--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/mnist_sda.py	Fri Feb 26 14:15:38 2010 -0500
@@ -0,0 +1,44 @@
+#!/usr/bin/python
+# coding: utf-8
+
+# Parameterize call to sgd_optimization for MNIST
+
+import numpy 
+import theano
+import time
+import theano.tensor as T
+from theano.tensor.shared_randomstreams import RandomStreams
+
+from sgd_optimization import SdaSgdOptimizer
+import cPickle, gzip
+from jobman import DD
+
+MNIST_LOCATION = '/u/savardf/datasets/mnist.pkl.gz'
+
+def sgd_optimization_mnist(learning_rate=0.1, pretraining_epochs = 2, \
+                            pretrain_lr = 0.1, training_epochs = 5, \
+                            dataset='mnist.pkl.gz'):
+    # Load the dataset 
+    f = gzip.open(dataset,'rb')
+    # this gives us train, valid, test (each with .x, .y)
+    dataset = cPickle.load(f)
+    f.close()
+
+    n_ins = 28*28
+    n_outs = 10
+
+    hyperparameters = DD({'finetuning_lr':learning_rate,
+                       'pretraining_lr':pretrain_lr,
+                       'pretraining_epochs_per_layer':pretraining_epochs,
+                       'max_finetuning_epochs':training_epochs,
+                       'hidden_layers_sizes':[100],
+                       'corruption_levels':[0.2],
+                       'minibatch_size':20})
+
+    optimizer = SdaSgdOptimizer(dataset, hyperparameters, n_ins, n_outs)
+    optimizer.pretrain()
+    optimizer.finetune()
+
+if __name__ == '__main__':
+    sgd_optimization_mnist(dataset=MNIST_LOCATION)
+