tensorflow 2.0 深度学习（第二部分 part1）

逃离我推掉我的手 2023-07-02 04:25 15阅读 0赞

# ![20191009191333910.png][] #

# [日萌社][Link 1] #

[人工智能AI：Keras PyTorch MXNet TensorFlow PaddlePaddle 深度学习实战（不定时更新）][AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]

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[tensorflow 2.0 深度学习（第一部分 part1）][tensorflow 2.0 _ part1]

[tensorflow 2.0 深度学习（第一部分 part2）][tensorflow 2.0 _ part2]

[tensorflow 2.0 深度学习（第一部分 part3）][tensorflow 2.0 _ part3]

[tensorflow 2.0 深度学习（第二部分 part1）][tensorflow 2.0 _ part1 1]

[tensorflow 2.0 深度学习（第二部分 part2）][tensorflow 2.0 _ part2 1]

[tensorflow 2.0 深度学习（第二部分 part3）][tensorflow 2.0 _ part3 1]

[tensorflow 2.0 深度学习 (第三部分 卷积神经网络 part1)][tensorflow 2.0 _ _ _ part1]

[tensorflow 2.0 深度学习 (第三部分 卷积神经网络 part2)][tensorflow 2.0 _ _ _ part2]

[tensorflow 2.0 深度学习（第四部分 循环神经网络）][tensorflow 2.0 _]

[tensorflow 2.0 深度学习（第五部分 GAN生成神经网络 part1）][tensorflow 2.0 _ GAN_ part1]

[tensorflow 2.0 深度学习（第五部分 GAN生成神经网络 part2）][tensorflow 2.0 _ GAN_ part2]

[tensorflow 2.0 深度学习（第六部分 强化学习）][tensorflow 2.0 _ 1]

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# **神经网络** #

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# **感知机**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70][] #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 1][]

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# **全连接层** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 2][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 3][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 4][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 5][]

import tensorflow as tf 
    from 	tensorflow import keras
     
    x = tf.random.normal([2, 3])
    model = keras.Sequential([
    		keras.layers.Dense(2, activation='relu'),
    		keras.layers.Dense(2, activation='relu'),
    		keras.layers.Dense(2)
    	])
    
    #通过 build 函数完成内部张量的创建，其中None为任意的 batch 数量，3为输入特征长度
    model.build(input_shape=[None, 3])
    model.summary()
    
    for p in model.trainable_variables:
    	print(p.name, p.shape)

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 6][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 7][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 8][]

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# **前向传播、反向传播、误差函数** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 9][]

import  tensorflow as tf
    from    tensorflow import keras
    from    tensorflow.keras import layers
    from    tensorflow.keras import datasets
    import  os
     
    x = tf.random.normal([2,28*28]) #[2,784]
    w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
    b1 = tf.Variable(tf.zeros([256]))
    o1 = tf.matmul(x,w1) + b1 #[2,784]@[784, 256]+[256]
    o1
     
    x = tf.random.normal([4,28*28])#[4,784]
    fc1 = layers.Dense(256, activation=tf.nn.relu) #[4,784]@[784,256]+[256]= [b, 784] => [b, 256]
    fc2 = layers.Dense(128, activation=tf.nn.relu) #[4,256]@[256,128]+[128]= [b, 256] => [b, 128]
    fc3 = layers.Dense(64, activation=tf.nn.relu)  #[4,128]@[128,64]+[64]= [b, 128] => [b, 64]
    fc4 = layers.Dense(10, activation=None)   #[4,64]@[64,10]+[10]= [b, 64] => [b, 10]
    h1 = fc1(x)
    h2 = fc2(h1)
    h3 = fc3(h2)
    h4 = fc4(h3)
    
    model = layers.Sequential([
        layers.Dense(256, activation=tf.nn.relu) ,
        layers.Dense(128, activation=tf.nn.relu) ,
        layers.Dense(64, activation=tf.nn.relu) ,
        layers.Dense(10, activation=None) ,
    ])
    out = model(x)# 256*784+256 + 128*256+128 + 64*128+64 + 10*64+10
     
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    # x: [60k, 28, 28],
    # y: [60k]
    (x, y), _ = datasets.mnist.load_data()
    # x: [0~255] => [0~1.]
    x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.  #标准化/归一化
    y = tf.convert_to_tensor(y, 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) #batch构建批量大小
    train_iter = iter(train_db)#iter返回生成器对象
    sample = next(train_iter)#next调用生成器返回第一个批量大小的数据
    print('batch:', sample[0].shape, sample[1].shape)
     
