吴恩达+neural-networks-deep-learning+第二周作业

import numpy as npimport matplotlib.pyplot as pltimport h5pyimport scipyfrom PIL import Imagefrom scipy import ndimagefrom lr_utils import load_dataset%matplotlib inline

### START CODE HERE ### (≈ 3 lines of code)m_train = train_set_x_orig.shape[0]m_test = test_set_x_orig.shape[0]num_px = train_set_x_orig.shape[1]### END CODE HERE ###print ("Number of training examples: m_train = " + str(m_train))print ("Number of testing examples: m_test = " + str(m_test))print ("Height/Width of each image: num_px = " + str(num_px))print ("Each image is of size: (" + str(num_px) + ", " + str(num_px) + ", 3)")print ("train_set_x shape: " + str(train_set_x_orig.shape))print ("train_set_y shape: " + str(train_set_y.shape))print ("test_set_x shape: " + str(test_set_x_orig.shape))print ("test_set_y shape: " + str(test_set_y.shape))

# Reshape the training and test examples### START CODE HERE ### (≈ 2 lines of code)train_set_x_flatten = train_set_x_orig.reshape(-1,train_set_x_orig.shape[1]*train_set_x_orig.shape[2]*3).Ttest_set_x_flatten = test_set_x_orig.reshape(-1,test_set_x_orig.shape[1]*test_set_x_orig.shape[2]*3).T### END CODE HERE ###print ("train_set_x_flatten shape: " + str(train_set_x_flatten.shape))print ("train_set_y shape: " + str(train_set_y.shape))print ("test_set_x_flatten shape: " + str(test_set_x_flatten.shape))print ("test_set_y shape: " + str(test_set_y.shape))print ("sanity check after reshaping: " + str(train_set_x_flatten[0:5,0]))

train_set_x = train_set_x_flatten/255.test_set_x = test_set_x_flatten/255.

def propagate(w, b, X, Y): ???""" ???Implement the cost function and its gradient for the propagation explained above ???Arguments: ???w -- weights, a numpy array of size (num_px * num_px * 3, 1) ???b -- bias, a scalar ???X -- data of size (num_px * num_px * 3, number of examples) ???Y -- true "label" vector (containing 0 if non-cat, 1 if cat) of size (1, number of examples) ???Return: ???cost -- negative log-likelihood cost for logistic regression ???dw -- gradient of the loss with respect to w, thus same shape as w ???db -- gradient of the loss with respect to b, thus same shape as b ???????Tips: ???- Write your code step by step for the propagation. np.log(), np.dot() ???""" ???????m = X.shape[1] ???????# FORWARD PROPAGATION (FROM X TO COST) ???### START CODE HERE ### (≈ 2 lines of code) ???A = sigmoid(np.dot(w.T,X)+b) ???????????????????????????????????# compute activation ???cost = -1/m*((np.dot(Y,np.log(A).T))+(np.dot(1-Y,np.log(1-A).T))) ????????????????????????????????# compute cost ???### END CODE HERE ### ???????# BACKWARD PROPAGATION (TO FIND GRAD) ???### START CODE HERE ### (≈ 2 lines of code) ???dw = 1/m*np.dot(X,(A-Y).T) ???db = 1/m*np.sum(A-Y) ???### END CODE HERE ### ???assert(dw.shape == w.shape) ???assert(db.dtype == float) ???cost = np.squeeze(cost) ???assert(cost.shape == ()) ???????grads = {"dw": dw, ????????????"db": db} ???????return grads, cost

