tensorflow 2.0 深度学习（第一部分 part3）

一时失言乱红尘 2023-07-02 04:25 16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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# **TensorFlow进阶** #

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# **合并(拼接concat、堆叠stack)、分割** #

![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][]

![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][]

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# **数据统计** #

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# **向量范数** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 5][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 6][]

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# **求解张量在某个维度上的最大值/最小值/均值/和，也可以求全局最大值/最小值/均值/和** #

![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][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 9][]

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# **张量比较：比较预测类别标签张量和真实类别张量比较，统计准确率指标** #

![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][]

import  tensorflow as tf
    import  os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    tf.random.set_seed(2467)
    
    def accuracy(output, target, topk=(1,)):
        maxk = max(topk) #6 取top中的前6个概率值的索引号
        batch_size = target.shape[0] #10 样本数
    
        #output.shape为(10, 6)，此处即为取每个样品的top中的前6个概率值的索引号
        pred = tf.math.top_k(output, maxk).indices #shape=(10, 6) 即[批量大小,6个概率值]
        pred = tf.transpose(pred, perm=[1, 0]) #shape=(6, 10) 即[6个概率值,批量大小]
        target_ = tf.broadcast_to(target, pred.shape) #shape=(10,) => shape=(6, 10) 即把真实类别标签拷贝6份
        # correct.shape=(6, 10) correct中的元素值为True/False
        correct = tf.equal(pred, target_)
    
        res = []
        for k in topk: #k 为 1/2/3/4/5/6
            #correct[:k]分别为shape=(1, 10)、(2, 10)、(3, 10)、(4, 10)、(5, 10)、(6, 10)
            #correct_k.shape=(10,)分别为shape=(10,)、(20,)、(30,)、(40,)、(50,)、(60,)
            #correct中的元素值为True/False，correct_k的元素值为1/0
            correct_k = tf.cast(tf.reshape(correct[:k], [-1]), dtype=tf.float32)
            correct_k = tf.reduce_sum(correct_k) #correct_k是一个实数值：4.0、4.0、5.0、7.0、8.0、10.0
            acc = float(correct_k* (100.0 / batch_size)) #准确率分别为40.0、40.0、50.0、70.0、80.0、100.0
            res.append(acc)
        return res
    
    #tf.random.normal(shape, mean=0.0, stddev=1.0)可以创建形状为 shape，均值为mean，标准差为 stddev 的正态分布
    output = tf.random.normal([10, 6]) #shape=(10, 6) 作为每个样品类别预测概率值
    output = tf.math.softmax(output, axis=1) #softmax预算把预测值转换为概率值
    #tf.random.uniform(shape, minval=0, maxval=None, dtype=tf.float32)可以创建采样自[minval,maxval]区间的均匀分布的张量
    target = tf.random.uniform([10], maxval=6, dtype=tf.int32) #shape=(10,) 作为真实标签值，即真实类别的类别号
    print('prob:', output.numpy())
    pred = tf.argmax(output, axis=1) 
    print('pred:', pred.numpy())    #[0 4 4 4 1 4 3 5 5 1] 获取每个样品的类别预测概率最大值的元素索引，索引号即作为该样品的预测类别号
    print('label:', target.numpy()) #[0 2 3 4 2 4 2 3 5 5] 真实类别的类别号
    
    acc = accuracy(output, target, topk=(1,2,3,4,5,6))
    print('top-1-6 acc:', acc)

#  #

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# **填充与复制：0填充padding** #

![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][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 16][]

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# **数据限幅：限制数据的范围** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 17][]

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# **张量限幅、梯度裁剪(clip\_by\_value、clip\_by\_norm、clip\_by\_global\_norm)**   ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 18][] #

