TensorFlow2.0代码实战专栏（六）：使用低级方法来构建神经网络以便更好地理解

from __future__ import absolute_import, division, print_function

import tensorflow as tf
import numpy as np

# MNIST 数据集参数
num_classes = 10 # 所有类别（数字 0-9）
num_features = 784 # 数据特征数目 (图像形状: 28*28)

# 训练参数
learning_rate = 0.001
training_steps = 3000
batch_size = 256
display_step = 100

# 网络参数
n_hidden_1 = 128 # 第一层隐含层神经元的数目
n_hidden_2 = 256 # 第二层隐含层神经元的数目

# 准备MNIST数据
from tensorflow.keras.datasets import mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# 转化为float32
x_train, x_test = np.array(x_train, np.float32), np.array(x_test, np.float32)
# 将每张图像展平为具有784个特征的一维向量（28 * 28）
x_train, x_test = x_train.reshape([-1, num_features]), x_test.reshape([-1, num_features])
# 将图像值从[0,255]归一化到[0,1]
x_train, x_test = x_train / 255., x_test / 255.

# 使用tf.data API对数据进行随机排序和批处理
train_data = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_data = train_data.repeat().shuffle(5000).batch(batch_size).prefetch(1)

# 存储层的权重和偏置

# 随机值生成器初始化权重
random_normal = tf.initializers.RandomNormal()

weights = {
    'h1': tf.Variable(random_normal([num_features, n_hidden_1])),
    'h2': tf.Variable(random_normal([n_hidden_1, n_hidden_2])),
    'out': tf.Variable(random_normal([n_hidden_2, num_classes]))
}
biases = {
    'b1': tf.Variable(tf.zeros([n_hidden_1])),
    'b2': tf.Variable(tf.zeros([n_hidden_2])),
    'out': tf.Variable(tf.zeros([num_classes]))
}

# 创建模型
def neural_net(x):
    # Hidden fully connected layer with 128 neurons.
    # 具有128个神经元的隐含完全连接层
    layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
    # Apply sigmoid to layer_1 output for non-linearity.
    # 将sigmoid用于layer_1输出以获得非线性
    layer_1 = tf.nn.sigmoid(layer_1)

    # 具有128个神经元的隐含完全连接层
    layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
    # 将sigmoid用于layer_2输出以获得非线性
    layer_2 = tf.nn.sigmoid(layer_2)

    # 输出完全连接层，每一个神经元代表一个类别
    out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
    # 应用softmax将输出标准化为概率分布
    return tf.nn.softmax(out_layer)

# 交叉熵损失函数
def cross_entropy(y_pred, y_true):
    # 将标签编码为独热向量
    y_true = tf.one_hot(y_true, depth=num_classes)
    # 将预测值限制在一个范围之内以避免log（0）错误
    y_pred = tf.clip_by_value(y_pred, 1e-9, 1.)
    # 计算交叉熵
    return tf.reduce_mean(-tf.reduce_sum(y_true * tf.math.log(y_pred)))

#  准确率评估
def accuracy(y_pred, y_true):
     # 预测类是预测向量中最高分的索引（即argmax）
    correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.cast(y_true, tf.int64))
    return tf.reduce_mean(tf.cast(correct_prediction, tf.float32), axis=-1)

# 随机梯度下降优化器
optimizer = tf.optimizers.SGD(learning_rate)

# 优化过程
def run_optimization(x, y):
    # 将计算封装在GradientTape中以实现自动微分
    with tf.GradientTape() as g:
        pred = neural_net(x)
        loss = cross_entropy(pred, y)

    # 要更新的变量，即可训练的变量
    trainable_variables = weights.values() + biases.values()

    # 计算梯度
    gradients = g.gradient(loss, trainable_variables)

    # 按gradients更新 W 和 b
    optimizer.apply_gradients(zip(gradients, trainable_variables))

# 针对给定步骤数进行训练
for step, (batch_x, batch_y) in enumerate(train_data.take(training_steps), 1):
    # 运行优化以更新W和b值
    run_optimization(batch_x, batch_y)

    if step % display_step == 0:
        pred = neural_net(batch_x)
        loss = cross_entropy(pred, batch_y)
        acc = accuracy(pred, batch_y)
        print("step: %i, loss: %f, accuracy: %f" % (step, loss, acc))

step: 100, loss: 567.292969, accuracy: 0.136719
step: 200, loss: 398.614929, accuracy: 0.562500
step: 300, loss: 226.743774, accuracy: 0.753906
step: 400, loss: 193.384521, accuracy: 0.777344
step: 500, loss: 138.649963, accuracy: 0.886719
step: 600, loss: 109.713669, accuracy: 0.898438
step: 700, loss: 90.397217, accuracy: 0.906250
step: 800, loss: 104.545380, accuracy: 0.894531
step: 900, loss: 94.204697, accuracy: 0.890625
step: 1000, loss: 81.660645, accuracy: 0.906250
step: 1100, loss: 81.237137, accuracy: 0.902344
step: 1200, loss: 65.776703, accuracy: 0.925781
step: 1300, loss: 94.195862, accuracy: 0.910156
step: 1400, loss: 79.425507, accuracy: 0.917969
step: 1500, loss: 93.508163, accuracy: 0.914062
step: 1600, loss: 88.912506, accuracy: 0.917969
step: 1700, loss: 79.033607, accuracy: 0.929688
step: 1800, loss: 65.788315, accuracy: 0.898438
step: 1900, loss: 73.462387, accuracy: 0.937500
step: 2000, loss: 59.309540, accuracy: 0.917969
step: 2100, loss: 67.014008, accuracy: 0.917969
step: 2200, loss: 48.297115, accuracy: 0.949219
step: 2300, loss: 64.523148, accuracy: 0.910156
step: 2400, loss: 72.989517, accuracy: 0.925781
step: 2500, loss: 57.588585, accuracy: 0.929688
step: 2600, loss: 44.957100, accuracy: 0.960938
step: 2700, loss: 59.788242, accuracy: 0.937500
step: 2800, loss: 63.581337, accuracy: 0.937500
step: 2900, loss: 53.471252, accuracy: 0.941406
step: 3000, loss: 43.869728, accuracy: 0.949219

# 在验证集上测试模型
pred = neural_net(x_test)
print("Test Accuracy: %f" % accuracy(pred, y_test))

# 可视化预测
import matplotlib.pyplot as plt

# 从验证集中预测5张图像
n_images = 5
test_images = x_test[:n_images]
predictions = neural_net(test_images)

# 显示图片和模型预测结果
for i in range(n_images):
    plt.imshow(np.reshape(test_images[i], [28, 28]), cmap='gray')
    plt.show()
    print("Model prediction: %i" % np.argmax(predictions.numpy()[i]))