    # [b, 784] => [b, 256] => [b, 128] => [b, 10]
    # [dim_in, dim_out], [dim_out]  第N层权重[输入神经元节点数, 输出神经元节点数]、偏置[输出神经元节点数]
    # 隐藏层1张量
    w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
    b1 = tf.Variable(tf.zeros([256]))
    # 隐藏层2张量
    w2 = tf.Variable(tf.random.truncated_normal([256, 128], stddev=0.1))
    b2 = tf.Variable(tf.zeros([128]))
    # 隐藏层3张量
    w3 = tf.Variable(tf.random.truncated_normal([128, 64], stddev=0.1))
    b3 = tf.Variable(tf.zeros([64]))
    # 输出层张量
    w4 = tf.Variable(tf.random.truncated_normal([64, 10], stddev=0.1))
    b4 = tf.Variable(tf.zeros([10]))
    
    lr = 1e-3 #学习率
    
    #构建epoch训练次数
    for epoch in range(10): # iterate db for 10
        #遍历生成器对象，每次获取每个批量大小的数据
        for step, (x, y) in enumerate(train_db): # for every batch
            # x:[128, 28, 28]
            # y: [128]
            # [b, 28, 28] => [b, 28*28] 展平为 (批量大小,行*列)
            x = tf.reshape(x, [-1, 28*28])
    
            #构建梯度记录环境
            with tf.GradientTape() as tape: # tf.Variable
                # x: [b, 28*28]
                #  隐藏层1前向计算，[b, 28*28] => [b, 256]
                h1 = x@w1 + tf.broadcast_to(b1, [x.shape[0], 256])
                h1 = tf.nn.relu(h1)
                # 隐藏层2前向计算，[b, 256] => [b, 128]
                h2 = h1@w2 + b2
                h2 = tf.nn.relu(h2)
                # 隐藏层3前向计算，[b, 128] => [b, 64] 
                h3 = h2@w3 + b3
                h3 = tf.nn.relu(h3)
                # 输出层前向计算，[b, 64] => [b, 10] 
                h4 = h3@w4 + b4
                out = h4
    
                # compute loss
                # out: [b, 10]
                # y: [b] => [b, 10] 真实标签one-hot化
                y_onehot = tf.one_hot(y, depth=10)
    
                # mse = mean(sum(y-out)^2)
                # [b, 10] 均方差mse = mean(sum(y-out)^2) 预测值与真实值之差的平方的平均值
                loss = tf.square(y_onehot - out)
                # mean: scalar
                loss = tf.reduce_mean(loss)
    
            #1.求导，tape.gradient(y,[参数θ])求参数θ相对于y的梯度信息
            #  dy_dw = tape.gradient(y, [w])
            #2.通过tape.gradient(loss,[参数θ])函数求得网络参数θ的梯度信息
            #  grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3]) 
            #  根据loss对模型所有参数求导 tape.gradient(loss, model.trainable_variables)
            # compute gradients
            grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3, w4, b4])
    
            # print(grads)
            # w1 = w1 - lr * w1_grad 优化器规则，根据 模型参数θ = θ - lr * grad 更新网络参数
            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])
            w4.assign_sub(lr * grads[6])
            b4.assign_sub(lr * grads[7])
    
            if step % 100 == 0:
                print(epoch, step, 'loss:', float(loss))

#  #

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# **激活函数(非线性函数)** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 10][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 11][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 12][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 13][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 14][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 15][]

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# **输出层设计**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 16][] #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 17][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 18][]

import tensorflow as tf
    help(tf.losses.categorical_crossentropy) 
    	查看categorical_crossentropy函数的默认参数列表和使用方法介绍
     	其中形参默认为from_logits=False，网络预测值y_pred 表示必须为经过了 Softmax函数的输出值。
    	当 from_logits 设置为 True 时，网络预测值y_pred 表示必须为还没经过 Softmax 函数的变量 z。
    	from_logits=True 标志位将softmax激活函数实现在损失函数中，便不需要手动添加softmax损失函数，提升数值计算稳定性。
    
    Help on function categorical_crossentropy in module tensorflow.python.keras.losses:
    categorical_crossentropy(y_true, y_pred, from_logits=False, label_smoothing=0) 
        Computes the categorical crossentropy loss.
        Args:
          y_true: tensor of true targets.
          y_pred: tensor of predicted targets.
          from_logits: Whether `y_pred` is expected to be a logits tensor. By default,
            we assume that `y_pred` encodes a probability distribution.