>>> import tensorflow as tf
    >>> w1=tf.random.normal([3,3])
    >>> w2=tf.random.normal([3,3])
    >>> w1
    <tf.Tensor: id=5, shape=(3, 3), dtype=float32, numpy=
    array([[-0.3745235 , -0.54776704, -0.6978908 ],
           [-0.48667282, -1.9662677 ,  1.2693951 ],
           [-1.6218463 , -1.3147658 ,  1.1985897 ]], dtype=float32)>
    #tf.norm(a)默认为执行L2范数的tf.norm(a. ord=2)，等同于tf.sqrt(tf.reduce_sum(tf.square(a)))
    #计算裁剪前的网络参数θ的总范数global_norm：所有裁剪前的网络参数θ的L2范数tf.norm(θ)的平方和，然后开方sqrt
    >>> global_norm=tf.math.sqrt(tf.norm(w1)**2+tf.norm(w2)**2)
    >>> global_norm #4.7265425
    <tf.Tensor: id=27, shape=(), dtype=float32, numpy=4.7265425>
    #通过tf.clip_by_global_norm([θ],MAX_norm)裁剪后，网络参数的梯度组的总范数缩减到MAX_norm=2
    #clip_by_global_norm返回裁剪后的List[参数θ] 和 裁剪前的梯度总范数和global_norm 的2个对象
    >>> (ww1,ww2),global_norm=tf.clip_by_global_norm([w1,w2],2) #梯度裁剪一般在计算出梯度后、梯度更新之前进行
    >>> ww1
    <tf.Tensor: id=47, shape=(3, 3), dtype=float32, numpy=
    array([[-0.15847673, -0.2317834 , -0.29530713],
           [-0.20593184, -0.832011  ,  0.53713477],
           [-0.6862717 , -0.556333  ,  0.50717396]], dtype=float32)>
    >>> ww2
    <tf.Tensor: id=48, shape=(3, 3), dtype=float32, numpy=
    array([[ 0.03117203, -0.7264457 ,  0.32293826],
           [ 0.5894358 ,  0.87403387,  0.04680141],
           [ 0.0015509 ,  0.15240058,  0.05759645]], dtype=float32)>
    >>> global_norm
    <tf.Tensor: id=35, shape=(), dtype=float32, numpy=4.7265425>
    #计算裁剪后的网络参数θ的总范数global_norm：所有裁剪后的网络参数θ的L2范数tf.norm(θ)的平方和，然后开方sqrt
    >>> global_norm2 = tf.math.sqrt(tf.norm(ww1)**2+tf.norm(ww2)**2)
    >>> global_norm2
    <tf.Tensor: id=64, shape=(), dtype=float32, numpy=1.9999998>
    >>> print(global_norm, global_norm2)
    tf.Tensor(4.7265425, shape=(), dtype=float32) tf.Tensor(1.9999998, shape=(), dtype=float32)

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 19][]

import  tensorflow as tf
    from    tensorflow import keras
    from    tensorflow.keras import datasets, layers, optimizers
    import  os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
    print(tf.__version__)
    
    (x, y), _ = datasets.mnist.load_data()
    x = tf.convert_to_tensor(x, dtype=tf.float32) / 50. #标准化/归一化
    y = tf.convert_to_tensor(y)
    y = tf.one_hot(y, depth=10) #真实标签one-hot化
    print('x:', x.shape, 'y:', y.shape)
    #构建批量大小和epoch训练次数
    train_db = tf.data.Dataset.from_tensor_slices((x,y)).batch(128).repeat(30)
    x,y = next(iter(train_db))  #获取生成器并调用next遍历第一个批量大小的数据
    print('sample:', x.shape, y.shape)
    # print(x[0], y[0])
    
    def main():
        # 784 => 512 第一层权重[输入神经元数, 输出神经元数]，偏置[输出神经元数]
        w1, b1 = tf.Variable(tf.random.truncated_normal([784, 512], stddev=0.1)), tf.Variable(tf.zeros([512]))
        # 512 => 256 第二层权重[输入神经元数, 输出神经元数]，偏置[输出神经元数]
        w2, b2 = tf.Variable(tf.random.truncated_normal([512, 256], stddev=0.1)), tf.Variable(tf.zeros([256]))
        # 256 => 10 第三层权重[输入神经元数, 输出神经元数]，偏置[输出神经元数]
        w3, b3 = tf.Variable(tf.random.truncated_normal([256, 10], stddev=0.1)), tf.Variable(tf.zeros([10]))
        optimizer = optimizers.SGD(lr=0.01) #SGD随机梯度下降优化算法
    
        #每次遍历训练集生成器的一个批量大小数据
        for step, (x,y) in enumerate(train_db):
            # [b, 28, 28] => [b, 784] 展平
            x = tf.reshape(x, (-1, 784))
            #构建梯度记录环境
            with tf.GradientTape() as tape:
                # 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
                # out = tf.nn.relu(out)
    
                # compute loss
                # [b, 10] - [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-out)
                # [b, 10] => [b] 计算每个样本的平均误差
                loss = tf.reduce_mean(loss, axis=1)
                # [b] => scalar 总误差除以样本数
                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]) 
            #  compute gradients。根据loss 求w1, w2, w3, b1, b2, b3的梯度值 用于后面继续更新对应的模型参数θ。
            grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
    
            # print('==before==')
            # for g in grads:  #计算所有裁剪前的网络参数θ的梯度值的L2范数tf.norm(a)，等同于tf.norm(a. ord=2)、tf.sqrt(tf.reduce_sum(tf.square(a)))
            #     print(tf.norm(g)) #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)))
     
