tensorflow 2.0 深度学习（第一部分 part2）

r囧r小猫 2023-07-02 04:25 2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基础** #

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# **数值类型**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70][] #

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# **张量(标量、向量、矩阵)** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 7][]

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# **索引与切片** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 21][]

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# **维度操作** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 28][]

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# **数学运算**![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 40][] #

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1.exp函数即指数函数：e的x次方的函数
    	exp：高等数学里以自然常数e为底的指数函数，等于2.718281828459045
    	numpy.exp()：返回e的幂次方，e是一个常数为2.718281828459045
    	exp(0) = e的0次方 = 1
    	exp(1) = e的1次方 = e = 2.718281828459045
     	exp(2) = e的2次方 = 7.38905609893065
     
    	>>> import numpy as np
    	>>> np.exp(1)
    	2.718281828459045
    	>>> np.exp(2)
    	7.38905609893065
    
    2.log10()、log2()
    	#log10：以10为底数的对数，对数是对求幂的逆运算
    	#log2：以2为底数的对数，对数是对求幂的逆运算
    
    	>>> import numpy as np
    	#log10：以10为底，求10的多少次方为1，即10的0次方为1
    	>>> np.log10(1)
    	0.0
    	#log10：以10为底，求10的多少次方为10，即10的1次方为10
    	>>> np.log10(10)
    	1.0
    	#log10：以10为底，求10的多少次方为100，即10的2次方为100
    	>>> np.log10(100)
    	2.0
    	>>> np.log10(1000)
    	3.0
    
    	#log2：以2为底，求2的多少次方为1，即2的0次方为1
    	>>> np.log2(1)
    	0.0
    	#log2：以2为底，求2的多少次方为2，即2的1次方为2
    	>>> np.log2(2)
    	1.0
    	#log2：以2为底，求2的多少次方为4，即2的2次方为4
    	>>> np.log2(4)
    	2.0
    	>>> np.log2(8)
    	3.0
    	>>> np.log2(16)
    	4.0
    
    3.log()
    	#log默认以e为底，求e(2.718281828459045)的多少次方为1，即e(2.718281828459045)的0次方为1
    	>>> np.log(1)
    	0.0
    	#log默认以e为底，求e(2.718281828459045)的多少次方为e，即e(2.718281828459045)的1次方为e
    	>>> np.log(np.e)
    	1.0
    	#log默认以e为底，求e(2.718281828459045)的多少次方为10，即e(2.718281828459045)的2.302585092994046次方为10
    	>>> np.log(10)
    	2.302585092994046
    	#log默认以e为底，求e(2.718281828459045)的多少次方为100，即e(2.718281828459045)的4.605170185988092次方为100
    	>>> np.log(100)
    	4.605170185988092
    
    4.math.log()、math.log2()、math.log10()
    	#math.log()、math.log2()、math.log10() 等同于 numpy的log()、log2()、log10()
    	>>> import math
    	#log默认以e为底，求e(2.718281828459045)的多少次方为1，即e(2.718281828459045)的0次方为1
    	>>> math.log(1)
    	0.0
    	>>> math.log(math.e)
    	1.0
    	>>> math.log(10)
    	2.302585092994046
    	>>> math.log(100)
    	4.605170185988092
    
    	#log2：以2为底，求2的多少次方为1，即2的0次方为1
    	>>> math.log2(1)
    	0.0
    	>>> math.log2(2)
    	1.0
    	>>> math.log2(4)
    	2.0
    	 
    	#log10：以10为底，求10的多少次方为1，即10的0次方为1
    	>>> math.log10(1)
    	0.0
    	>>> math.log10(10)
    	1.0
    	>>> math.log10(100)
    	2.0
    
    5.math.log(m,n)	和对应的numpy写法（np.log(m) / np.log(n)）
    	#math.log(m,n) 表示n为底数，m为真数，即以n为底m的对数
    
    	#4的1次方为4
    	>>> math.log(4,4)
    	1.0
    	#3的1.2618595071429148次方约为4
    	>>> math.log(4,3)
    	1.2618595071429148
    	#2的2次方约为4
    	>>> math.log(4,2)
    	2.0
    
    	#等同于 math.log(4,4)
    	>>> np.log(4) / np.log(4)
    	1.0
    	#等同于 math.log(4,3)
    	>>> np.log(4) / np.log(3)
    	1.2618595071429148
    	#等同于 math.log(4,2)
    	>>> np.log(4) / np.log(2)
    	2.0

![20200126115747862.png][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 42][]

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![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 46][]

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# **前向传播** #

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 47][]

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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],
    # 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) #构建批量大小
    train_iter = iter(train_db) #获取数据集的遍历对象--生成器
    sample = next(train_iter) #next调用生成器第一次遍历的批量大小数据
    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)) #第一层线性的权重
    b1 = tf.Variable(tf.zeros([256])) #第一层线性的偏置
    w2 = tf.Variable(tf.random.truncated_normal([256, 128], stddev=0.1)) #第二层线性的权重
    b2 = tf.Variable(tf.zeros([128])) #第二层线性的偏置
    w3 = tf.Variable(tf.random.truncated_normal([128, 10], stddev=0.1)) #第三层线性的权重
    b3 = tf.Variable(tf.zeros([10])) #第三层线性的偏置
    lr = 1e-3 #学习率
    
    #epoch定义训练次数
    for epoch in range(10): # iterate db for 10
        #每次遍历的是数据集中的一个批量大小的数据
        for step, (x, y) in enumerate(train_db): # for every batch
            # x:[128, 28, 28]
            # y: [128]
            # [b, 28, 28] => [b, 28*28]
            x = tf.reshape(x, [-1, 28*28]) #展平为[批量大小, 28*28]
            #构建梯度记录环境
            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]) #第一层线性运算
                h1 = tf.nn.relu(h1) #第一层非线性运算--激活函数
                # [b, 256] => [b, 128]
                h2 = h1@w2 + b2  #第二层线性运算
                h2 = tf.nn.relu(h2) #第二层非线性运算--激活函数
                # [b, 128] => [b, 10] 
                out = h2@w3 + b3 #第三层线性运算
    
                # compute loss
                # out: [b, 10]
                # y: [b] => [b, 10]
                y_onehot = tf.one_hot(y, depth=10) #真实标签one-hot化
    
                # mse = mean(sum(y-out)^2)  均方差：计算每个样本的平均误差，再把总误差除以总样本数
                # [b, 10]
                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)
            # Step3. optimize and update w1, w2, w3, b1, b2, b3
            # 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
            # compute gradients
            grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
            # print(grads)
    
            #优化器规则，根据 模型参数θ = θ - lr * grad 更新网络参数
            # 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))

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9henVzYS5ibG9nLmNzZG4ubmV0_size_16_color_FFFFFF_t_70 49][]

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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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