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()
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/keras/constraints/MaxNorm