# Python3机器学习个例

2017-01-13 11:33:54来源:CSDN作者:ufo0913人点击

`__author__ = 'm.bashari'import numpy as npfrom sklearn import datasets, linear_modelimport matplotlib.pyplot as pltdef generate_data():    np.random.seed(0)    X, y = datasets.make_moons(200, noise=0.20)    return X, ydef visualize(X, y, clf):    # plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.Spectral)    # plt.show()    plot_decision_boundary(lambda x: clf.predict(x), X, y)def plot_decision_boundary(pred_func, X, y):    # Set min and max values and give it some padding    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5    h = 0.01    # Generate a grid of points with distance h between them    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))    # Predict the function value for the whole gid    Z = pred_func(np.c_[xx.ravel(), yy.ravel()])    Z = Z.reshape(xx.shape)    # Plot the contour and training examples    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)    plt.title("Logistic Regression")    plt.show()def classify(X, y):    clf = linear_model.LogisticRegressionCV()    clf.fit(X, y)    return clfdef main():    X, y = generate_data()    # visualize(X, y)    clf = classify(X, y)    visualize(X, y, clf)if __name__ == "__main__":    main()`

`__author__ = 'm.bashari'import numpy as npfrom sklearn import datasets, linear_modelimport matplotlib.pyplot as pltclass Config:    nn_input_dim = 2  # input layer dimensionality    nn_output_dim = 2  # output layer dimensionality    # Gradient descent parameters (I picked these by hand)    epsilon = 0.01  # learning rate for gradient descent    reg_lambda = 0.01  # regularization strengthdef generate_data():    np.random.seed(0)    X, y = datasets.make_moons(200, noise=0.20)    return X, ydef visualize(X, y, model):    # plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.Spectral)    # plt.show()    plot_decision_boundary(lambda x:predict(model,x), X, y)def plot_decision_boundary(pred_func, X, y):    # Set min and max values and give it some padding    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5    h = 0.01    # Generate a grid of points with distance h between them    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))    # Predict the function value for the whole gid    Z = pred_func(np.c_[xx.ravel(), yy.ravel()])    Z = Z.reshape(xx.shape)    # Plot the contour and training examples    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral)    plt.title("Decision Boundary for hidden layer size 3")    plt.show()# Helper function to evaluate the total loss on the datasetdef calculate_loss(model, X, y):    num_examples = len(X)  # training set size    W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2']    # Forward propagation to calculate our predictions    z1 = X.dot(W1) + b1    a1 = np.tanh(z1)    z2 = a1.dot(W2) + b2    exp_scores = np.exp(z2)    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)    # Calculating the loss    corect_logprobs = -np.log(probs[range(num_examples), y])    data_loss = np.sum(corect_logprobs)    # Add regulatization term to loss (optional)    data_loss += Config.reg_lambda / 2 * (np.sum(np.square(W1)) + np.sum(np.square(W2)))    return 1. / num_examples * data_lossdef predict(model, x):    W1, b1, W2, b2 = model['W1'], model['b1'], model['W2'], model['b2']    # Forward propagation    z1 = x.dot(W1) + b1    a1 = np.tanh(z1)    z2 = a1.dot(W2) + b2    exp_scores = np.exp(z2)    probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)    return np.argmax(probs, axis=1)# This function learns parameters for the neural network and returns the model.# - nn_hdim: Number of nodes in the hidden layer# - num_passes: Number of passes through the training data for gradient descent# - print_loss: If True, print the loss every 1000 iterationsdef build_model(X, y, nn_hdim, num_passes=20000, print_loss=False):    # Initialize the parameters to random values. We need to learn these.    num_examples = len(X)    np.random.seed(0)    W1 = np.random.randn(Config.nn_input_dim, nn_hdim) / np.sqrt(Config.nn_input_dim)    b1 = np.zeros((1, nn_hdim))    W2 = np.random.randn(nn_hdim, Config.nn_output_dim) / np.sqrt(nn_hdim)    b2 = np.zeros((1, Config.nn_output_dim))    # This is what we return at the end    model = {}    # Gradient descent. For each batch...    for i in range(0, num_passes):        # Forward propagation        z1 = X.dot(W1) + b1        a1 = np.tanh(z1)        z2 = a1.dot(W2) + b2        exp_scores = np.exp(z2)        probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)        # Backpropagation        delta3 = probs        delta3[range(num_examples), y] -= 1        dW2 = (a1.T).dot(delta3)        db2 = np.sum(delta3, axis=0, keepdims=True)        delta2 = delta3.dot(W2.T) * (1 - np.power(a1, 2))        dW1 = np.dot(X.T, delta2)        db1 = np.sum(delta2, axis=0)        # Add regularization terms (b1 and b2 don't have regularization terms)        dW2 += Config.reg_lambda * W2        dW1 += Config.reg_lambda * W1        # Gradient descent parameter update        W1 += -Config.epsilon * dW1        b1 += -Config.epsilon * db1        W2 += -Config.epsilon * dW2        b2 += -Config.epsilon * db2        # Assign new parameters to the model        model = {'W1': W1, 'b1': b1, 'W2': W2, 'b2': b2}        # Optionally print the loss.        # This is expensive because it uses the whole dataset, so we don't want to do it too often.        if print_loss and i % 1000 == 0:            print("Loss after iteration %i: %f" % (i, calculate_loss(model, X, y)))    return modeldef classify(X, y):    # clf = linear_model.LogisticRegressionCV()    # clf.fit(X, y)    # return clf    passdef main():    X, y = generate_data()    model = build_model(X, y, 3, print_loss=True) # 隐藏层设置为3    visualize(X, y, model)if __name__ == "__main__":    main()`