#### Neural networks

Here's the neural network code I showed in class:

nnet.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

from sklearn.datasets import load_digits
X = digits.data
if (corrupt) :
X = X + np.random.binomial(1, 0.5, X.shape) * np.random.uniform(1, 15, X.shape)
y = digits.target
X /= X.max()
return X,y

def train_test_evaluate(X, y) :
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.preprocessing import LabelBinarizer
layers = [64, 100, 20, 10]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
labels_train = LabelBinarizer().fit_transform(y_train)
labels_test = LabelBinarizer().fit_transform(y_test)

nn = NeuralNetwork(layers, 'logistic')
nn.fit(X_train,labels_train,epochs=10)
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__' :
X,y = load_digits_data(corrupt = True)
train_test_evaluate(X, y)