Finish passing to new conv2d interface

nouiz · nouiz · commit ff6939b7bcdb · 2016-03-22T11:05:15.000-04:00
diff --git a/code/convolutional_mlp.py b/code/convolutional_mlp.py
@@ -94,7 +94,7 @@ def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):
             input=input,
             filters=self.W,
             filter_shape=filter_shape,
-            image_shape=image_shape
+            input_shape=image_shape
         )
 
         # downsample each feature map individually, using maxpooling
diff --git a/doc/lenet.txt b/doc/lenet.txt
@@ -196,7 +196,7 @@ one of Figure 1. The input consists of 3 features maps (an RGB color image) of s
 
         import theano
         from theano import tensor as T
-        from theano.tensor.nnet import conv
+        from theano.tensor.nnet import conv2d
 
         import numpy
 
@@ -226,7 +226,7 @@ one of Figure 1. The input consists of 3 features maps (an RGB color image) of s
                     dtype=input.dtype), name ='b')
 
         # build symbolic expression that computes the convolution of input with filters in w
-        conv_out = conv.conv2d(input, W)
+        conv_out = conv2d(input, W)
 
         # build symbolic expression to add bias and apply activation function, i.e. produce neural net layer output
         # A few words on ``dimshuffle`` :
@@ -404,7 +404,7 @@ to be compatible with our previous MLP implementation.
     Note that the term "convolution" could corresponds to different mathematical operations:
 
     1. `theano.tensor.nnet.conv2d
-       <http://deeplearning.net/software/theano/library/tensor/nnet/conv.html#theano.tensor.nnet.conv.conv2d>`_,
+       <http://deeplearning.net/software/theano/library/tensor/nnet/conv.html#theano.tensor.nnet.conv2d>`_,
        which is the most common one in almost all of the recent published
        convolutional models.
        In this operation, each output feature map is connected to each

Original file line number	Diff line number	Diff line change
`@@ -94,7 +94,7 @@ def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2, 2)):`
`94`	`94`	`input=input,`
`95`	`95`	`filters=self.W,`
`96`	`96`	`filter_shape=filter_shape,`
`97`		`- image_shape=image_shape`
	`97`	`+ input_shape=image_shape`
`98`	`98`	`)`
`99`	`99`
`100`	`100`	`# downsample each feature map individually, using maxpooling`