Row 5955

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How do I manage LSTM hidden layer states in a TFLite model? I got the following suggestion from ChatGPT, but input_details[1] is out of range ``` import numpy as np import tensorflow as tf from tensorflow.lite.python.interpreter import Interpreter

# Load the TFLite model interpreter = Interpreter(model_path="your_tflite_model.tflite") interpreter.allocate_tensors()

# Get input and output details input_details = interpreter.get_input_details() output_details = interpreter.get_output_details()

# Initialize LSTM state initial_state = np.zeros((1, num_units)) # Adjust shape based on your LSTM configuration

def reset_lstm_state(): # Reset LSTM state to initial state interpreter.set_tensor(input_details[1]['index'], initial_state)

# Perform inference def inference(input_data): interpreter.set_tensor(input_details[0]['index'], input_data) interpreter.invoke() output_data = interpreter.get_tensor(output_details[0]['index']) return output_data

# Example usage input_data = np.array(...) # Input data, shape depends on your model output_data = inference(input_data) reset_lstm_state() # Reset LSTM state after inference ```

FieldValue
text How do I manage LSTM hidden layer states in a TFLite model? I got the following suggestion from ChatGPT, but input_details[1] is out of range ``` import numpy as np import tensorflow as tf from tensorflow.lite.python.interpreter import Interpreter # Load the TFLite model interpreter = Interpreter(model_path="your_tflite_model.tflite") interpreter.allocate_tensors() # Get input and output details input_details = interpreter.get_input_details() output_details = interpreter.get_output_details() …
label r/tensorflow
dataType post
communityName r/tensorflow
datetime 2024-05-05
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url_encoded Z0FBQUFBQm5Lak9HN2wxNXdZdXJjS1lMQkszeGozaHNqWHk5eEs0QllTMmRPWFBOTXZvMjdtQkVuRUZVaWFRQ2l0ZGw3YVpnbVFSaDJWN0EwMWZUbDRiTGMtYzgwRXpLQW5ZY0lQcC1NVWdJREhPRG8zVjNfblR5Rnh0RTQwWnh3Q2FpSkJRTW9EY0stdlFkQWhlMGhPeGlkV3B5emp5YU5SU0xhbDNKMXEySnFDVnZBM0VrbC1md1VtRWhkbW4zVzRaMkRLQXFEeDlt

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{
  "text": "How do I manage LSTM hidden layer states in a TFLite model?\nI got the following suggestion from ChatGPT, but input_details[1] is out of range\n```\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.lite.python.interpreter import Interpreter\n\n# Load the TFLite model\ninterpreter = Interpreter(model_path=\"your_tflite_model.tflite\")\ninterpreter.allocate_tensors()\n\n# Get input and output details\ninput_details = interpreter.get_input_details()\noutput_details = interpreter.get_output_details()\n\n# Initialize LSTM state\ninitial_state = np.zeros((1, num_units))  # Adjust shape based on your LSTM configuration\n\ndef reset_lstm_state():\n    # Reset LSTM state to initial state\n    interpreter.set_tensor(input_details[1]['index'], initial_state)\n\n# Perform inference\ndef inference(input_data):\n    interpreter.set_tensor(input_details[0]['index'], input_data)\n    interpreter.invoke()\n    output_data = interpreter.get_tensor(output_details[0]['index'])\n    return output_data\n\n# Example usage\ninput_data = np.array(...)  # Input data, shape depends on your model\noutput_data = inference(input_data)\nreset_lstm_state()  # Reset LSTM state after inference\n```",
  "label": "r/tensorflow",
  "dataType": "post",
  "communityName": "r/tensorflow",
  "datetime": "2024-05-05",
  "username_encoded": "Z0FBQUFBQm5LakwyaTdlaVdKSURaZE41TnZmdW5CZEwxczZhRWpFQ1RhTkxEbVYzLTV1NkphUHlNZEFFM0tGT2ZPUWtFZEVxZnRZYVptOWZZOWMtTDdQNm5hQno1YUx1Ymc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9HN2wxNXdZdXJjS1lMQkszeGozaHNqWHk5eEs0QllTMmRPWFBOTXZvMjdtQkVuRUZVaWFRQ2l0ZGw3YVpnbVFSaDJWN0EwMWZUbDRiTGMtYzgwRXpLQW5ZY0lQcC1NVWdJREhPRG8zVjNfblR5Rnh0RTQwWnh3Q2FpSkJRTW9EY0stdlFkQWhlMGhPeGlkV3B5emp5YU5SU0xhbDNKMXEySnFDVnZBM0VrbC1md1VtRWhkbW4zVzRaMkRLQXFEeDlt"
}
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