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 19][]

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# **loss误差函数**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 20][] #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 21][]

import tensorflow as tf 
    
    x=tf.random.normal([1,3])
    w=tf.ones([3,2])
    b=tf.ones([2])
    y = tf.constant([0, 1])
    
    with tf.GradientTape() as tape:
    	tape.watch([w, b])
    	logits = tf.sigmoid(x@w+b) #非线性激活函数sigmoid(线性函数x@w+b)
    	loss = tf.reduce_mean(tf.losses.MSE(y, logits)) #MSE均方差损失函数(y真实值-预测值)
    #通过loss对模型参数θ的梯度
    grads = tape.gradient(loss, [w, b])
    print('w grad:', grads[0])
    print('b grad:', grads[1])

import tensorflow as tf 
    
    tf.random.set_seed(4323) #随机种子
    x=tf.random.normal([1,3])
    w=tf.random.normal([3,2])
    b=tf.random.normal([2])
    y = tf.constant([0, 1])
    
    with tf.GradientTape() as tape:
    	tape.watch([w, b])
    	logits = (x@w+b) #线性函数x@w+b
    	#from_logits默认为from_logits=False，网络预测值y_pred 表示必须为经过了 Softmax函数的输出值。
    	#当 from_logits 设置为 True 时，网络预测值y_pred 表示必须为还没经过 Softmax 函数的变量z。
    	loss = tf.reduce_mean(tf.losses.categorical_crossentropy(y, logits, from_logits=True))
    #通过loss对模型参数θ的梯度
    grads = tape.gradient(loss, [w, b])
    print('w grad:', grads[0])
    print('b grad:', grads[1])

import  tensorflow as tf
    from    tensorflow import keras
    from    tensorflow.keras import datasets, layers
    import  os
     
    a = tf.random.normal([4,35,8]) # 模拟成绩册A
    b = tf.random.normal([6,35,8]) # 模拟成绩册B
    tf.concat([a,b],axis=0) # 合并成绩册 TensorShape([10, 35, 8])
     
    x = tf.random.normal([2,784])
    w1 = tf.Variable(tf.random.truncated_normal([784, 256], stddev=0.1))
    b1 = tf.Variable(tf.zeros([256]))
    o1 = tf.matmul(x,w1) + b1  #[2,784]@[784, 256]+[256]=[2, 256]
    o1 = tf.nn.relu(o1)
    o1 #TensorShape([2, 256])
     
    x = tf.random.normal([4,28*28])
    # 创建全连接层，指定输出节点数和激活函数
    fc = layers.Dense(512, activation=tf.nn.relu) 
    h1 = fc(x)  # 通过fc类完成一次全连接层的计算
    vars(fc)
     
    x = tf.random.normal([4,4])
    # 创建全连接层，指定输出节点数和激活函数
    fc = layers.Dense(3, activation=tf.nn.relu) 
    h1 = fc(x)  # 通过fc类完成一次全连接层的计算
     
    fc.non_trainable_variables
    embedding = layers.Embedding(10000, 100) #词汇表单词数量为10000，每个单词的维度为100
    x = tf.ones([25000,80])
    embedding(x)
     
    z = tf.random.normal([2,10]) # 构造输出层的输出
    y_onehot = tf.constant([1,3]) # 构造真实值
    y_onehot = tf.one_hot(y_onehot, depth=10) # 真实值one-hot编码 TensorShape([2, 10])
    # from_logits=True表示 输出层未使用Softmax函数，故from_logits设置为True
    loss = keras.losses.categorical_crossentropy(y_onehot,z,from_logits=True)
    loss = tf.reduce_mean(loss) # 计算平均交叉熵损失
    loss
    
    # from_logits=True表示 输出层未使用Softmax函数，故from_logits设置为True
    criteon = keras.losses.CategoricalCrossentropy(from_logits=True) # 计算平均交叉熵损失
    loss = criteon(y_onehot,z) # 计算损失
    loss

import tensorflow as tf 
    y = tf.constant([1, 2, 3, 0, 2])
    y = tf.one_hot(y, depth=4) #TensorShape([5, 4])
    y = tf.cast(y, dtype=tf.float32)
    
    out = tf.random.normal([5, 4])
    
