Tensorflow.js tf.layers.alphaDropout() Function
The tf.layers.AlphaDropout() function is used to apply Alpha Dropout to the input. Since it is a regularization layer hence, it is only active at training time.
- inputShape : It is an optional parameter which is used to create the input layer, and it takes values like number and null.
- batchInputShape : It is an optional parameter which is used to create the input layer before the main layer, and it takes values like number and null.
- batchSize : Its is an optional parameter used to make batchInputShape, and, and it accepts only numbers.
- dtype : It is an optional parameter, and it stands for data type. By default, it has ‘float32’ and also supports other values like ‘int32’, ‘bool’ etc.
- name: It is an optional parameter and is used to define the name of the layer, and it accepts strings.
- trainable : It is an optional parameter that determines the provided input layers are updated or not. It accepts boolean values.
- weights : It possesses the starting weights of the layer. It is also an optional parameter.
- inputDType : It is an optional parameter used for input data type. Like dtype it also supports all its values.
Return Value: It returns AlphaDropout.
Tensor [[121 , 152 ], [2213, 7814]]
Tensor [[25 , 163], [127, 328]]