TensorFlow2-实战-手写数字识别（一）：手写版（不用模型）、步骤：【初始化参数】--＞【循环（①根据参数W、B与输入值X计算出预测值Y；②计算Loss；③计算梯度；④梯度下降来更新参数）】

深藏阁楼爱情的钟 2023-09-29 10:34 4阅读 0赞

模型计算步骤：

*  初始化参数w、b
 *  根据参数，通过模型前向计算，得到输入特征值对应的输出(步骤①+②)
 *  根据预测值与真实值计算Loss
 *  计算梯度
 *  根据梯度下降算法更新参数

import pandas as pd
    import tensorflow as tf
    from tensorflow.keras import datasets, optimizers
    import os
    
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    
    # 读取数据集
    # x: [60k, 28, 28], y: [60k]
    mnist_dataset = datasets.mnist.load_data()
    dataset_train, datasset_val = mnist_dataset[0], mnist_dataset[1]
    (x, y) = dataset_train
    (x_val, y_val) = datasset_val
    
    # 将ndarray数组转为Tensor格式
    x = tf.convert_to_tensor(x, dtype=tf.float32) / 255.  # x: [0~255] => [0~1.]
    print("x.shape = {0}, x[0] = \n{1}".format(x.shape, pd.DataFrame(x[0].numpy())))  # x.shape =  (60000, 28, 28)
    y = tf.convert_to_tensor(y, dtype=tf.int32)
    print("y.shape = {0}, y = {1}".format(y.shape, y))  # y.shape = (60000,), y = [5 0 4 ... 5 6 8]
    
    print("\n数据集数据特征：")
    print("x.shape = {0}, y.shape = {1}, x.dtype = {2}, y.dtype = {3}".format(x.shape, y.shape, x.dtype, y.dtype))
    print("tf.reduce_min(x) = {0}, tf.reduce_max(x) = {1}".format(tf.reduce_min(x), tf.reduce_max(x)))
    print("tf.reduce_min(y) = {0}, tf.reduce_max(y) = {1}".format(tf.reduce_min(y), tf.reduce_max(y)))
    
    # 从(x, y)中抽取训练数据集,batch_size=128
    train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128)
    train_iter = iter(train_db)
    sample = next(train_iter)
    print('\n每一个batch数据的形状: x.shape = {0}, y.shape = {1}'.format(sample[0].shape, sample[1].shape))
    
    # 初始化参数
    # [b, 784] => [b, 256] => [b, 128] => [b, 10]
    # [dim_in, dim_out], [dim_out]
    w1 = tf.Variable(tf.random.truncated_normal([784, 256], mean=0, stddev=0.1))
    b1 = tf.Variable(tf.zeros([256]))
    w2 = tf.Variable(tf.random.truncated_normal([256, 128], mean=0, stddev=0.1))
    b2 = tf.Variable(tf.zeros([128]))
    w3 = tf.Variable(tf.random.truncated_normal([128, 10], mean=0, stddev=0.1))
    b3 = tf.Variable(tf.zeros([10]))
    
    # 设置学习率
    lr = 1e-3
    
    # 训练10个epoch
    for epoch in range(10):  # iterate db for 10
        # 遍历所有的batch
        for batch_idx, (x, y) in enumerate(train_db):
            # x:[128, 28, 28]；y: [128]
            # 将特征值进行维度变换
            x = tf.reshape(x, [-1, 28 * 28])  # [b, 28, 28] => [b, 28*28]
            # Tensorflow使用梯度带（tf.GradientTape）来记录正向运算过程，然后反向传播自动得到梯度值。
            with tf.GradientTape() as tape:  # tf.Variable
                # 步骤一: 根据参数，通过模型前向计算，得到输入特征值对应的输出(步骤①+②)
                # ①: 第一层网络
                # 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)
    
                # ②: 第二层网络
                # [b, 256] => [b, 128]
                h2 = h1 @ w2 + b2
                h2 = tf.nn.relu(h2)
                # [b, 128] => [b, 10]
                out = h2 @ w3 + b3
    
                # 步骤二: 计算Loss
                # out: [b, 10]
                # y: [b] => [b, 10]
                y_onehot = tf.one_hot(y, depth=10)
                loss = tf.reduce_mean(tf.square(y_onehot - out))  # mse = mean(sum(y-out)^2)
    
            # 步骤三: 计算梯度
            grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])
            # print(grads)
    
            # 步骤四: 根据梯度下降算法更新参数
            # w1 = w1 - lr * w1_grad
            # ①、使用optimizer进行梯度下降
            # optimizer.apply_gradients(zip(grads, [w1, b1, w2, b2, w3, b3]))
            # ②、不使用optimizer, 手动进行梯度下降
            # 要进行原地更新【w1 = w1 - lr * grads[0], 生成的结果会从Variable又变回Tensor, 会出错】
            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 batch_idx % 100 == 0:
                print("epoch = {0}, batch_idx = {1}, loss = {2}".format(epoch, batch_idx, float(loss)))

