吴恩达深度学习第二课第二周作业

import numpy as np import matplotlib.pyplot as plt import h5py import scipy.io import sklearn import sklearn.datasets def sigmoid(x): """ Compute the sigmoid of x Arguments: x -- A scalar or numpy array of any size. Return: s -- sigmoid(x) """ s = 1/(1+np.exp(-x)) return s def relu(x): """ Compute the relu of x Arguments: x -- A scalar or numpy array of any size. Return: s -- relu(x) """ s = np.maximum(0,x) return s def load_params_and_grads(seed=1): np.random.seed(seed) W1 = np.random.randn(2,3) b1 = np.random.randn(2,1) W2 = np.random.randn(3,3) b2 = np.random.randn(3,1) dW1 = np.random.randn(2,3) db1 = np.random.randn(2,1) dW2 = np.random.randn(3,3) db2 = np.random.randn(3,1) return W1, b1, W2, b2, dW1, db1, dW2, db2 def initialize_parameters(layer_dims): """ Arguments: layer_dims -- python array (list) containing the dimensions of each layer in our network Returns: parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL": W1 -- weight matrix of shape (layer_dims[l], layer_dims[l-1]) b1 -- bias vector of shape (layer_dims[l], 1) Wl -- weight matrix of shape (layer_dims[l-1], layer_dims[l]) bl -- bias vector of shape (1, layer_dims[l]) Tips: - For example: the layer_dims for the "Planar Data classification model" would have been [2,2,1]. This means W1's shape was (2,2), b1 was (1,2), W2 was (2,1) and b2 was (1,1). Now you have to generalize it! - In the for loop, use parameters['W' + str(l)] to access Wl, where l is the iterative integer. """ np.random.seed(3) parameters = {} L = len(layer_dims) # number of layers in the network for l in range(1, L): parameters['W' + str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1])* np.sqrt(2 / layer_dims[l-1]) parameters['b' + str(l)] = np.zeros((layer_dims[l], 1)) assert(parameters['W' + str(l)].shape == layer_dims[l], layer_dims[l-1]) assert(parameters['W' + str(l)].shape == layer_dims[l], 1) return parameters def compute_cost(a3, Y): """ Implement the cost function Arguments: a3 -- post-activation, output of forward propagation Y -- "true" labels vector, same shape as a3 Returns: cost - value of the cost function """ m = Y.shape[1] logprobs = np.multiply(-np.log(a3),Y) + np.multiply(-np.log(1 - a3), 1 - Y) cost = 1./m * np.sum(logprobs) return cost def forward_propagation(X, parameters): """ Implements the forward propagation (and computes the loss) presented in Figure 2. Arguments: X -- input dataset, of shape (input size, number of examples) parameters -- python dictionary containing your parameters "W1", "b1", "W2", "b2", "W3", "b3": W1 -- weight matrix of shape () b1 -- bias vector of shape () W2 -- weight matrix of shape () b2 -- bias vector of shape () W3 -- weight matrix of shape () b3 -- bias vector of shape () Returns: loss -- the loss function (vanilla logistic loss) """ # retrieve parameters W1 = parameters["W1"] b1 = parameters["b1"] W2 = parameters["W2"] b2 = parameters["b2"] W3 = parameters["W3"] b3 = parameters["b3"] # LINEAR -> RELU -> LINEAR -> RELU -> LINEAR -> SIGMOID z1 = np.dot(W1, X) + b1 a1 = relu(z1) z2 = np.dot(W2, a1) + b2 a2 = relu(z2) z3 = np.dot(W3, a2) + b3 a3 = sigmoid(z3) cache = (z1, a1, W1, b1, z2, a2, W2, b2, z3, a3, W3, b3) return a3, cache def backward_propagation(X, Y, cache): """ Implement the backward propagation presented in figure 2. Arguments: X -- input dataset, of shape (input size, number of examples) Y -- true "label" vector (containing 0 if cat, 1 if non-cat) cache -- cache output from forward_propagation() Returns: gradients -- A dictionary with the gradients with respect to each parameter, activation and pre-activation variables """ m = X.shape[1] (z1, a1, W1, b1, z2, a2, W2, b2, z3, a3, W3, b3) = cache dz3 = 1./m * (a3 - Y) dW3 = np.dot(dz3, a2.T) db3 = np.sum(dz3, axis=1, keepdims = True) da2 = np.dot(W3.T, dz3) dz2 = np.multiply(da2, np.int64(a2 > 0)) dW2 = np.dot(dz2, a1.T) db2 = np.sum(dz2, axis=1, keepdims = True) da1 = np.dot(W2.T, dz2) dz1 = np.multiply(da1, np.int64(a1 > 0)) dW1 = np.dot(dz1, X.T) db1 = np.sum(dz1, axis=1, keepdims = True) gradients = {"dz3": dz3, "dW3": dW3, "db3": db3, "da2": da2, "dz2": dz2, "dW2": dW2, "db2": db2, "da1": da1, "dz1": dz1, "dW1": dW1, "db1": db1} return gradients def predict(X, y, parameters): """ This function is used to predict the results of a n-layer neural network. Arguments: X -- data set of examples you would like to label parameters -- parameters of the trained model Returns: p -- predictions for the given dataset X """ m = X.shape[1] p = np.zeros((1,m), dtype = np.int) # Forward propagation a3, caches = forward_propagation(X, parameters) # convert probas to 0/1 predictions for i in range(0, a3.shape[1]): if a3[0,i] > 0.5: p[0,i] = 1 else: p[0,i] = 0 # print results #print ("predictions: " + str(p[0,:])) #print ("true labels: " + str(y[0,:])) print("Accuracy: " + str(np.mean((p[0,:] == y[0,:])))) return p def load_2D_dataset(): data = scipy.io.loadmat('datasets/data.mat') train_X = data['X'].T train_Y = data['y'].T test_X = data['Xval'].T test_Y = data['yval'].T plt.scatter(train_X[0, :], train_X[1, :], c=train_Y, s=40, cmap=plt.cm.Spectral); return train_X, train_Y, test_X, test_Y def plot_decision_boundary(model, X, y): # Set min and max values and give it some padding 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 # 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 grid Z = model(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.ylabel('x2') plt.xlabel('x1') plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) plt.show() def predict_dec(parameters, X): """ Used for plotting decision boundary. Arguments: parameters -- python dictionary containing your parameters X -- input data of size (m, K) Returns predictions -- vector of predictions of our model (red: 0 / blue: 1) """ # Predict using forward propagation and a classification threshold of 0.5 a3, cache = forward_propagation(X, parameters) predictions = (a3 > 0.5) return predictions def load_dataset(): np.random.seed(3) train_X, train_Y = sklearn.datasets.make_moons(n_samples=300, noise=.2) #300 #0.2 # Visualize the data plt.scatter(train_X[:, 0], train_X[:, 1], c=train_Y, s=40, cmap=plt.cm.Spectral); train_X = train_X.T train_Y = train_Y.reshape((1, train_Y.shape[0])) return train_X, train_Y