make the number of hidden unit for mlp parametrisable.

Author: nouiz

code/mlp.py

@@ -174,7 +174,7 @@ def __init__(self, rng, input, n_in, n_hidden, n_out):
 
 
 def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
-             dataset='../data/mnist.pkl.gz', batch_size=20):
+             dataset='../data/mnist.pkl.gz', batch_size=20, n_hidden=500):
     """
     Demonstrate stochastic gradient descent optimization for a multilayer
     perceptron
@@ -227,7 +227,8 @@ def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000,
     rng = numpy.random.RandomState(1234)
 
     # construct the MLP class
-    classifier = MLP(rng=rng, input=x, n_in=28 * 28, n_hidden=500, n_out=10)
+    classifier = MLP(rng=rng, input=x, n_in=28 * 28,
+                     n_hidden=n_hidden, n_out=10)
 
     # the cost we minimize during training is the negative log likelihood of
     # the model plus the regularization terms (L1 and L2); cost is expressed