            #通过tf.clip_by_global_norm([θ],MAX_norm)裁剪后，网络参数的梯度组的总范数缩减到MAX_norm=15
            #clip_by_global_norm返回裁剪后的List[参数θ] 和 裁剪前的梯度总范数和global_norm 的2个对象
            grads,  _ = tf.clip_by_global_norm(grads, 15) #梯度裁剪一般在计算出梯度后、梯度更新之前进行
    
            # print('==after==')
            # for g in grads:  #计算所有裁剪后的网络参数θ的梯度值的L2范数tf.norm(a)，等同于tf.norm(a. ord=2)、tf.sqrt(tf.reduce_sum(tf.square(a)))
            #     print(tf.norm(g)) #tf.norm(a)默认为执行L2的tf.norm(a. ord=2)，等同于tf.sqrt(tf.reduce_sum(tf.square(a)))
    
            #优化器规则，根据 模型参数θ = θ - lr * grad 更新网络参数
            # update w' = w - lr*grad
            optimizer.apply_gradients(zip(grads, [w1, b1, w2, b2, w3, b3]))
    
            if step % 100 == 0:
                print(step, 'loss:', float(loss))
    
    if __name__ == '__main__':
        main()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 20][]

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# **tf.gather：根据axis轴上的索引维度获取数据** #

# ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 22][] #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 23][]

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# **tf.gather\_nd：根据axis轴上批量的索引维度获取数据** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 24][]

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# **tf.boolean\_mask：通过给定掩码(mask)的方式采样** #

# ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 25][] #

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# **tf.where：根据cond条件的真假从a或b中读取数据 / 获取cond张量中所有为True的元素的索引** #

# ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 26][] #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 27][]

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# **scatter\_nd：往全0张量中的指定索引上更新数值** #

# ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 28][] #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 29][]

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# **tf.meshgrid：生成网格坐标** #

# ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 30][] #

>>> x.shape
    TensorShape([100])
    >>> y.shape
    TensorShape([100])
    >>> x1,y2 = tf.meshgrid(x,y)
    >>> x1.shape
    TensorShape([100, 100])
    >>> y1.shape
    TensorShape([100, 100])

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 31][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 32][]

import tensorflow as tf
    import matplotlib.pyplot as plt
    
    def func(x):
        """
        :param x: [b, 2]
        :return:
        """
        z = tf.math.sin(x[...,0]) + tf.math.sin(x[...,1])
        return z
    
    #x轴上采样500个数据点
    x = tf.linspace(0., 2*3.14, 500) #TensorShape([500])
    #y轴上采样500个数据点
    y = tf.linspace(0., 2*3.14, 500) #TensorShape([500])
    # [50, 50] point_x和point_y的shape均为TensorShape([500, 500])
    #生成网格点，并拆分返回，point_x包含所有数据点的x坐标，point_y包含所有数据点的y坐标
    point_x, point_y = tf.meshgrid(x, y)
    # [50, 50, 2] 把point_x和point_y在axis=2轴上拼接为一个张量
    points = tf.stack([point_x, point_y], axis=2)
    # points = tf.reshape(points, [-1, 2])
    print('points:', points.shape) #points: (500, 500, 2)
    z = func(points)
    print('z:', z.shape) #TensorShape([500, 500])
    
    plt.figure('plot 2d func value')
    plt.imshow(z, origin='lower', interpolation='none')
    plt.colorbar()
    plt.figure('plot 2d func contour')
    plt.contour(point_x, point_y, z)
    plt.colorbar()
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 33][]

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# **常用数据集功能：预处理(Preprocess)、随机打散(Shuffle)、批训练(Train on batch)等** #

# ![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 34][] #

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import  matplotlib
    from    matplotlib import pyplot as plt
    # 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 
    import  tensorflow as tf
    from    tensorflow import keras
    from    tensorflow.keras import datasets, layers, optimizers
    import  os
     
    os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
    print(tf.__version__)
     
    def preprocess(x, y): 
        # [b, 28, 28], [b]
        print(x.shape,y.shape)
        x = tf.cast(x, dtype=tf.float32) / 255. #标准化
        x = tf.reshape(x, [-1, 28*28])  #展平为(批量大小, 行*列)
        y = tf.cast(y, dtype=tf.int32)  
        y = tf.one_hot(y, depth=10)	#真实标签one-hot化，处理为(样本数,10)
        return x,y
     
