contrib.keras.constraints.MaxNorm

tf.contrib.keras.constraints.MaxNorm

class tf.contrib.keras.constraints.MaxNorm

class tf.contrib.keras.constraints.max_norm

Defined in tensorflow/contrib/keras/python/keras/constraints.py.

MaxNorm weight constraint.

Constrains the weights incident to each hidden unit to have a norm less than or equal to a desired value.

Arguments:

m: the maximum norm for the incoming weights.
axis: integer, axis along which to calculate weight norms.
    For instance, in a `Dense` layer the weight matrix
    has shape `(input_dim, output_dim)`,
    set `axis` to `0` to constrain each weight vector
    of length `(input_dim,)`.
    In a `Convolution2D` layer with `data_format="channels_last"`,
    the weight tensor has shape
    `(rows, cols, input_depth, output_depth)`,
    set `axis` to `[0, 1, 2]`
    to constrain the weights of each filter tensor of size
    `(rows, cols, input_depth)`.

References: - Dropout: A Simple Way to Prevent Neural Networks from Overfitting Srivastava, Hinton, et al. 2014

Methods

__init__

__init__(
    max_value=2,
    axis=0
)

__call__

__call__(w)

get_config

get_config()