contrib.keras.constraints.MinMaxNorm

tf.contrib.keras.constraints.MinMaxNorm

class tf.contrib.keras.constraints.MinMaxNorm

class tf.contrib.keras.constraints.min_max_norm

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

MinMaxNorm weight constraint.

Constrains the weights incident to each hidden unit to have the norm between a lower bound and an upper bound.

Arguments:

min_value: the minimum norm for the incoming weights.
max_value: the maximum norm for the incoming weights.
rate: rate for enforcing the constraint: weights will be
    rescaled to yield
    `(1 - rate) * norm + rate * norm.clip(min_value, max_value)`.
    Effectively, this means that rate=1.0 stands for strict
    enforcement of the constraint, while rate<1.0 means that
    weights will be rescaled at each step to slowly move
    towards a value inside the desired interval.
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 `dim_ordering="tf"`,
    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)`.

Methods

__init__

__init__(
    min_value=0.0,
    max_value=1.0,
    rate=1.0,
    axis=0
)

__call__

__call__(w)

get_config

get_config()