深度学习编程作业(识别图像是否为猫)

            import
             numpy as np

            import
             h5py

            import
             matplotlib.pyplot as plt

            import
             scipy

            from PIL import Image
from scipy import ndimage
from lr_utils import load_dataset

#%matplotlib inlinedef showPic(index):
    plt.imshow(train_set_x_orig[index])
    plt.show()
    ‘‘‘print("y = " + str(train_set_y[:, index]) + ", it‘s a ‘" + classes[np.squeeze(train_set_y[:, index])].decode(
        "utf-8") + "‘ picture.")‘‘‘def sigmoid(z):
    s = 1 / (1 + np.exp(-z))
    return s

def initialize_with_zeros(dim):
    w = np.zeros((dim, 1))
    b = 0
    assert (w.shape == (dim, 1))
    assert (isinstance(b, float) or isinstance(b, int))
    return w, b

def propagate(w, b, X, Y):
    m = X.shape[1]
    A = sigmoid(np.dot(w.T, X) + b)
    cost = -1/m * np.sum(Y * np.log(A) + (1-Y) * np.log(1-A))
    dw = 1/m * np.dot(X, (A-Y).T)
    db = 1/m * np.sum(A - Y)
    assert (dw.shape == w.shape)
    assert (db.dtype == float)
    cost = np.squeeze(cost)
    assert (cost.shape == ())
    grads = {"dw": dw,
             "db": db}
    return grads, cost

def optimize(w, b, X, Y, num_iterations, learning_rate, print_cost = False):
    costs = []
    for i in range(num_iterations):
        grades, cost = propagate(w, b, X, Y)
        dw = grades["dw"]
        db = grades["db"]
        w = w - learning_rate * dw
        b = b - learning_rate * db
        if i % 100 == 0:
            costs.append(cost)

        if print_cost and i % 100 ==0:
            print("Cost after iteration %i: %f" %(i, cost))

    params = {"w": w,
              "b": b}
    grades = {"dw": dw,
              "db": db}
    return params,grades,costs

def predict(w, b, X):
    m = X.shape[1]
    Y_prediction = np.zeros((1, m))
    w = w.reshape(X.shape[0], 1)
    A = sigmoid(np.dot(w.T, X) + b)
    for i in range(X.shape[1]):
        if(A[0][i] <= 0.5):
            Y_prediction[0][i] = 0
        else:
            Y_prediction[0][i] = 1
    assert (Y_prediction.shape == (1, m))
    return Y_prediction



def model(X_train, Y_train, X_test, Y_test, num_iterations=2000, learning_rate=0.5, print_cost=False):
    w,b = initialize_with_zeros(X_train.shape[0])
    params, grades, costs = optimize(w,b,X_train,Y_train,num_iterations,learning_rate,print_cost)
    w = params["w"]
    b = params["b"]
    Y_prediction_train = predict(w, b, X_train)
    Y_prediction_test = predict(w, b, X_test)
    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}
    return d


if__name__ ==‘__main__‘:
    train_set_x_orig, train_set_y, test_set_x_orig, test_set_y, classes = load_dataset()
    #train_set_x_orig is a numpy-array of shape (m_train, num_px, num_px, 3).#showPic(25)
    m_train = train_set_x_orig.shape[0]
    m_test = test_set_x_orig.shape[0]
    num_px = train_set_x_orig.shape[1]
    train_set_x_flatten = train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
    test_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0], -1).T
    train_set_x = train_set_x_flatten / 255.
    test_set_x = test_set_x_flatten / 255.
    d = model(train_set_x, train_set_y, test_set_x, test_set_y, num_iterations=5000, learning_rate=0.005, print_cost=True)
    ‘‘‘index = 1
    showPic(index)
    print("y = " + str(test_set_y[0, index]) + ", you predicted that it is a \"" + classes[
        int(d["Y_prediction_test"][0, index])].decode("utf-8") + "\" picture.")
    costs = np.squeeze(d[‘costs‘])
    plt.plot(costs)
    plt.ylabel(‘cost‘)
    plt.xlabel(‘iterations (per hundreds)‘)
    plt.title("Learning rate =" + str(d["learning_rate"]))
    plt.show()‘‘‘
    my_image="123.jpg"
    fname = "images/" + my_image
    image = np.array(ndimage.imread(fname, flatten=False))
    my_image = scipy.misc.imresize(image, size=(num_px, num_px)).reshape((1, num_px * num_px * 3)).T
    my_predicted_image = predict(d["w"], d["b"], my_image)
    plt.imshow(image)
    plt.show()
    print("y = " + str(np.squeeze(my_predicted_image)) + ", your algorithm predicts a \"" + classes[
        int(np.squeeze(my_predicted_image)),].decode("utf-8") + "\" picture.")

            import
             numpy as np

            import
             h5py
    
    

            def
             load_dataset():
    train_dataset = h5py.File(‘datasets/train_catvnoncat.h5‘, "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
    test_dataset = h5py.File(‘datasets/test_catvnoncat.h5‘, "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
    classes = np.array(test_dataset["list_classes"][:]) # the list of classes    
    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
    
    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes