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一、合并与分割

1. tf.concat-合并-原有的维度上进行累加

concat操作需要满足除拼接维度外，其余维度均相等

2.tf.stack-合并-创造一个新的维度

stack操作需要Tensor维度均相等

3.tf.unstack-分割

不能指定打散的数量，只能按维度进行分割

4.tf.split-分割

可以指定打散的数量

二、数据统计操作

1.tf.norm—张量的范数

二范数

一范数

2.tf.reduce_max/min/mean/sum—张量的最大值、最小值、平均值、和

3.tf.argmax/argmin—张量最大值的位置与最小值的位置

4.tf.equal—张量的比较

5.tf.unique—张量的独特值

三、张量排序

1.tf.sort-排序/tf.argsort-排序并返回索引

2.tf.math.top_k-最大值的前几个

3.案例

# 将无关信息屏蔽掉import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'import tensorflow as tftf.random.set_seed(2467)# output->[b,n] target->[b,]def accuracy(output,target,topk=(1,)):    maxk = max(topk)    batch_size = target.shape[0]    # 返回最大值前maxk个的索引    pred = tf.math.top_k(output,maxk).indices    # 转置    pred = tf.transpose(pred,perm=[1,0])    # 将target广播成pred形状    target_ = tf.broadcast_to(target,pred.shape)    # 比较    correct = tf.equal(pred,target_)    res = []    for k in topk:        correct_k = tf.cast(tf.reshape(correct[:k],[-1]),dtype=tf.float32)        # print('123=',correct_k)        correct_k = tf.reduce_sum(correct_k)        acc = float(correct_k / batch_size)        res.append(acc)    return resif __name__ == '__main__':    # 正态分布    output = tf.random.normal([10,6])    # 使6类概率总和为1    output = tf.math.softmax(output,axis=1)    # 均匀分布    target = tf.random.uniform([10],maxval=6,dtype=tf.int32)    print('prob:',output.numpy())    pred = tf.argmax(output,axis=1)    print('pred:',pred.numpy())    print('label:',target.numpy())    acc = accuracy(output,target,topk=(1,2,3,4,5,6))    print('top-1-6 acc:',acc)

prob: [[0.25310278 0.21715644 0.16043882 0.13088997 0.04334083 0.19507109] [0.05892418 0.04548917 0.00926314 0.14529602 0.66777605 0.07325139] [0.09742808 0.08304427 0.07460099 0.04067177 0.626185   0.07806987] [0.20478569 0.12294924 0.12010485 0.13751231 0.36418733 0.05046057] [0.11872064 0.31072393 0.12530336 0.1552888  0.2132587  0.07670452] [0.01519807 0.09672114 0.1460476  0.00934331 0.5649092  0.16778067] [0.04199061 0.18141054 0.06647632 0.6006175  0.03198383 0.07752118] [0.09226219 0.2346089  0.13022321 0.16295874 0.05362028 0.3263266 ] [0.07019574 0.0861177  0.10912605 0.10521299 0.2152082  0.4141393 ] [0.01882887 0.26597694 0.19122466 0.24109262 0.14920162 0.13367532]]pred: [0 4 4 4 1 4 3 5 5 1]label: [0 2 3 4 2 4 2 3 5 5]top-1-6 acc: [0.4000000059604645, 0.4000000059604645, 0.5, 0.699999988079071, 0.800000011920929, 1.0]

四、填充与复制

1.tf.pad-数据的填充

2.tf.tile-数据的复制

五、张量的限幅

1.tf.clip_by_value—根据值来裁剪

2.tf.clip_by_norm-根据范数裁剪

3.tf.nn.relu—将矩阵中每行小于0的值置0

4.tf.clip_by_global_norm-等比例裁剪

Gradient clipping梯度裁剪

5.案例

# 将无关信息屏蔽掉import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'import tensorflow as tffrom tensorflow import kerasfrom tensorflow.keras import datasets, layers, optimizers# 列出你所有的物理GPU，设置内存自动增长gpus = tf.config.experimental.list_physical_devices('GPU')for gpu in gpus:    tf.config.experimental.set_memory_growth(gpu, True)print(tf.__version__)(x, y), _ = datasets.mnist.load_data()x = tf.convert_to_tensor(x, dtype=tf.float32) / 50.y = tf.convert_to_tensor(y)y = tf.one_hot(y, depth=10)print('x:', x.shape, 'y:', y.shape)train_db = tf.data.Dataset.from_tensor_slices((x, y)).batch(128).repeat(30)x, y = next(iter(train_db))print('sample:', x.shape, y.shape)# print(x[0], y[0])def main():    # 784 => 512    w1, b1 = tf.Variable(tf.random.truncated_normal([784, 512], stddev=0.1)), tf.Variable(tf.zeros([512]))    # 512 => 256    w2, b2 = tf.Variable(tf.random.truncated_normal([512, 256], stddev=0.1)), tf.Variable(tf.zeros([256]))    # 256 => 10    w3, b3 = tf.Variable(tf.random.truncated_normal([256, 10], stddev=0.1)), tf.Variable(tf.zeros([10]))    # 优化器    optimizer = optimizers.SGD(lr=0.01)    for step, (x, y) in enumerate(train_db):        # [b, 28, 28] => [b, 784]        x = tf.reshape(x, (-1, 784))        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]            loss = tf.square(y - out)            # [b, 10] => [b]            loss = tf.reduce_mean(loss, axis=1)            # [b] => scalar            loss = tf.reduce_mean(loss)        # compute gradient        grads = tape.gradient(loss, [w1, b1, w2, b2, w3, b3])        print('==before==')        for g in grads:            print(tf.norm(g))        # 对所有可训练参数进行等比例裁剪        grads, _ = tf.clip_by_global_norm(grads, 15)        print('==after==')        for g in grads:            print(tf.norm(g))        # 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()

六、高阶op

1.tf.where—配合tf.gather_nd使用

tf.where(cond)—返回元素为True的坐标

tf.where(cond,A,B)—表示根据cond将A中的元素筛选后替换到B中相同位置

import tensorflow as tfa = tf.ones([3, 3])b = tf.zeros([3, 3])mask = [[1, 0, 0], [0, 0, 1], [0, 1, 1]]c = tf.convert_to_tensor(mask)cc = tf.cast(c, dtype=tf.bool)print(tf.where(cc, b, a))

tf.Tensor([[0. 1. 1.] [1. 1. 0.] [1. 0. 0.]], shape=(3, 3), dtype=float32)

2.tf.scatter_nd-根据坐标有目的的更新

3.tf.meshgrid-生成一个坐标轴

4.案例

import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'import tensorflow as tfimport matplotlib.pyplot as pltdef fun(x):    """    :param x: [b,2]    :return:    """    z = tf.math.sin(x[...,0]) + tf.math.sin(x[...,1])    return zif __name__ == '__main__':    x = tf.linspace(0.,2*3.14,500)    y = tf.linspace(0.,2*3.14,500)    # [500,500]    point_x, point_y = tf.meshgrid(x,y)    # [500,500,2]    points = tf.stack([point_x,point_y],axis=2)    print('points:',points.shape)    z = fun(points)    print('z:',z.shape)    plt.figure('plot 2d func value')    plt.imshow(z,origin='lower',interpolation='none')    plt.colorbar()        plt.figure('plot 2d func contour')    # 画出等高线    plt.contour(point_x,point_y,z)    plt.colorbar()    plt.show()

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