Save Model Keras AZ Synapse

Question

How to save a keras file using python Notebook in Azure Synapse?

Answer 1

Hi Londono Quintero Ferney De Jesus,

Thank you for posting query in Microsoft Q&A Platform.

To save a Keras file using a Python Notebook in Azure Synapse, you can use the save method of the Keras model object. Here's an example:

from tensorflow import keras

# create a Keras model
model = keras.Sequential([
    keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    keras.layers.Dense(10, activation='softmax')
])

# train the model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=32)

# save the model to a file
model.save('my_model.h5')

In the above example, we first create a Keras model using the Sequential API. We then train the model using the compile and fit methods. Finally, we save the model to a file using the save method and specifying the file name as 'my_model.h5'.

Note that the save method saves the entire model, including the architecture, weights, and optimizer state. You can load the saved model using the load_model function from the tensorflow.keras.models module:

from tensorflow.keras.models import load_model

# load the saved model
model = load_model('my_model.h5')

In the above example, we use the load_model function to load the saved model from the file 'my_model.h5'. Once the model is loaded, you can use it to make predictions on new data.

Hope this helps. please try and let me know how it goes.

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