Updating the MNIST benchmarking to compare nolearn and lasagne.

examples/bench_mnist.py

@@ -1,7 +1,9 @@
 from sklearn.cross_validation import train_test_split
 from sklearn.datasets import fetch_mldata
-import logging
+
 import sys
+import time
+import logging
 import numpy as np
 
 np.set_printoptions(precision=4)
@@ -21,52 +23,79 @@
 classifiers = []
 
 
+try:
+    from nolearn.dbn import DBN
+    clf = DBN(
+        [X_train.shape[1], 300, 10],
+        learn_rates=0.3,
+        learn_rate_decays=0.9,
+        epochs=10,
+        verbose=1)
+    classifiers.append(('nolearn.dbn', clf))
+except ImportError:
+    pass
 
 
-# try:
-#     raise ImportError
-#
-#     from nolearn.dbn import DBN
-#     clf = DBN(
-#         [X_train.shape[1], 300, 10],
-#         learn_rates=0.3,
-#         learn_rate_decays=0.9,
-#         epochs=10,
-#         verbose=1)
-#     classifiers.append(('nolearn.dbn', clf))
-# except ImportError:
-#     pass
+try:
+    from nolearn.lasagne import NeuralNet
+    from lasagne.layers import InputLayer, DenseLayer
+    from lasagne.nonlinearities import softmax
+    from lasagne.updates import nesterov_momentum
+
+    clf = NeuralNet(
+        layers=[
+            ('input', InputLayer),
+            ('hidden1', DenseLayer),
+            ('output', DenseLayer),
+            ],
+        input_shape=(None, 784),
+        output_num_units=10,
+        output_nonlinearity=softmax,
+        eval_size=0.0,
+
+        more_params=dict(
+            hidden1_num_units=300,
+        ),
+
+        update=nesterov_momentum,
+        update_learning_rate=0.02,
+        update_momentum=0.9,
+
+        max_epochs=10,
+        verbose=1
+        )
+    classifiers.append(('nolearn.lasagne', clf))
+
+except ImportError:
+    pass
 
 
 try:
     from sknn.mlp import MultiLayerPerceptronClassifier
 
     clf = MultiLayerPerceptronClassifier(
-        layers=[("Maxout", 50, 2),("Maxout", 50, 2), ("Softmax",)],
-        learning_rate=0.01,
-        batch_size=10,
-        n_stable=50,
-        f_stable=0.0,
-        n_iter=2,
+        layers=[("Rectifier", 300), ("Softmax",)],
+        learning_rate=0.02,
         learning_rule='momentum',
-        # dropout=True,
-        verbose=1,
+        batch_size=25,
+        n_stable=10,
+        n_iter=10,
+        verbose=0,
     )
     classifiers.append(('sknn.mlp', clf))
 except ImportError:
     pass
 
 
 for name, clf in classifiers:
-    print y_train
-
-    clf.fit(X_train, y_train)
+    start = time.time()
+    clf.fit(X_train.astype(np.float32), y_train.astype(np.int32))
 
     from sklearn.metrics import classification_report
 
     y_pred = clf.predict(X_test)
     print name
-    #exit()
     print "\tAccuracy:", clf.score(X_test, y_test)
+    print "\tTime:", time.time() - start
     print "\tReport:"
     print classification_report(y_test, y_pred)