To save a deep learning model, its architecture can be serialized to a JSON file using model.to_json() and written to a file with json_file.write(model_json). Additionally, the model's weights and architecture can be saved to an H5 file with model.save("emotiondetector.h5").

Cell 19

model_json = model.to_json()
with open("emotiondetector.json",'w') as json_file:
    json_file.write(model_json)
model.save("emotiondetector.h5")

def serialize_model_to_json(model):
    """
    Serialize the given model into a JSON string.

    Args:
    model (keras.Model): The model to be serialized.

    Returns:
    str: The JSON representation of the model.
    """
    model_json = model.to_json()
    return model_json

# Serialize the model and write it to a file
model_json = serialize_model_to_json(model)
with open("emotiondetector.json", "w") as json_file:
    json_file.write(model_json)

Saving a Deep Learning Model

Purpose

Save a deep learning model's architecture and weights to JSON and H5 files.

Code Breakdown

1. Serialize Model to JSON

   • model.to_json(): Convert the Keras model's architecture to a JSON string.
   • model_json = model.to_json(): Store the JSON string in the model_json variable.

2. Write JSON String to File

   • with open("emotiondetector.json", 'w') as json_file:: Open a file named "emotiondetector.json" in write mode.
   • json_file.write(model_json): Write the JSON string to the file.

3. Save Model Weights to H5 File

   • model.save("emotiondetector.h5"): Save the model's weights and architecture to an H5 file named "emotiondetector.h5".