From 8c98867cfa3880d21f5db962401c480dac16c09e Mon Sep 17 00:00:00 2001
From: Matt Graham <m.m.graham@ed.ac.uk>
Date: Mon, 19 Sep 2016 07:33:37 +0100
Subject: [PATCH] Removing convolution placeholders.

---
 mlp/conv.py | 126 ----------------------------------------------------
 1 file changed, 126 deletions(-)
 delete mode 100644 mlp/conv.py

diff --git a/mlp/conv.py b/mlp/conv.py
deleted file mode 100644
index 4bb9786..0000000
--- a/mlp/conv.py
+++ /dev/null
@@ -1,126 +0,0 @@
-
-# Machine Learning Practical (INFR11119),
-# Pawel Swietojanski, University of Edinburgh
-
-
-import numpy
-import logging
-from mlp.layers import Layer
-
-
-logger = logging.getLogger(__name__)
-
-"""
-You have been given some very initial skeleton below. Feel free to build on top of it and/or
-modify it according to your needs. Just notice, you can factor out the convolution code out of
-the layer code, and just pass (possibly) different conv implementations for each of the stages
-in the model where you are expected to apply the convolutional operator. This will allow you to
-keep the layer implementation independent of conv operator implementation, and you can easily
-swap it layer, for example, for more efficient implementation if you came up with one, etc.
-"""
-
-def my1_conv2d(image, kernels, strides=(1, 1)):
-    """
-    Implements a 2d valid convolution of kernels with the image
-    Note: filer means the same as kernel and convolution (correlation) of those with the input space
-    produces feature maps (sometimes refereed to also as receptive fields). Also note, that
-    feature maps are synonyms here to channels, and as such num_inp_channels == num_inp_feat_maps
-    :param image: 4D tensor of sizes (batch_size, num_input_channels, img_shape_x, img_shape_y)
-    :param filters: 4D tensor of filters of size (num_inp_feat_maps, num_out_feat_maps, kernel_shape_x, kernel_shape_y)
-    :param strides: a tuple (stride_x, stride_y), specifying the shift of the kernels in x and y dimensions
-    :return: 4D tensor of size (batch_size, num_out_feature_maps, feature_map_shape_x, feature_map_shape_y)
-    """
-    raise NotImplementedError('Write me!')
-
-
-class ConvLinear(Layer):
-    def __init__(self,
-                 num_inp_feat_maps,
-                 num_out_feat_maps,
-                 image_shape=(28, 28),
-                 kernel_shape=(5, 5),
-                 stride=(1, 1),
-                 irange=0.2,
-                 rng=None,
-                 conv_fwd=my1_conv2d,
-                 conv_bck=my1_conv2d,
-                 conv_grad=my1_conv2d):
-        """
-
-        :param num_inp_feat_maps: int, a number of input feature maps (channels)
-        :param num_out_feat_maps: int, a number of output feature maps (channels)
-        :param image_shape: tuple, a shape of the image
-        :param kernel_shape: tuple, a shape of the kernel
-        :param stride: tuple, shift of kernels in both dimensions
-        :param irange: float, initial range of the parameters
-        :param rng: RandomState object, random number generator
-        :param conv_fwd: handle to a convolution function used in fwd-prop
-        :param conv_bck: handle to a convolution function used in backward-prop
-        :param conv_grad: handle to a convolution function used in pgrads
-        :return:
-        """
-
-        super(ConvLinear, self).__init__(rng=rng)
-
-        raise NotImplementedError()
-
-    def fprop(self, inputs):
-        raise NotImplementedError()
-
-    def bprop(self, h, igrads):
-        raise NotImplementedError()
-
-    def bprop_cost(self, h, igrads, cost):
-        raise NotImplementedError('ConvLinear.bprop_cost method not implemented')
-
-    def pgrads(self, inputs, deltas, l1_weight=0, l2_weight=0):
-        raise NotImplementedError()
-
-    def get_params(self):
-        raise NotImplementedError()
-
-    def set_params(self, params):
-        raise NotImplementedError()
-
-    def get_name(self):
-        return 'convlinear'
-
-#you can derive here particular non-linear implementations:
-#class ConvSigmoid(ConvLinear):
-#...
-
-
-class ConvMaxPool2D(Layer):
-    def __init__(self,
-                 num_feat_maps,
-                 conv_shape,
-                 pool_shape=(2, 2),
-                 pool_stride=(2, 2)):
-        """
-
-        :param conv_shape: tuple, a shape of the lower convolutional feature maps output
-        :param pool_shape: tuple, a shape of pooling operator
-        :param pool_stride: tuple, a strides for pooling operator
-        :return:
-        """
-
-        super(ConvMaxPool2D, self).__init__(rng=None)
-        raise NotImplementedError()
-
-    def fprop(self, inputs):
-        raise NotImplementedError()
-
-    def bprop(self, h, igrads):
-        raise NotImplementedError()
-
-    def get_params(self):
-        return []
-
-    def pgrads(self, inputs, deltas, **kwargs):
-        return []
-
-    def set_params(self, params):
-        pass
-
-    def get_name(self):
-        return 'convmaxpool2d'
\ No newline at end of file