# HG changeset patch
# User humel
# Date 1268759788 14400
# Node ID 4d109b648c3153668b52f5eb25a9e2da89bfcb73
# Parent  2024368a8d3d0ab3e7b359b0a3bd7ed74e40ebee
Fixed dataset import. Removed unuseful code from da_conv. Keys parameters are now passed as arguments.

diff -r 2024368a8d3d -r 4d109b648c31 deep/convolutional_dae/stacked_convolutional_dae.py
--- a/deep/convolutional_dae/stacked_convolutional_dae.py	Tue Mar 16 12:14:10 2010 -0400
+++ b/deep/convolutional_dae/stacked_convolutional_dae.py	Tue Mar 16 13:16:28 2010 -0400
@@ -1,6 +1,7 @@
 import numpy
 import theano
 import time
+import sys
 import theano.tensor as T
 from theano.tensor.shared_randomstreams import RandomStreams
 import theano.sandbox.softsign
@@ -8,10 +9,11 @@
 from theano.tensor.signal import downsample
 from theano.tensor.nnet import conv 
 
-from ift6266 import datasets
+sys.path.append('../../../')
 
+from ift6266 import datasets
 from ift6266.baseline.log_reg.log_reg import LogisticRegression
- 
+
 class SigmoidalLayer(object):
     def __init__(self, rng, input, n_in, n_out):
 
@@ -57,8 +59,6 @@
     
  
     initial_b_prime= numpy.zeros((filter_shape[1],),dtype=theano.config.floatX)
-        
-    self.W_prime=T.dtensor4('W_prime')
 
     self.b_prime = theano.shared(value = initial_b_prime, name = "b_prime")
  
@@ -96,6 +96,7 @@
     self.params = [ self.W, self.b, self.b_prime ] 
 
 class LeNetConvPoolLayer(object):
+
     def __init__(self, rng, input, filter_shape, image_shape=None, poolsize=(2,2)):
         self.input = input
   
@@ -211,36 +212,38 @@
         
         self.errors = self.logLayer.errors(self.y)
 
-def sgd_optimization_mnist( learning_rate=0.1, pretraining_epochs = 2, \
-                            pretrain_lr = 0.01, training_epochs = 1000, \
+def sgd_optimization_mnist( learning_rate=0.1, pretraining_epochs = 1, \
+                            pretrain_lr = 0.1, training_epochs = 1000, \
+                            kernels = [ [2,5,5] , [2,3,3] ], mlp_layers=[500], \
+                            corruption_levels = [ 0.2, 0.2, 0.2], \
+                            max_pool_layers = [ [2,2] , [2,2] ], \
                             dataset=datasets.nist_digits):
     
-    batch_size = 500 # size of the minibatch
+    batch_size = 100 # size of the minibatch
  
     # allocate symbolic variables for the data
     index = T.lscalar() # index to a [mini]batch
     x = T.matrix('x') # the data is presented as rasterized images
     y = T.ivector('y') # the labels are presented as 1d vector of
     # [int] labels
+
     layer0_input = x.reshape((x.shape[0],1,32,32))
     
-
-    # Setup the convolutional layers with their DAs(add as many as you want)
-    corruption_levels = [ 0.2, 0.2, 0.2]
     rng = numpy.random.RandomState(1234)
-    ker1=2
-    ker2=2
     conv_layers=[]
-    conv_layers.append([[ker1,1,5,5], None, [2,2] ])
-    conv_layers.append([[ker2,ker1,5,5], None, [2,2] ])
+    init_layer = [ [ kernels[0][0],1,kernels[0][1],kernels[0][2] ], None, max_pool_layers[0] ]
+    conv_layers.append(init_layer)
+    conv_n_out = (32-kernels[0][2]+1)/max_pool_layers[0][0]
 
-    # Setup the MLP layers of the network
-    mlp_layers=[500]
-  
-    network = SdA(input = layer0_input, n_ins_mlp = ker2*4*4,
+    for i in range(1,len(kernels)):    
+        layer = [ [ kernels[i][0],kernels[i-1][0],kernels[i][1],kernels[i][2] ], None, max_pool_layers[i] ]
+        conv_layers.append(layer)
+        conv_n_out =  (conv_n_out - kernels[i][2]+1)/max_pool_layers[i][0]
+
+    network = SdA(input = layer0_input, n_ins_mlp = kernels[-1][0]*conv_n_out**2,
                   conv_hidden_layers_sizes = conv_layers,
                   mlp_hidden_layers_sizes = mlp_layers,
-                  corruption_levels = corruption_levels , n_out = 10,
+                  corruption_levels = corruption_levels , n_out = 62,
                   rng = rng , pretrain_lr = pretrain_lr ,
                   finetune_lr = learning_rate )