Here is a complete example exporting a simple tf.Module object: class MyModule(tf.Module): This allows you to run your model independently of the Python program that created it. You can import and export the tf.Variable values and the tf.function graphs using tf.saved_model.This is useful during training as it is quick to save and restore a model's state. You can save and restore the values of your variables using tf.train.Checkpoint.The tf.Module class is necessary to support two significant features: Tf.Module is a class for managing your tf.Variable objects, and the tf.function objects that operate on them. Refer to Intro to graphs for more details. You can export these graphs, using tf.saved_model, to run on other systems like a server or a mobile device, no Python installation required.In many cases they provide a significant speedup in execution (though not this trivial example).These captured graphs provide two benefits: x = tf.constant()Ī graph may not be reusable for inputs with a different signature ( shape and dtype), so a new graph is generated instead: x = tf.constant(, dtype=tf.float32) Below, note that my_func doesn't print tracing since print is a Python function, not a TensorFlow function. On subsequent calls TensorFlow only executes the optimized graph, skipping any non-TensorFlow steps. The first time you run the tf.function, although it executes in Python, it captures a complete, optimized graph representing the TensorFlow computations done within the function. These require that you use tf.function to separate your pure-TensorFlow code from Python. Export: so you can save your model when it's done training.Performance optimization: to speed up training and inference.While you can use TensorFlow interactively like any Python library, TensorFlow also provides tools for: This simplified example only takes the derivative with respect to a single scalar ( x), but TensorFlow can compute the gradient with respect to any number of non-scalar tensors simultaneously. TensorFlow can calculate this automatically: with tf.GradientTape() as tape: The derivative of y is y' = f'(x) = (2*x + 2) = 4. Typically you'll use this to calculate the gradient of a model's error or loss with respect to its weights. To enable this, TensorFlow implements automatic differentiation (autodiff), which uses calculus to compute gradients. Gradient descent and related algorithms are a cornerstone of modern machine learning. Refer to the Variables guide for details. To store model weights (or other mutable state) in TensorFlow use a tf.Variable. Print("TensorFlow **IS NOT** using the GPU") if tf.config.list_physical_devices('GPU'): When properly configured, TensorFlow can use accelerator hardware like GPUs to execute operations very quickly. Running large calculations on CPU can be slow. Note: Typically, anywhere a TensorFlow function expects a Tensor as input, the function will also accept anything that can be converted to a Tensor using tf.convert_to_tensor. TensorFlow implements standard mathematical operations on tensors, as well as many operations specialized for machine learning.
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