Function variableGrads

  • variableGrads(f, varList?): {
        grads: NamedTensorMap;
        value: Scalar;
    }

  • Computes and returns the gradient of f(x) with respect to the list of trainable variables provided by varList. If no list is provided, it defaults to all trainable variables.

    const a = tf.variable(tf.tensor1d([3, 4]));
    const b = tf.variable(tf.tensor1d([5, 6]));
    const x = tf.tensor1d([1, 2]);

    // f(a, b) = a * x ^ 2 + b * x
    const f = () => a.mul(x.square()).add(b.mul(x)).sum();
    // df/da = x ^ 2, df/db = x
    const {value, grads} = tf.variableGrads(f);

    Object.keys(grads).forEach(varName => grads[varName].print());

    Parameters

    • f: (() => Scalar)

      The function to execute. f() should return a scalar.

    • Optional varList: Variable<Rank>[]

      The list of variables to compute the gradients with respect to. Defaults to all trainable variables.

    Returns {
        grads: NamedTensorMap;
        value: Scalar;
    }

    An object with the following keys and values:

    • value: The value of the function f.
    • grads: A map from the names of the variables to the gradients. If the varList argument is provided explicitly and contains a subset of non-trainable variables, this map in the return value will contain keys that map the names of the non-trainable variables to null.

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