arngarden · July 26, 2013 10:13 · Jul 26, 2013
diff --git a/gistfile1.py b/gistfile1.py
@@ -0,0 +1,53 @@
+import theano
+from pylearn2.models import mlp
+from pylearn2.training_algorithms import sgd
+from pylearn2.termination_criteria import EpochCounter
+from pylearn2.datasets.dense_design_matrix import DenseDesignMatrix
+import numpy as np
+from random import randint
+
+
+class XOR(DenseDesignMatrix):
+    def __init__(self):
+        self.class_names = ['0', '1']
+        X = [[randint(0, 1), randint(0, 1)] for _ in range(1000)]
+        y = []
+        for a, b in X:
+            if a + b == 1:
+                y.append([0, 1])
+            else:
+                y.append([1, 0])
+        X = np.array(X)
+        y = np.array(y)
+        super(XOR, self).__init__(X=X, y=y)
+
+
+# create XOR dataset
+ds = XOR()
+# create hidden layer with 2 nodes, init weights in range -0.1 to 0.1 and add
+# a bias with value 1
+hidden_layer = mlp.Sigmoid(layer_name='hidden', dim=2, irange=.1, init_bias=1.)
+# create Softmax output layer
+output_layer = mlp.Softmax(2, 'output', irange=.1)
+# create Stochastic Gradient Descent trainer that runs for 400 epochs
+trainer = sgd.SGD(learning_rate=.05, batch_size=10, termination_criterion=EpochCounter(400))
+layers = [hidden_layer, output_layer]
+# create neural net that takes two inputs
+ann = mlp.MLP(layers, nvis=2)
+trainer.setup(ann, ds)
+# train neural net until the termination criterion is true
+while True:
+    trainer.train(dataset=ds)
+    ann.monitor.report_epoch()
+    ann.monitor()
+    if not trainer.continue_learning(ann):
+        break
+
+inputs = np.array([[0, 0]])
+print ann.fprop(theano.shared(inputs, name='inputs')).eval()
+inputs = np.array([[0, 1]])
+print ann.fprop(theano.shared(inputs, name='inputs')).eval()
+inputs = np.array([[1, 0]])
+print ann.fprop(theano.shared(inputs, name='inputs')).eval()
+inputs = np.array([[1, 1]])
+print ann.fprop(theano.shared(inputs, name='inputs')).eval()