# GRADED FUNCTION: optimizedef optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False): ???""" ???This function optimizes w and b by running a gradient descent algorithm ???????Arguments: ???w -- weights, a numpy array of size (num_px * num_px * 3, 1) ???b -- bias, a scalar ???X -- data of shape (num_px * num_px * 3, number of examples) ???Y -- true "label" vector (containing 0 if non-cat, 1 if cat), of shape (1, number of examples) ???num_iterations -- number of iterations of the optimization loop ???learning_rate -- learning rate of the gradient descent update rule ???print_cost -- True to print the loss every 100 steps ???????Returns: ???params -- dictionary containing the weights w and bias b ???grads -- dictionary containing the gradients of the weights and bias with respect to the cost function ???costs -- list of all the costs computed during the optimization, this will be used to plot the learning curve. ???????Tips: ???You basically need to write down two steps and iterate through them: ???????1) Calculate the cost and the gradient for the current parameters. Use propagate(). ???????2) Update the parameters using gradient descent rule for w and b. ???""" ???????costs = [] ???????for i in range(num_iterations): ???????????????????????# Cost and gradient calculation (≈ 1-4 lines of code) ???????### START CODE HERE ### ????????grads, cost = propagate(w,b,X,Y) ???????### END CODE HERE ### ???????????????# Retrieve derivatives from grads ???????dw = grads["dw"] ???????db = grads["db"] ???????????????# update rule (≈ 2 lines of code) ???????### START CODE HERE ### ???????w = w-learning_rate*dw ???????b = b-learning_rate*db ???????### END CODE HERE ### ???????????????# Record the costs ???????if i % 100 == 0: ???????????costs.append(cost) ???????????????# Print the cost every 100 training examples ???????if print_cost and i % 100 == 0: ???????????print ("Cost after iteration %i: %f" %(i, cost)) ???????params = {"w": w, ?????????????"b": b} ???????grads = {"dw": dw, ????????????"db": db} ???????return params, grads, costs

# GRADED FUNCTION: predictdef predict(w, b, X): ???‘‘‘ ???Predict whether the label is 0 or 1 using learned logistic regression parameters (w, b) ???????Arguments: ???w -- weights, a numpy array of size (num_px * num_px * 3, 1) ???b -- bias, a scalar ???X -- data of size (num_px * num_px * 3, number of examples) ???????Returns: ???Y_prediction -- a numpy array (vector) containing all predictions (0/1) for the examples in X ???‘‘‘ ???????m = X.shape[1] ???Y_prediction = np.zeros((1,m)) ???w = w.reshape(X.shape[0], 1) ???????# Compute vector "A" predicting the probabilities of a cat being present in the picture ???### START CODE HERE ### (≈ 1 line of code) ???A = sigmoid(np.dot(w.T,X)+b) ???### END CODE HERE ### ???????######### ???Y_prediction=A>0.5 ???Y_prediction=Y_prediction.astype(float) ???######### ???????for i in range(A.shape[1]): ???????????????# Convert probabilities A[0,i] to actual predictions p[0,i] ???????### START CODE HERE ### (≈ 4 lines of code) ???????pass ???????### END CODE HERE ### ???????assert(Y_prediction.shape == (1, m)) ???????return Y_prediction

# GRADED FUNCTION: modeldef model(X_train, Y_train, X_test, Y_test, num_iterations = 2000, learning_rate = 0.5, print_cost = False): ???""" ???Builds the logistic regression model by calling the function you‘ve implemented previously ???????Arguments: ???X_train -- training set represented by a numpy array of shape (num_px * num_px * 3, m_train) ???Y_train -- training labels represented by a numpy array (vector) of shape (1, m_train) ???X_test -- test set represented by a numpy array of shape (num_px * num_px * 3, m_test) ???Y_test -- test labels represented by a numpy array (vector) of shape (1, m_test) ???num_iterations -- hyperparameter representing the number of iterations to optimize the parameters ???learning_rate -- hyperparameter representing the learning rate used in the update rule of optimize() ???print_cost -- Set to true to print the cost every 100 iterations ???????Returns: ???d -- dictionary containing information about the model. ???""" ???????### START CODE HERE ### ???????# initialize parameters with zeros (≈ 1 line of code) ???w, b = initialize_with_zeros(X_train.shape[0]) ???# Gradient descent (≈ 1 line of code) ???parameters, grads, costs = optimize(w, b , X_train , Y_train , num_iterations , learning_rate , print_cost = False) ???????# Retrieve parameters w and b from dictionary "parameters" ???w = parameters["w"] ???b = parameters["b"] ???????# Predict test/train set examples (≈ 2 lines of code) ???Y_prediction_test = predict(w,b,X_test) ???Y_prediction_train = predict(w,b,X_train) ???### END CODE HERE ### ???# Print train/test Errors ???print("train accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100)) ???print("test accuracy: {} %".format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100)) ???????d = {"costs": costs, ????????"Y_prediction_test": Y_prediction_test, ?????????"Y_prediction_train" : Y_prediction_train, ?????????"w" : w, ?????????"b" : b, ????????"learning_rate" : learning_rate, ????????"num_iterations": num_iterations} ???print(d["costs"]) ???return d