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To save Keras models without TensorFlow, you can use the built-in methods provided by Keras itself. You can save the model architecture to a JSON file using the to_json method and save the model weights to an HDF5 file using the save_weights method. By saving the model architecture and weights separately, you can easily recreate and load the model later using the model_from_json and load_weights methods. This way, you can save and load Keras models without relying on TensorFlow-specific APIs.

How to store keras models without tensorflow in a different location?

To store Keras models without TensorFlow in a different location, you can use the save method provided by Keras. Here's how you can do it:

1. Train your Keras model as usual.

import keras from keras.models import Sequential from keras.layers import Dense

Define your model

model = Sequential() model.add(Dense(64, activation='relu', input_shape=(10,))) model.add(Dense(64, activation='relu')) model.add(Dense(1, activation='sigmoid'))

Compile the model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Train the model

model.fit(X_train, y_train, epochs=10, batch_size=32)

1. Save the trained model to a file.

# Save the model to a file model.save('path/to/your/model.h5')

1. Now, you can load the model from the saved file location.

# Load the model from the file from keras.models import load_model

model = load_model('path/to/your/model.h5')

By following these steps, you can store and load your Keras models without TensorFlow in a different location.

What is the compatibility of saved keras models without tensorflow with different versions of keras?

Saved Keras models are generally compatible with different versions of Keras as long as the version differences are not too significant. However, there may be some issues or compatibility errors if there are major changes or updates in the Keras framework.

It is recommended to use the same version of Keras that was used to train and save the model when loading a saved model. If you encounter any compatibility issues, you may need to update the saved model to be compatible with the newer version of Keras.

Additionally, if you are using a saved Keras model without TensorFlow, make sure that the dependencies and configurations are set up correctly in the new environment to ensure the compatibility of the model.

How to load keras models without tensorflow for inference purposes?

1. Save the Keras model as a JSON file (model.json) and the weights as an HDF5 file (model_weights.h5) using the following code:

from keras.models import model_from_json

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

model.save_weights("model_weights.h5")

1. Load the saved model and weights files using the following code:

from keras.models import model_from_json

Load model architecture from JSON file

with open('model.json', 'r') as json_file: loaded_model_json = json_file.read() loaded_model = model_from_json(loaded_model_json)

Load model weights from HDF5 file

loaded_model.load_weights("model_weights.h5")

1. Make predictions using the loaded model:

predictions = loaded_model.predict(input_data)

By following these steps, you can load a Keras model without TensorFlow for inference purposes.