吴裕雄--天生自然TensorFlow2教程：测试（张量）- 实战

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets
import os

# do not print irrelevant information
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# x: [60k,28,28], [10,28,28]
# y: [60k], [10k]
(x, y), (x_test, y_test) = datasets.mnist.load_data()
# transform Tensor
# x: [0~255] ==》 [0~1.]
x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.
y = tf.convert_to_tensor(y, dtype=tf.int32)

x_test = tf.convert_to_tensor(x_test, dtype=tf.float32) / 255.
y_test = tf.convert_to_tensor(y_test, dtype=tf.int32)

# batch of 128
train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128)
test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(128)
train_iter = iter(train_db)
sample = next(train_iter)

# [b,784] ==> [b,256] ==> [b,128] ==> [b,10]
# [dim_in,dim_out],[dim_out]
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]))

# learning rate
lr = 1e-3

for epoch in range(10):  # iterate db for 10
    # tranin every train_db
    for step, (x, y) in enumerate(train_db):
        # x: [128,28,28]
        # y: [128]

        # [b,28,28] ==> [b,28*28]
        x = tf.reshape(x, [-1, 28 * 28])

        with tf.GradientTape(
        ) as tape:  # only data types of tf.variable are logged
            # x: [b,28*28]
            # h1 = x@w1 + b1
            # [b,784]@[784,256]+[256] ==> [b,256] + [256] ==> [b,256] + [b,256]
            h1 = x @ w1 + tf.broadcast_to(b1, [x.shape[0], 256])
            h1 = tf.nn.relu(h1)
            # [b,256] ==> [b,128]
            # h2 = x@w2 + b2  # b2 can broadcast automatic
            h2 = h1 @ w2 + b2
            h2 = tf.nn.relu(h2)
            # [b,128] ==> [b,10]
            out = h2 @ w3 + b3

            # compute loss
            # out: [b,10]
            # y:[b] ==> [b,10]
            y_onehot = tf.one_hot(y, depth=10)

            # mse = mean(sum(y-out)^2)
            # [b,10]
            loss = tf.square(y_onehot - out)
            # mean:scalar
            loss = tf.reduce_mean(loss)

        # compute gradients
        grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
        # w1 = w1 - lr * w1_grad
        # w1 = w1 - lr * grads[0]  # not in situ update
        # in situ update
        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])

        if step % 100 == 0:
            print(f'epoch:{epoch}, step: {step}, loss:{float(loss)}')

    # [w1,b1,w2,b2,w3,b3]
    total_correct, total_num = 0, 0
    for step, (x, y) in enumerate(test_db):
        # [b,28,28] ==> [b,28*28]
        x = tf.reshape(x, [-1, 28 * 28])

        # [b,784] ==> [b,256] ==> [b,128] ==> [b,10]
        h1 = tf.nn.relu(x @ w1 + b1)
        h2 = tf.nn.relu(h1 @ w2 + b2)
        out = h2 @ w3 + b3

        # out: [b,10] ~ R
        # prob: [b,10] ~ (0,1)
        prob = tf.nn.softmax(out, axis=1)
        # [b,10] ==> [b]
        pred = tf.argmax(prob, axis=1)
        pred = tf.cast(pred, dtype=tf.int32)
        # y: [b]
        # [b], int32
        correct = tf.cast(tf.equal(pred, y), dtype=tf.int32)
        correct = tf.reduce_sum(correct)

        total_correct += int(correct)
        total_num += x.shape[0]
    acc = total_correct / total_num
    print(f'test acc: {acc}')