Merge pull request lisa-lab#150 from nouiz/master2

small stuff and use update interface to Theano 0.8

Author: lamblin

code/DBN.py

@@ -174,7 +174,7 @@ def pretraining_functions(self, train_set_x, batch_size, k):
 
             # compile the theano function
             fn = theano.function(
-                inputs=[index, theano.Param(learning_rate, default=0.1)],
+                inputs=[index, theano.In(learning_rate, value=0.1)],
                 outputs=cost,
                 updates=updates,
                 givens={

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

code/rbm.py

@@ -257,7 +257,8 @@ def get_cost_updates(self, lr=0.1, persistent=None, k=1):
             # chain_start is the initial state corresponding to the
             # 6th output
             outputs_info=[None, None, None, None, None, chain_start],
-            n_steps=k
+            n_steps=k,
+            name="gibbs_hvh"
         )
         # start-snippet-3
         # determine gradients on RBM parameters
@@ -496,7 +497,8 @@ def test_rbm(learning_rate=0.1, training_epochs=15,
     ) = theano.scan(
         rbm.gibbs_vhv,
         outputs_info=[None, None, None, None, None, persistent_vis_chain],
-        n_steps=plot_every
+        n_steps=plot_every,
+        name="gibbs_vhv"
     )
 
     # add to updates the shared variable that takes care of our persistent

data/download.sh

@@ -5,7 +5,7 @@ WGET=$?
 which curl >/dev/null 2>&1
 CURL=$?
 if [ "$WGET" -eq 0 ]; then
-    DL_CMD="wget -c"
+    DL_CMD="wget --no-verbose -c"
 elif [ "$CURL" -eq 0 ]; then
     DL_CMD="curl -C - -O"
 else

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