    # mse = mean(sum(y-out)^2) 均方差mse = mean(sum(y-out)^2) 预测值与真实值之差的平方的平均值
    loss1 = tf.reduce_mean(tf.square(y-out))
    
    #tf.norm(a)默认为执行L2范数tf.norm(a. ord=2)，等同于tf.sqrt(tf.reduce_sum(tf.square(a)))
    loss2 = tf.square(tf.norm(y-out)) / (5*4)
    
    # MSE 函数返回的是每个样本的均方差，需要在样本数量上再次平均来获得 batch 的均方差
    # loss = keras.losses.MSE(y_onehot, o) # 计算均方差
    # loss = tf.reduce_mean(loss) # 计算 batch 均方差
    loss3 = tf.reduce_mean(tf.losses.MSE(y, out))  
    
    print(loss1) #tf.Tensor(0.96629584, shape=(), dtype=float32)
    print(loss2) #tf.Tensor(0.9662959, shape=(), dtype=float32)
    print(loss3) #tf.Tensor(0.96629584, shape=(), dtype=float32)

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 22][]

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![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 27][]

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# **神经网络类型** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 28][]

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# **油耗预测实战** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 29][]

#统计数据
    sns.pairplot(train_dataset[["Cylinders", "Displacement", "Weight", "MPG"]], diag_kind="kde")
    #查看训练集的输入X的统计数据
    train_stats = train_dataset.describe()
    #              MPG   Cylinders  Displacement  Horsepower  ...  Model Year         USA      Europe       Japan
    #count  314.000000  314.000000    314.000000  314.000000  ...  314.000000  314.000000  314.000000  314.000000
    #mean    23.310510    5.477707    195.318471  104.869427  ...   75.898089    0.624204    0.178344    0.197452
    #std      7.728652    1.699788    104.331589   38.096214  ...    3.675642    0.485101    0.383413    0.398712
    #min     10.000000    3.000000     68.000000   46.000000  ...   70.000000    0.000000    0.000000    0.000000
    #25%     17.000000    4.000000    105.500000   76.250000  ...   73.000000    0.000000    0.000000    0.000000
    #50%     22.000000    4.000000    151.000000   94.500000  ...   76.000000    1.000000    0.000000    0.000000
    #75%     28.950000    8.000000    265.750000  128.000000  ...   79.000000    1.000000    0.000000    0.000000
    #max     46.600000    8.000000    455.000000  225.000000  ...   82.000000    1.000000    1.000000    1.000000

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from __future__ import absolute_import, division, print_function, unicode_literals
    import pathlib
    import os
    import matplotlib.pyplot as plt
    import pandas as pd
    import seaborn as sns
    import tensorflow as tf
    from tensorflow import keras
    from tensorflow.keras import layers, losses
    
    print(tf.__version__) #2.0.0
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    # 在线下载汽车效能数据集
    dataset_path = keras.utils.get_file("auto-mpg.data", "http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data")
    # 效能（公里数每加仑），气缸数，排量，马力，重量
    # 加速度，型号年份，产地
    column_names = ['MPG','Cylinders','Displacement','Horsepower','Weight',
                    'Acceleration', 'Model Year', 'Origin']
    raw_dataset = pd.read_csv(dataset_path, names=column_names,
                          na_values = "?", comment='\t',
                          sep=" ", skipinitialspace=True)
    
    #    MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  Model Year  Origin
    #0  18.0          8         307.0       130.0  3504.0          12.0          70       1
    #1  15.0          8         350.0       165.0  3693.0          11.5          70       1
    #2  18.0          8         318.0       150.0  3436.0          11.0          70       1
    #3  16.0          8         304.0       150.0  3433.0          12.0          70       1
    #4  17.0          8         302.0       140.0  3449.0          10.5          70       1
    dataset = raw_dataset.copy()
    # 查看部分数据
    dataset.tail()
    dataset.head()
    dataset
    
    # 统计空白数据,并清除
    dataset.isna().sum()
    dataset = dataset.dropna()
    dataset.isna().sum()
    dataset #[392 rows x 8 columns]
    
    # 处理类别型数据，其中origin列代表了类别1,2,3,分布代表产地：美国、欧洲、日本
    # 其弹出这一列
    origin = dataset.pop('Origin')
    # 根据origin列来写入新列
    dataset['USA'] = (origin == 1)*1.0
    dataset['Europe'] = (origin == 2)*1.0
    dataset['Japan'] = (origin == 3)*1.0
    dataset.tail()
    #    MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  Model Year  USA  Europe  Japan
    #0  18.0          8         307.0       130.0  3504.0          12.0          70  1.0     0.0    0.0
    #1  15.0          8         350.0       165.0  3693.0          11.5          70  1.0     0.0    0.0
    #2  18.0          8         318.0       150.0  3436.0          11.0          70  1.0     0.0    0.0
    #3  16.0          8         304.0       150.0  3433.0          12.0          70  1.0     0.0    0.0
    #4  17.0          8         302.0       140.0  3449.0          10.5          70  1.0     0.0    0.0
     
