import tensorflow as tf import numpy as np import matplotlib.pylab as plt plt.rcParams["font.family"] = 'SimHei' # 将字体改为中文 plt.rcParams['axes.unicode_minus'] = False # 设置了中文字体默认后，坐标的"-"号无法显示，设置这个参数就可以避免 # 加载手写数字数据 mnist = tf.keras.datasets.mnist (train_x, train_y), (test_x, test_y) = mnist.load_data() # 将0到9转化为one-hot编码 y_hot = np.zeros((10, 10)) for i in range(y_hot.shape[0]): y_hot[i, i] = 1 # print('y_hot:', y_hot) # 将标记值转化为one-hot编码 train_Y = np.zeros((train_y.shape[0], 10)) for i in range(train_y.shape[0]): train_Y[i] = y_hot[train_y[i]] print('train_Y:', train_Y, train_Y.shape) # 将28*28展开为784*1 # 训练集 train_X1 = np.ones((train_x.shape[0], 784)) ones = np.ones((train_x.shape[0], 1)) print('ones.shape:', ones.shape) for i in range(train_x.shape[0]): train_X1[i] = train_x[i].reshape([1, -1]) print('train_X1.shape:', train_X1.shape) train_X = tf.concat([train_X1, ones], axis=1) # 测试集 test_X1 = np.ones((test_x.shape[0], 784)) ones = np.ones((test_x.shape[0], 1)) for i in range(test_x.shape[0]): test_X1[i] = test_x[i].reshape([1, -1]) test_X = tf.concat([test_X1, ones], axis=1) # 将标记数据转化为列向量 train_y = train_y.reshape(-1,1) test_y = test_y.reshape(-1,1) # 存储准确值数据 acc_train = [] acc_test = [] # 设置超参数 iter = 1500 # 迭代次数 learn_rate = 5e-12 # 学习率 # 初始化训练参数 w = tf.Variable(np.random.randn(785, 10)*0.0001) print('初试w:',w,w.shape) for i in range(iter): with tf.GradientTape() as tape: y_p = 1/(1+tf.math.exp(-tf.matmul(train_X,w))) y_p_test = 1 / (1 + tf.math.exp(-tf.matmul(test_X, w))) loss = tf.reduce_sum(-(train_Y * tf.math.log(y_p)+(1 - train_Y)*tf.math.log(1-y_p))) # print('loss:',loss) dl_dw = tape.gradient(loss,w) w.assign_sub(learn_rate * dl_dw) if i % 20 == 0: print('i:{}, loss:{}, w:{}'.format(i,loss,w)) # print('y_p:',y_p) # 训练集准确率 y_p_round = tf.round(y_p) # 将预测数据进行四舍五入变成one-hot编码格式 p_y = tf.reshape(tf.argmax(y_p_round, 1), (-1, 1)) # 将one-hot转化为预测数字 is_right = tf.equal(p_y, train_y) # 比对是否预测正确 right_int = tf.cast(is_right, tf.int8) # 将bool型转化为0，1 acc = tf.reduce_mean(tf.cast(right_int, dtype=tf.float32)) # 求准确数组的平均值，也就是准确率 acc_train.append(acc) print('acc:', acc) # 测试集准确率 y_p_test_round = tf.round(y_p_test) p_y_test = tf.reshape(tf.argmax(y_p_test_round, 1), (-1, 1)) is_right_test = tf.equal(p_y_test, test_y) right_int_test = tf.cast(is_right_test, tf.int8) acc2 = tf.reduce_mean(tf.cast(right_int_test, dtype=tf.float32)) acc_test.append(acc2) print('acc2:', acc2) print() # 画出准确率的训练折线图 plt.plot(acc_train,label = '训练集正确率') plt.plot(acc_test,label = '测试集正确率') plt.legend() plt.show()
