《TensorFlow2深度学习》学习笔记（四）对笔记二中的模型增加正确率展示

全部代码如下：（红色部分为与笔记二不同之处）

#1.Import the neccessary libraries needed
import numpy as np
import tensorflow as tf
import matplotlib
from matplotlib import pyplot as plt

########################################################################

#2.Set default parameters for plots
matplotlib.rcParams['font.size'] = 20
matplotlib.rcParams['figure.titlesize'] = 20
matplotlib.rcParams['figure.figsize'] = [9, 7]
matplotlib.rcParams['font.family'] = ['STKaiTi']
matplotlib.rcParams['axes.unicode_minus']=False

########################################################################

#3.Initialize Parameters

#Initialize learning rate
lr = 1e-2                                              #----------------------changed
#Initialize batch size
batchsz = 512
#Initialize loss and accurate array
losses = []
accs = []                                              #----------------------changed
#Initialize the weights layers and the bias layers
w1=tf.Variable(tf.random.truncated_normal([784,256],stddev=0.1))
b1=tf.Variable(tf.zeros([256]))
w2=tf.Variable(tf.random.truncated_normal([256,128],stddev=0.1))
b2=tf.Variable(tf.zeros([128]))
w3=tf.Variable(tf.random.truncated_normal([128,10],stddev=0.1))
b3=tf.Variable(tf.zeros([10]))

########################################################################
#4.Define preprocess function                          #----------------------changed
def preprocess(x,y):
    x=tf.cast(x,dtype=tf.float32)/255.
    x=tf.reshape(x,[-1,28*28])
    y=tf.cast(y,dtype=tf.int32)
    #one_hot接受的输入为int32,输出为float32
    y=tf.one_hot(y,depth=10)
    return x,y

########################################################################

#5.Import the minist dataset offline
(x_train,y_train),(x_test,y_test)=tf.keras.datasets.mnist.load_data(path=r'F:\learning\machineLearning\TensorFlow2_deeplearning\forward_progression\mnist.npz')
train_db=tf.data.Dataset.from_tensor_slices((x_train,y_train))
train_db=train_db.shuffle(10000)                       #-----------------------changed
train_db=train_db.batch(batchsz)
train_db=train_db.map(preprocess)
#Control the epoch times
train_db=train_db.repeat(20)

test_db=tf.data.Dataset.from_tensor_slices((x_test,y_test))
test_db=test_db.shuffle(1000).batch(batchsz).map(preprocess)

########################################################################

#The main function
def main():
    for step,(x,y) in enumerate(train_db):#Or for x,y in train_db:
        with tf.GradientTape() as tape: # tf.Variable
            # layer1
            h1 = x@w1 + b1
            h1 = tf.nn.relu(h1)
            # layer2
            h2 = h1@w2 + b2
            h2 = tf.nn.relu(h2)
            # output
            out = h2@w3 + b3
            # compute loss
            loss = tf.square(y-out)
            # mean: scalar
            loss = tf.reduce_mean(loss)
        # compute gradients
        grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
        #Update the weights and the bias               #-----------------------changed
        for p, g in zip([w1, b1, w2, b2, w3, b3], grads):
            p.assign_sub(lr * g)

        if step % 80 == 0:
            print(step, 'loss:', float(loss))
            losses.append(float(loss))

        if step % 80 == 0:                             #-----------------------changed
            total, total_correct = 0., 0
            for x,y in test_db:
                # layer1
                h1 = x@w1 + b1
                h1 = tf.nn.relu(h1)
                # layer2
                h2 = h1@w2 + b2
                h2 = tf.nn.relu(h2)
                # output
                out = h2@w3 + b3
                pred=tf.argmax(out,axis=1)
                y=tf.argmax(y,axis=1)
                correct=tf.equal(pred,y)
                total_correct+=tf.reduce_sum(tf.cast(correct,dtype=tf.int32)).numpy()
                total+=x.shape[0]
            print(step,'Evaluate ACC:',total_correct/total)
            accs.append(total_correct/total)
    plt.figure()
    x = [i*80 for i in range(len(losses))]
    plt.plot(x, losses, color='C0', marker='s', label='训练')
    plt.ylabel('MSE')
    plt.xlabel('Step')
    plt.legend()

    plt.figure()
    plt.plot(x, accs, color='C1', marker='s', label='测试')
    plt.ylabel('准确率')
    plt.xlabel('Step')
    plt.legend()

    plt.show()
if __name__ == '__main__':
    main()

其中learning rate在此处改为了1e-2，经测试若为1e-3则accurate rate会增长较慢，在20epoch下最终会达到30~40%,而1e-2则会接近80%

并且通过.map(preprocess)方法预处理了train_db，包括将图片数据标准化到（0-1），reshape到[-1,28*28]，将标签数据做one-hot处理，深度为10；通过train_db=train_db.repeat(20)代替了for epoch in range(20)；用

for p, g in zip([w1, b1, w2, b2, w3, b3], grads):
　　p.assign_sub(lr * g)

代替了

w1.assign_sub(lr * grads[0])
b1.assign_sub(lr * grads[1])
w2.assign_sub(lr * grads[2])
b2.assign_sub(lr * grads[3])
w3.assign_sub(lr * grads[4])
b3.assign_sub(lr * grads[5])