    # 切分为训练集和测试集
    train_dataset = dataset.sample(frac=0.8,random_state=0) #[314 rows x 10 columns]
    test_dataset = dataset.drop(train_dataset.index)  #[78 rows x 10 columns]
    
    #统计数据
    sns.pairplot(train_dataset[["Cylinders", "Displacement", "Weight", "MPG"]], diag_kind="kde")
    #查看训练集的输入X的统计数据
    train_stats = train_dataset.describe()
    #              MPG   Cylinders  Displacement  Horsepower  ...  Model Year         USA      Europe       Japan
    #count  314.000000  314.000000    314.000000  314.000000  ...  314.000000  314.000000  314.000000  314.000000
    #mean    23.310510    5.477707    195.318471  104.869427  ...   75.898089    0.624204    0.178344    0.197452
    #std      7.728652    1.699788    104.331589   38.096214  ...    3.675642    0.485101    0.383413    0.398712
    #min     10.000000    3.000000     68.000000   46.000000  ...   70.000000    0.000000    0.000000    0.000000
    #25%     17.000000    4.000000    105.500000   76.250000  ...   73.000000    0.000000    0.000000    0.000000
    #50%     22.000000    4.000000    151.000000   94.500000  ...   76.000000    1.000000    0.000000    0.000000
    #75%     28.950000    8.000000    265.750000  128.000000  ...   79.000000    1.000000    0.000000    0.000000
    #max     46.600000    8.000000    455.000000  225.000000  ...   82.000000    1.000000    1.000000    1.000000
    
    train_stats.pop("MPG")
    #train_stats['列值字段名']：该方式为通过使用['列值字段名']访问的是列值，不能直接使用['行值字段名']访问行值。
    #如要使用行值字段名访问行值的话，必须先transpose()反转张量的行和列，那么本来的行值字段名和行值都变成了列值字段名和列值，
    #本来的列值字段名和列值都变成了行值字段名和行值，那么现在便可以使用['原来的行值字段名']访问原来的行值。
    #(8, 9) 表示8行统计指标值，9列特征值 => (9, 8) 表示9行特征值，8列统计指标值
    train_stats = train_stats.transpose() 
    train_stats #train_stats['列值字段名']：可以使用train_stats['count']，不可以使用train_stats['Cylinders']
    #              count         mean         std     min      25%     50%      75%     max
    #Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0     8.00     8.0
    #Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   265.75   455.0
    #Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   128.00   225.0
    #Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5  3608.00  5140.0
    #Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5    17.20    24.8
    #Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0    79.00    82.0
    #USA           314.0     0.624204    0.485101     0.0     0.00     1.0     1.00     1.0
    #Europe        314.0     0.178344    0.383413     0.0     0.00     0.0     0.00     1.0
    #Japan         314.0     0.197452    0.398712     0.0     0.00     0.0     0.00     1.0
    
    
    # 移动MPG油耗效能这一列为真实标签Y
    train_labels = train_dataset.pop('MPG') #(314,)
    test_labels = test_dataset.pop('MPG') #(78,)
     
    # 标准化数据
    def norm(x):
      return (x - train_stats['mean']) / train_stats['std']
    #>>> train_stats['mean'].shape
    #(9,)
    #>>> train_stats['mean'] 
    #Cylinders          5.477707
    #Displacement     195.318471
    #Horsepower       104.869427
    #Weight          2990.251592
    #Acceleration      15.559236
    #Model Year        75.898089
    #USA                0.624204
    #Europe             0.178344
    #Japan              0.197452
    #Name: mean, dtype: float64
    
