基于Python实现浅层神经网络案例（源码）.rar

# Test: binary classification for colored points import numpy as np import matplotlib.pyplot as plt from nn_model import * from predict import * import sklearn import sklearn.datasets import sklearn.linear_model # Generate training data np.random.seed(1) m = 400 # number of samples N = int(m / 2) # number of points per class D = 2 # dimension X = np.zeros((m, D)) # data matrix where each row is a single example Y = np.zeros((m ,1), dtype = 'uint8') # labels vector (0 for red, 1 for blue) a = 4 # maximum radius of the flower for j in range(2): ix = range(N * j, N * (j + 1)) theta = np.linspace(j * 3.12, (j + 1) * 3.12, N) + np.random.randn(N) * 0.2 radius = a * np.sin(4 * theta) + np.random.randn(N) * 0.2 X[ix] = np.c_[radius * np.sin(theta), radius * np.cos(theta)] Y[ix] = j X = X.T Y = Y.T # Model training parameters = nn_model(X, Y, nh = 4, num_iterations = 10000, print_cost = True) # Accuracy predictions = predict(X, parameters) print('Training accuracy = %.2f ' % float((np.dot(Y, predictions.T) + np.dot(1 - Y, 1 - predictions.T)) / float(Y.size) * 100) + '%') # Binary classification x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 h = 0.01 # Meshgrid interval xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Generate a grid of points with interval h Z = predict(np.c_[xx.ravel(), yy.ravel()].T, parameters) Z = Z.reshape(xx.shape) plt.figure(2) plt.contourf(xx, yy, Z, cmap = plt.cm.Spectral) plt.xlabel('x1') plt.ylabel('x2') plt.scatter(X[0, :], X[1, :], c = Y.flatten(), s = 40, cmap = plt.cm.Spectral) plt.title("Decision Boundary for hidden layer size " + str(4)) # Tuning hidden layer size hidden_layer_sizes = [1, 3, 4, 5, 6, 20] plt.figure(3) for i, nh in enumerate(hidden_layer_sizes): plt.subplot(3, 2, i + 1) plt.title('Hidden layer of size %d' % nh) parameters = nn_model(X, Y, nh, num_iterations = 5000) Z = predict(np.c_[xx.ravel(), yy.ravel()].T, parameters) Z = Z.reshape(xx.shape) plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) plt.xlabel('x1') plt.ylabel('x2') plt.scatter(X[0, :], X[1, :], c=Y.flatten(), s=40, cmap=plt.cm.Spectral) predictions = predict(X, parameters) accuracy = float((np.dot(Y, predictions.T) + np.dot(1 - Y, 1 - predictions.T)) / float(Y.size) * 100) print('Training accuracy for {} hidden units: {} % '.format(nh, accuracy)) plt.show()