python-tensorflowHow do I save a TensorFlow model in Python?

Saving a TensorFlow model in Python is easy and straightforward. The following example code shows how to save a model in TensorFlow:

# Create and train a model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(4,))
])
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(X_train, y_train, epochs=5)

# Save the model
model.save('my_model.h5')

This will save the model as an HDF5 file called my_model.h5. The code can be broken down into the following parts:

1. tf.keras.Sequential(): This creates a Sequential model in TensorFlow.
2. tf.keras.layers.Dense(): This adds a densely-connected layer to the model.
3. model.compile(): This compiles the model with the given optimizer, loss, and metrics.
4. model.fit(): This fits the model to the training data.
5. model.save(): This saves the model as an HDF5 file.

For more information, see the TensorFlow documentation.

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