tensorflow 2.0 神经网络与全连接层 之 张量实战

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测试/验证（Test/Evaluation）

• train/evaluation/test splitting
• Stop at the best epoch
• Use the best epoch model to p

精确度（Accuracy）

• Pred: [Y, Y, Y, N, Y, N, N, Y, N, Y]
• Label:[Y, N, Y, Y, N, Y, N, Y, N, Y]
• Equal:[1, 0, 1, 0, 0, 0, 1, 1, 1, 1]
• Acc: 6/10

    # test/evaluation
    # [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]
        # int64!!!
        pred = tf.argmax(prob, axis=1)
        pred = tf.cast(pred, dtype=tf.int32)
        # y: [b]
        # [b], int32
        # print(pred.dtype, y.dtype)
        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('test acc:', acc)

完整代码

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

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()
# # 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)

print(x.shape, y.shape, x.dtype, y.dtype)
print(tf.reduce_min(x), tf.reduce_max(x))
print(tf.reduce_min(y), tf.reduce_max(y))


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)
print('batch:', sample[0].shape, sample[1].shape)


# [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]))

lr = 1e-3

for epoch in range(100):  # iterate db for 10
    for step, (x, y) in enumerate(train_db):  # for every batch
        # x:[128, 28, 28]
        # y: [128]

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

        with tf.GradientTape() as tape:  # tf.Variable
            # 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 = 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])
        # print(grads)
        # w1 = w1 - lr * w1_grad
        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(epoch, step, 'loss:', float(loss))

    # test/evaluation
    # [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]
        # int64!!!
        pred = tf.argmax(prob, axis=1)
        pred = tf.cast(pred, dtype=tf.int32)
        # y: [b]
        # [b], int32
        # print(pred.dtype, y.dtype)
        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('test acc:', acc)

小结

回归：

1. 方程组
2. 引入噪声
3. 引入梯度
4. Numpy实战
5. 引入离散预测

分类：

1. 读取数据
2. 构建模型
3. 前向传播实战
4. 误差计算
5. 梯度计算及更新
6. 测试实战