    (x, y), (x_test, y_test) = datasets.mnist.load_data()
    print('x:', x.shape, 'y:', y.shape, 'x test:', x_test.shape, 'y test:', y_test)
     
    batchsz = 512
    train_db = tf.data.Dataset.from_tensor_slices((x, y)) #构建样本数据+真实标签的可遍历的数据集
    train_db = train_db.shuffle(1000)	#shuffle打乱样本数据顺序
    train_db = train_db.batch(batchsz)  #构建数据集的批量大小的维度
    train_db = train_db.map(preprocess) #对每个批量中的样本数据+真实标签进行预处理
    train_db = train_db.repeat(20)	#repeat定义epoch训练次数
     
    test_db = tf.data.Dataset.from_tensor_slices((x_test, y_test))
    test_db = test_db.shuffle(1000).batch(batchsz).map(preprocess)
    x,y = next(iter(train_db)) #使用iter返回生成器，next执行生成器的一次遍历，即获取一个批量大小的数据
    print('train sample:', x.shape, y.shape)
    # print(x[0], y[0])
     
    def main():
        # learning rate
        lr = 1e-2 #学习率
        accs,losses = [], [] 
    
        # 784 => 512 第一层权重w1.shape为(784, 256)，偏置b1.shape为(256)
        w1, b1 = tf.Variable(tf.random.normal([784, 256], stddev=0.1)), tf.Variable(tf.zeros([256]))
        # 512 => 256 第二层权重w2.shape为(256, 128)，偏置b2.shape为(128)
        w2, b2 = tf.Variable(tf.random.normal([256, 128], stddev=0.1)), tf.Variable(tf.zeros([128]))
        # 256 => 10 第三层权重w3.shape为(128, 10)，偏置b3.shape为(10)
        w3, b3 = tf.Variable(tf.random.normal([128, 10], stddev=0.1)), tf.Variable(tf.zeros([10]))
     
        #每次遍历一个批量大小的数据(批量大小, 28, 28)
        for step, (x,y) in enumerate(train_db):
            # [b, 28, 28] => [b, 784]  展平为(批量大小, 行*列)
            x = tf.reshape(x, (-1, 784))
            #记录参数θ的梯度
            with tf.GradientTape() as tape:
                # layer1. 第一层隐藏层：线性x@w1+b1、非线性relu(h1)
                h1 = x @ w1 + b1
                h1 = tf.nn.relu(h1)
                # layer2 第二层隐藏层：线性h1@w2+b2、非线性relu(h2)
                h2 = h1 @ w2 + b2
                h2 = tf.nn.relu(h2)
                # output 第三层输出层：线性h2@w3+b2
                out = h2 @ w3 + b3
                # out = tf.nn.relu(out)
    
                # compute loss 计算mse = mean(sum(y-out)^2)
                # [b, 10] - [b, 10]
                loss = tf.square(y-out)
                # [b, 10] => 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]) 
            grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3]) 
            #assign_sub()将原地(In-place)减去给定的参数值，实现参数θ的自我更新网络参数
            for p, g in zip([w1, b1, w2, b2, w3, b3], grads):
                p.assign_sub(lr * g) #θ = θ - lr * θ_grad
     
            # print
            if step % 80 == 0:
                print(step, 'loss:', float(loss))
                losses.append(float(loss))
     
            if step %80 == 0:
                # evaluate/test
                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
                    # [b, 10] => [b]
                    pred = tf.argmax(out, axis=1) #获取第二维度的类别最大概率值的索引值，即为类别索引号
                    # convert one_hot y to number y
                    y = tf.argmax(y, axis=1) #因为真实标签已经one_hot化了，因此同样需要获取第二维度的类别最大概率值的索引值，即为类别索引号
                    # bool type
                    correct = tf.equal(pred, y) #比较各对应索引上的类别值是否相同，返回True/False的bool值
                    # bool tensor => int tensor => numpy 
                    total_correct += tf.reduce_sum(tf.cast(correct, dtype=tf.int32)).numpy()#把bool值转换为int，然后计算总和
                    total += x.shape[0] #总和除以样本数，计算一个批量大小样本数的平均准确率
    
                print(step, 'Evaluate Acc:', total_correct/total)
                accs.append(total_correct/total) #计算全部批量大小样本数的平均准确率
    
    
        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.savefig('train.svg')
    
        plt.figure()
        plt.plot(x, accs, color='C1', marker='s', label='测试')
        plt.ylabel('准确率')
        plt.xlabel('Step')
        plt.legend()
        plt.savefig('test.svg')
    
    if __name__ == '__main__':
        main()

import  tensorflow as tf
    from    tensorflow import keras
    from    tensorflow.keras import datasets
    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
     
    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
    o1 = tf.nn.relu(o1)

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[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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