    #>>> train_stats['std'].shape
    #(9,)
    #>>> train_stats['std']
    #Cylinders         1.699788
    #Displacement    104.331589
    #Horsepower       38.096214
    #Weight          843.898596
    #Acceleration      2.789230
    #Model Year        3.675642
    #USA               0.485101
    #Europe            0.383413
    #Japan             0.398712
    #Name: std, dtype: float64
    
    normed_train_data = norm(train_dataset)
    normed_test_data = norm(test_dataset)
    print(normed_train_data.shape,train_labels.shape) #(314, 9) (314,)
    print(normed_test_data.shape, test_labels.shape) #(78, 9) (78,)
    
    class Network(keras.Model):
        # 回归网络
        def __init__(self):
            super(Network, self).__init__()
            # 创建3个全连接层
            self.fc1 = layers.Dense(64, activation='relu')
            self.fc2 = layers.Dense(64, activation='relu')
            self.fc3 = layers.Dense(1)
    
        def call(self, inputs, training=None, mask=None):
            # 依次通过3个全连接层
            x = self.fc1(inputs)
            x = self.fc2(x)
            x = self.fc3(x)
            return x
    
    model = Network()
    model.build(input_shape=(None, 9))
    model.summary()
    optimizer = tf.keras.optimizers.RMSprop(0.001)
    train_db = tf.data.Dataset.from_tensor_slices((normed_train_data.values, train_labels.values))
    train_db = train_db.shuffle(100).batch(32)
    
    # 未训练时测试
    # example_batch = normed_train_data[:10]
    # example_result = model.predict(example_batch)
    # example_result
     
    train_mae_losses = [] #MAE
    test_mae_losses = []
    
    #构建epoch训练次数
    for epoch in range(200):
        #遍历生成器对象，每次获取每个批量大小的数据
        for step, (x,y) in enumerate(train_db):
            #构建梯度记录环境
            with tf.GradientTape() as tape:
                out = model(x)
                loss = tf.reduce_mean(losses.MSE(y, out)) #MSE
                mae_loss = tf.reduce_mean(losses.MAE(y, out)) #MAE
    
            if step % 10 == 0:
                print(epoch, step, float(loss))
    
            #1.求导，tape.gradient(y,[参数θ])求参数θ相对于y的梯度信息
            #  dy_dw = tape.gradient(y, [w])
            #2.通过tape.gradient(loss,[参数θ])函数求得网络参数θ的梯度信息
            #  grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3]) 
            #  根据loss对模型所有参数求导 tape.gradient(loss, model.trainable_variables)
            grads = tape.gradient(loss, model.trainable_variables)
    
            #优化器规则，根据 模型参数θ = θ - lr * grad 更新网络参数
            optimizer.apply_gradients(zip(grads, model.trainable_variables))
    
        train_mae_losses.append(float(mae_loss))
        out = model(tf.constant(normed_test_data.values))
        test_mae_losses.append(tf.reduce_mean(losses.MAE(test_labels, out)))
    
    plt.figure()
    plt.xlabel('Epoch')
    plt.ylabel('MAE')
    plt.plot(train_mae_losses,  label='Train')
    plt.plot(test_mae_losses, label='Test')
    plt.legend()
    # plt.ylim([0,10])
    plt.legend()
    plt.savefig('auto.svg')
    plt.show()

[20191009191333910.png]: https://img-blog.csdnimg.cn/20191009191333910.png
[Link 1]: http://www.rimengshe.com/
[AI_Keras PyTorch MXNet TensorFlow PaddlePaddle]: https://blog.csdn.net/zimiao552147572/article/details/88867161
[tensorflow 2.0 _ part1]: https://azusa.blog.csdn.net/article/details/104086445
[tensorflow 2.0 _ part2]: https://azusa.blog.csdn.net/article/details/104086557
[tensorflow 2.0 _ part3]: https://azusa.blog.csdn.net/article/details/104086663
[tensorflow 2.0 _ part1 1]: https://azusa.blog.csdn.net/article/details/104086741
[tensorflow 2.0 _ part2 1]: https://azusa.blog.csdn.net/article/details/104086773
[tensorflow 2.0 _ part3 1]: https://azusa.blog.csdn.net/article/details/104087912
[tensorflow 2.0 _ _ _ part1]: https://azusa.blog.csdn.net/article/details/104088133
[tensorflow 2.0 _ _ _ part2]: https://azusa.blog.csdn.net/article/details/104088230
[tensorflow 2.0 _]: https://azusa.blog.csdn.net/article/details/104088366
[tensorflow 2.0 _ GAN_ part1]: https://azusa.blog.csdn.net/article/details/104088512
[tensorflow 2.0 _ GAN_ part2]: https://azusa.blog.csdn.net/article/details/104088559
[tensorflow 2.0 _ 1]: https://azusa.blog.csdn.net/article/details/104089741
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