python-tensorflowHow do I calculate loss using Python and TensorFlow?

To calculate loss using Python and TensorFlow, you can use the tf.keras.losses API. The API provides several ready-to-use loss functions, such as mean_squared_error, mean_absolute_error, and categorical_crossentropy.

For example, to calculate the mean squared error of two tensors, y_true and y_pred, use the following code:

loss = tf.keras.losses.mean_squared_error(y_true, y_pred)
print(loss)

Output example

tf.Tensor(2.5, shape=(), dtype=float32)

Code explanation

1. tf.keras.losses - the API for accessing different ready-to-use loss functions
2. mean_squared_error - the loss function used to calculate the mean squared error of two tensors
3. y_true and y_pred - the two tensors used to calculate the mean squared error
4. tf.keras.losses.mean_squared_error - the function used to calculate the mean squared error of two tensors
5. loss - the variable used to store the calculated mean squared error
6. print(loss) - the function used to print the calculated mean squared error

Helpful links

• TensorFlow Keras Losses
• Mean Squared Error

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