Created layer with maxout

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.framework import ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.ops import gen_array_ops
+
+from tensorflow.python.layers import base
+
+
+def maxout(inputs, num_units, axis=-1, name=None):
+    """Adds a maxout op from https://arxiv.org/abs/1302.4389
+
+      "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.
+
+    Args:
+      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.
+    Returns:
+      A `Tensor` representing the results of the pooling operation.
+    Raises:
+      ValueError: if num_units is not multiple of number of features.
+    """
+
+    return MaxOut(num_units=num_units, axis=axis, name=name)(inputs)
+
+
+class MaxOut(base.Layer):
+    """Adds a maxout op from https://arxiv.org/abs/1302.4389
+
+      "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.
+
+    Args:
+      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.
+    Returns:
+      A `Tensor` representing the results of the pooling operation.
+    Raises:
+      ValueError: if num_units is not multiple of number of features.
+    """
+
+    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 = ops.convert_to_tensor(inputs)
+        shape = inputs.get_shape().as_list()
+        if self.axis is None:
+            # Assume that channel is the last dimension
+            self.axis = -1
+        num_channels = shape[self.axis]
+        if num_channels % self.num_units:
+            raise ValueError('number of features({}) is not '
+                             'a multiple of num_units({})'
+                             .format(num_channels, self.num_units))
+        shape[self.axis] = -1
+        shape += [num_channels // self.num_units]
+
+        # Dealing with batches with arbitrary sizes
+        for i in range(len(shape)):
+            if shape[i] is None:
+                shape[i] = gen_array_ops.shape(inputs)[i]
+        outputs = math_ops.reduce_max(gen_array_ops.reshape(inputs, shape), -1, keep_dims=False)
+
+        return outputs

bob/learn/tensorflow/test/test_layers.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+# @date: Thu 13 Oct 2016 13:35 CEST
+
+import tensorflow as tf
+import numpy as np
+from bob.learn.tensorflow.layers import maxout
+from nose.tools import assert_raises_regexp
+
+slim = tf.contrib.slim
+
+def test_simple():
+    x = np.zeros([64, 10, 36])
+    graph = maxout(x, num_units=3)
+    assert graph.get_shape().as_list() == [64, 10, 3]
+
+def test_fully_connected():
+    x = np.zeros([64, 50])
+    graph = slim.fully_connected(x, 50, activation_fn=None)
+    graph = maxout(graph, num_units=10)
+    assert graph.get_shape().as_list() == [64, 10]
+
+def test_nchw():
+    x = np.random.uniform(size=(10, 100, 100, 3)).astype(np.float32)
+    graph = slim.conv2d(x, 10, [3, 3])
+    graph = maxout(graph, num_units=1)
+    assert graph.get_shape().as_list() == [10, 100, 100, 1]
+
+def test_invalid_shape():
+    x = np.random.uniform(size=(10, 100, 100, 3)).astype(np.float32)
+    graph = slim.conv2d(x, 3, [3, 3])
+    with assert_raises_regexp(ValueError, 'number of features'):
+        graph = maxout(graph, num_units=2)
+