_text = starting_sequence

# Generate characters one by one
for _ in range(10):
sequence = generated_text[-seq_length:]
X = np.array([char_to_idx[char] for char in sequence]).reshape(1, seq_length, 1)
predicted_idx = np.argmax(model.predict(X))
predicted_char = idx_to_char[predicted_idx]
generated_text += predicted_char

print(“Generated Text:\n” + generated_text”)
Certainly! Here’s an updated version of the code addressing memory optimization and adding debug statements:


import numpy as np
import gc
from keras.models import Sequential
from keras.layers import LSTM, Dense
from keras import backend as K

# Read and preprocess the dataset
def read_dataset(file_path):
    with open(file_path, “r”, encoding=“utf-8”) as file:
        lines = file.read().split(“\n”)

    text = " ".join(lines)  # Concatenate all lines into a single string

    # Create