--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/stacked_dae/nist_sda.py	Fri Feb 19 08:43:10 2010 -0500
@@ -0,0 +1,150 @@
+#!/usr/bin/python
+# coding: utf-8
+
+import numpy 
+import theano
+import time
+import theano.tensor as T
+from theano.tensor.shared_randomstreams import RandomStreams
+
+import os.path
+
+from sgd_optimization import sgd_optimization
+
+from jobman import DD
+from pylearn.io import filetensor
+
+from utils import produit_croise_jobs
+
+NIST_ALL_LOCATION = '/data/lisa/data/nist/by_class/all'
+
+# Just useful for tests... minimal number of epochs
+DEFAULT_HP_NIST = DD({'finetuning_lr':0.1,
+                       'pretraining_lr':0.1,
+                       'pretraining_epochs_per_layer':1,
+                       'max_finetuning_epochs':1,
+                       'hidden_layers_sizes':[1000,1000],
+                       'corruption_levels':[0.2,0.2],
+                       'minibatch_size':20})
+
+def jobman_entrypoint_nist(state, channel):
+    sgd_optimization_nist(state)
+
+def jobman_insert_nist():
+    vals = {'finetuning_lr': [0.00001, 0.0001, 0.001, 0.01, 0.1],
+            'pretraining_lr': [0.00001, 0.0001, 0.001, 0.01, 0.1],
+            'pretraining_epochs_per_layer': [2,5,20],
+            'hidden_layer_sizes': [100,300,1000],
+            'num_hidden_layers':[1,2,3],
+            'corruption_levels': [0.1,0.2,0.4],
+            'minibatch_size': [5,20,100]}
+
+    jobs = produit_croise_jobs(vals)
+
+    for job in jobs:
+        insert_job(job)
+
+
+class NIST:
+    def __init__(self, minibatch_size, basepath=None):
+        global NIST_ALL_LOCATION
+
+        self.minibatch_size = minibatch_size
+        self.basepath = basepath and basepath or NIST_ALL_LOCATION
+
+        self.set_filenames()
+
+        # arrays of 2 elements: .x, .y
+        self.train = [None, None]
+        self.test = [None, None]
+
+        self.load_train_test()
+
+        self.valid = [[], []]
+        #self.split_train_valid()
+
+
+    def get_tvt(self):
+        return self.train, self.valid, self.test
+
+    def set_filenames(self):
+        self.train_files = ['all_train_data.ft',
+                                'all_train_labels.ft']
+
+        self.test_files = ['all_test_data.ft',
+                            'all_test_labels.ft']
+
+    def load_train_test(self):
+        self.load_data_labels(self.train_files, self.train)
+        self.load_data_labels(self.test_files, self.test)
+
+    def load_data_labels(self, filenames, pair):
+        for i, fn in enumerate(filenames):
+            f = open(os.path.join(self.basepath, fn))
+            pair[i] = filetensor.read(f)
+            f.close()
+
+    def split_train_valid(self):
+        test_len = len(self.test[0])
+        
+        new_train_x = self.train[0][:-test_len]
+        new_train_y = self.train[1][:-test_len]
+
+        self.valid[0] = self.train[0][-test_len:]
+        self.valid[1] = self.train[1][-test_len:]
+
+        self.train[0] = new_train_x
+        self.train[1] = new_train_y
+
+def test_load_nist():
+    print "Will load NIST"
+
+    import time
+    t1 = time.time()
+    nist = NIST(20)
+    t2 = time.time()
+
+    print "NIST loaded. time delta = ", t2-t1
+
+    tr,v,te = nist.get_tvt()
+
+    print "Lenghts: ", len(tr[0]), len(v[0]), len(te[0])
+
+    raw_input("Press any key")
+
+# hp for hyperparameters
+def sgd_optimization_nist(hp=None, dataset_dir='/data/lisa/data/nist'):
+    global DEFAULT_HP_NIST
+    hp = hp and hp or DEFAULT_HP_NIST
+
+    print "Will load NIST"
+
+    import time
+    t1 = time.time()
+    nist = NIST(20)
+    t2 = time.time()
+
+    print "NIST loaded. time delta = ", t2-t1
+
+    train,valid,test = nist.get_tvt()
+    dataset = (train,valid,test)
+
+    print "Lenghts train, valid, test: ", len(train[0]), len(valid[0]), len(test[0])
+
+    n_ins = 32*32
+    n_outs = 62 # 10 digits, 26*2 (lower, capitals)
+
+    sgd_optimization(dataset, hp, n_ins, n_outs)
+
+if __name__ == '__main__':
+
+    import sys
+
+    args = sys.argv[1:]
+
+    if len(args) > 0 and args[0] == 'load_nist':
+        test_load_nist()
+
+    else:
+        sgd_optimization_nist()
+