Clips tensor values to a maximum L2-norm.

# Aliases:

• tf.compat.v1.clip_by_norm
• tf.compat.v2.clip_by_norm

 tf.clip_by_norm(
    t,
    clip_norm,
    axes=None,
    name=None
)

# Used in the guide:

• Eager execution Given a tensor t, and a maximum clip value clip_norm, this operation normalizes t so that its L2-norm is less than or equal to clip_norm, along the dimensions given in axes. Specifically, in the default case where all dimensions are used for calculation, if the L2-norm of t is already less than or equal to clip_norm, then t is not modified. If the L2-norm is greater than clip_norm, then this operation returns a tensor of the same type and shape as t with its values set to: t * clip_norm / l2norm(t) In this case, the L2-norm of the output tensor is clip_norm. As another example, if t is a matrix and axes == [1], then each row of the output will have L2-norm less than or equal to clip_norm. If axes == [0] instead, each column of the output will be clipped. This operation is typically used to clip gradients before applying them with an optimizer.

# Args:

• t: A Tensor or IndexedSlices.
• clip_norm: A 0-D (scalar) Tensor > 0. A maximum clipping value.
• axes: A 1-D (vector) Tensor of type int32 containing the dimensions to use for computing the L2-norm. If None (the default), uses all dimensions.
• name: A name for the operation (optional).

# Returns:

A clipped Tensor or IndexedSlices.

# Raises:

• ValueError: If the clip_norm tensor is not a 0-D scalar tensor.
• TypeError: If dtype of the input is not a floating point or complex type.