tensorflow常用函数

import tensorflow as tf
import  numpy as np

x = tf.constant([[1, 2, 3], [2, 2, 3]])
print(x)
print(tf.reduce_mean(x))
print(tf.reduce_sum(x, axis=1))

# 切分传入张量的第一维度，生成输入特征/标签对，构建数据集
features = tf.constant([12, 23, 10, 17])
labels = tf.constant([0, 1, 1, 0])
dataset = tf.data.Dataset.from_tensor_slices((features, labels))
print(dataset)
for e in dataset:
    print(e)

# with结构记录计算过程，gradient求出张量的梯度
with tf.GradientTape() as tape:
    w = tf.Variable(tf.constant(3.0))
    loss = tf.pow(w, 2)
grad = tape.gradient(loss, w)

# enumerate可遍历每个元素，组合为：索引 元素，常在for循环中使用
seq = ['one', 'two', 'three']
for i, ele in enumerate(seq):
    print(i, ele)

# tf.argmax(张量名，axis=操作轴)放回张量沿指定维度最大值的索引
test = np.array([[1, 2, 3], [2, 3, 4], [8, 7, 2]])
print(test)
print(tf.argmax(test, axis=0))