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--- code:neural_network [2016/08/09 10:25]
+++ code:neural_network [2016/08/09 10:25] (current)
@@ Line 1: / Line 1: @@
+==== Neural networks in Python ====
+<file python neural_network.py>
+"""
+Feed-forward neural networks trained using backpropagation
+based on code from http://rolisz.ro/2013/04/18/neural-networks-in-python/
+"""
+import numpy as np
+def tanh(x):
+    return np.tanh(x)
+def tanh_deriv(x):
+    return 1.0 - np.tanh(x)**2
+def logistic(x):
+    return 1/(1 + np.exp(-x))
+def logistic_derivative(x):
+    return logistic(x)*(1-logistic(x))
+class NeuralNetwork:
+    def __init__(self, layers, activation='tanh') :
+        """
+        layers: A list containing the number of units in each layer.
+                Should contain at least two values
+        activation: The activation function to be used. Can be
+                "logistic" or "tanh"
+        """
+        if activation == 'logistic':
+            self.activation = logistic
+            self.activation_deriv = logistic_derivative
+        elif activation == 'tanh':
+            self.activation = tanh
+            self.activation_deriv = tanh_deriv
+        self.num_layers = len(layers) - 1
+        self.weights = [ np.random.randn(layers[i - 1] + 1, layers[i] + 1)/10 for i in range(1, len(layers) - 1) ]
+        self.weights.append(np.random.randn(layers[-2] + 1, layers[-1])/10)
+    def forward(self, x) :
+        """
+        compute the activation of each layer in the network
+        """
+        a = [x]
+        for i in range(self.num_layers) :
+            a.append(self.activation(np.dot(a[i], self.weights[i])))
+        return a
+    def backward(self, y, a) :
+        """
+        compute the deltas for example i
+        """
+        deltas = [(y - a[-1]) * self.activation_deriv(a[-1])]
+        for l in range(len(a) - 2, 0, -1): # we need to begin at the second to last layer
+            deltas.append(deltas[-1].dot(self.weights[l].T)*self.activation_deriv(a[l]))
+        deltas.reverse()
+        return deltas
+    def fit(self, X, y, learning_rate=0.2, epochs=50):
+        X = np.asarray(X)
+        temp = np.ones( (X.shape[0], X.shape[1]+1))
+        temp[:, 0:-1] = X  # adding the bias unit to the input layer
+        X = temp
+        y = np.asarray(y)
+        for k in range(epochs):
+            if k%10==0 : print "***************** ", k, "epochs  ***************"
+            I = np.random.permutation(X.shape[0])
+            for i in I :
+                a = self.forward(X[i])
+                deltas = self.backward(y[i], a)
+                # update the weights using the activations and deltas:
+                for i in range(len(self.weights)):
+                    layer = np.atleast_2d(a[i])
+                    delta = np.atleast_2d(deltas[i])
+                    self.weights[i] += learning_rate * layer.T.dot(delta)
+    def predict(self, x):
+        x = np.asarray(x)
+        temp = np.ones(x.shape[0]+1)
+        temp[0:-1] = x
+        a = temp
+        for l in range(0, len(self.weights)):
+            a = self.activation(np.dot(a, self.weights[l]))
+        return a
+def test_digits() :
+    from sklearn.cross_validation import train_test_split
+    from sklearn.datasets import load_digits
+    from sklearn.metrics import confusion_matrix, classification_report
+    from sklearn.preprocessing import LabelBinarizer
+    digits = load_digits()
+    X = digits.data
+    y = digits.target
+    X /= X.max()
+    nn = NeuralNetwork([64,100,10],'logistic')
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)
+    labels_train = LabelBinarizer().fit_transform(y_train)
+    labels_test = LabelBinarizer().fit_transform(y_test)
+    nn.fit(X_train,labels_train,epochs=100)
+    predictions = []
+    for i in range(X_test.shape[0]) :
+        o = nn.predict(X_test[i])
+        predictions.append(np.argmax(o))
+    print confusion_matrix(y_test,predictions)
+    print classification_report(y_test,predictions)
+if __name__=='__main__' :
+    w = np.random.uniform(-1, 1, 2)
+    X = np.random.uniform(-1, 1, [20, 2])
+    y = (np.sign(np.dot(X, w)) + 1) / 2
+    network = NeuralNetwork([2,2,1], 'logistic')
+    network.fit(X,y, epochs=100)
+    for i in range(len(X)) :
+        print i, y[i], network.predict(X[i])
+</file>

CS545 fall 2016

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