打印结果：

x.shape = (60000, 28, 28), x[0] = 
         0    1    2    3         4  ...         23   24   25   26   27
    0   0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    1   0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    2   0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    3   0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    4   0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    5   0.0  0.0  0.0  0.0  0.000000 ...   0.498039  0.0  0.0  0.0  0.0
    6   0.0  0.0  0.0  0.0  0.000000 ...   0.250980  0.0  0.0  0.0  0.0
    7   0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    8   0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    9   0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    10  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    11  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    12  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    13  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    14  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    15  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    16  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    17  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    18  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    19  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    20  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    21  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    22  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    23  0.0  0.0  0.0  0.0  0.215686 ...   0.000000  0.0  0.0  0.0  0.0
    24  0.0  0.0  0.0  0.0  0.533333 ...   0.000000  0.0  0.0  0.0  0.0
    25  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    26  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    27  0.0  0.0  0.0  0.0  0.000000 ...   0.000000  0.0  0.0  0.0  0.0
    
    [28 rows x 28 columns]
    y.shape = (60000,), y = [5 0 4 ... 5 6 8]
    
    数据集数据特征：
    x.shape = (60000, 28, 28), y.shape = (60000,), x.dtype = <dtype: 'float32'>, y.dtype = <dtype: 'int32'>
    tf.reduce_min(x) = 0.0, tf.reduce_max(x) = 1.0
    tf.reduce_min(y) = 0, tf.reduce_max(y) = 9
    
    每一个batch数据的形状: x.shape = (128, 28, 28), y.shape = (128,)
    epoch = 0, batch_idx = 0, loss = 0.31570330262184143
    epoch = 0, batch_idx = 100, loss = 0.2133779525756836
    epoch = 0, batch_idx = 200, loss = 0.18124043941497803
    epoch = 0, batch_idx = 300, loss = 0.185683935880661
    epoch = 0, batch_idx = 400, loss = 0.16511934995651245
    epoch = 1, batch_idx = 0, loss = 0.16189470887184143
    epoch = 1, batch_idx = 100, loss = 0.1595584750175476
    epoch = 1, batch_idx = 200, loss = 0.14553877711296082
    epoch = 1, batch_idx = 300, loss = 0.151431605219841
    epoch = 1, batch_idx = 400, loss = 0.13905295729637146
    epoch = 2, batch_idx = 0, loss = 0.13708670437335968
    epoch = 2, batch_idx = 100, loss = 0.13877740502357483
    epoch = 2, batch_idx = 200, loss = 0.1271398961544037
    epoch = 2, batch_idx = 300, loss = 0.13122180104255676
    epoch = 2, batch_idx = 400, loss = 0.12365438044071198
    epoch = 3, batch_idx = 0, loss = 0.12157578766345978
    epoch = 3, batch_idx = 100, loss = 0.12561878561973572
    epoch = 3, batch_idx = 200, loss = 0.11524558067321777
    epoch = 3, batch_idx = 300, loss = 0.11790748685598373
    epoch = 3, batch_idx = 400, loss = 0.11349649727344513
    epoch = 4, batch_idx = 0, loss = 0.11081816256046295
    epoch = 4, batch_idx = 100, loss = 0.1163901537656784
    epoch = 4, batch_idx = 200, loss = 0.10680149495601654
    epoch = 4, batch_idx = 300, loss = 0.1084493026137352
    epoch = 4, batch_idx = 400, loss = 0.1061994805932045
    epoch = 5, batch_idx = 0, loss = 0.10276894271373749
    epoch = 5, batch_idx = 100, loss = 0.10943224281072617
    epoch = 5, batch_idx = 200, loss = 0.10031596571207047
    epoch = 5, batch_idx = 300, loss = 0.10134726762771606
    epoch = 5, batch_idx = 400, loss = 0.10063724219799042
    epoch = 6, batch_idx = 0, loss = 0.09647054225206375
    epoch = 6, batch_idx = 100, loss = 0.10392111539840698
    epoch = 6, batch_idx = 200, loss = 0.09510777145624161
    epoch = 6, batch_idx = 300, loss = 0.09576252847909927
    epoch = 6, batch_idx = 400, loss = 0.09620489180088043
    epoch = 7, batch_idx = 0, loss = 0.09139622002840042
    epoch = 7, batch_idx = 100, loss = 0.09940839558839798
    epoch = 7, batch_idx = 200, loss = 0.09078116714954376
    epoch = 7, batch_idx = 300, loss = 0.09128470718860626
    epoch = 7, batch_idx = 400, loss = 0.09251183271408081
    epoch = 8, batch_idx = 0, loss = 0.0871615782380104
    epoch = 8, batch_idx = 100, loss = 0.09560022503137589
    epoch = 8, batch_idx = 200, loss = 0.08711323887109756
    epoch = 8, batch_idx = 300, loss = 0.08756624162197113
    epoch = 8, batch_idx = 400, loss = 0.08939112722873688
    epoch = 9, batch_idx = 0, loss = 0.08357226848602295
    epoch = 9, batch_idx = 100, loss = 0.09233171492815018
    epoch = 9, batch_idx = 200, loss = 0.08394805341959
    epoch = 9, batch_idx = 300, loss = 0.08437744528055191
    epoch = 9, batch_idx = 400, loss = 0.08669549226760864
    
    Process finished with exit code 0