Porting to TF2

Fixes #75 (closed)

bob/learn/tensorflow/layers/Maxout.py

-#!/usr/bin/env python
-# vim: set fileencoding=utf-8 :
-# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
-# @date:  Fri 04 Aug 2017 14:14:22 CEST
-
-# MAXOUT IMPLEMENTED FOR TENSORFLOW
-
-from tensorflow.python.layers import base
-import tensorflow as tf
-
-
-def maxout(inputs, num_units, axis=-1, name=None):
-    return Maxout(num_units=num_units, axis=axis, name=name)(inputs)
-
-
-class Maxout(base.Layer):
-    """
-     Adds a maxout op from
-
-    "Maxout Networks"
-
-    Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, Yoshua
-    Bengio
-
-    Usually the operation is performed in the filter/channel dimension. This can also be
-    used after fully-connected layers to reduce number of features.
-
-    **Parameters**
-    inputs: Tensor input
-
-    num_units: Specifies how many features will remain after maxout in the `axis` dimension (usually channel).
-    This must be multiple of number of `axis`.
-
-    axis: The dimension where max pooling will be performed. Default is the
-      last dimension.
-
-    name: Optional scope for name_scope.
-    """
-
-    def __init__(self, num_units, axis=-1, name=None, **kwargs):
-        super(Maxout, self).__init__(name=name, trainable=False, **kwargs)
-        self.axis = axis
-        self.num_units = num_units
-
-    def call(self, inputs, training=False):
-        inputs = tf.convert_to_tensor(inputs)
-        shape = inputs.get_shape().as_list()
-        # Dealing with batches with arbitrary sizes
-        for i in range(len(shape)):
-            if shape[i] is None:
-                shape[i] = tf.shape(inputs)[i]
-
-        num_channels = shape[self.axis]
-        if not isinstance(num_channels, tf.Tensor) and num_channels % self.num_units:
-            raise ValueError(
-                "number of features({}) is not "
-                "a multiple of num_units({})".format(num_channels, self.num_units)
-            )
-
-        if self.axis < 0:
-            axis = self.axis + len(shape)
-        else:
-            axis = self.axis
-        assert axis >= 0, "Find invalid axis: {}".format(self.axis)
-
-        expand_shape = shape[:]
-        expand_shape[axis] = self.num_units
-        k = num_channels // self.num_units
-        expand_shape.insert(axis, k)
-
-        outputs = tf.math.reduce_max(
-            tf.reshape(inputs, expand_shape), axis, keepdims=False
-        )
-        return outputs