code:neural_networks
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code:neural_networks [2016/10/18 20:31] (current) asa created |
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| + | === Neural networks === | ||
| + | Here's the neural network code I showed in class: | ||
| + | |||
| + | <file python 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 | ||
| + | |||
| + | def load_digits_data(corrupt=True) : | ||
| + | from sklearn.datasets import load_digits | ||
| + | digits = 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) | ||
| + | |||
| + | </file> | ||
code/neural_networks.txt ยท Last modified: 2016/10/18 20:31 by asa
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