import tensorflow as tf

# 創(chuàng)建標(biāo)量
scalar = tf.constant(5)
# 創(chuàng)建向量
vector = tf.constant([1, 2, 3])
# 創(chuàng)建矩陣
matrix = tf.constant([[1, 2], [3, 4]])
# 創(chuàng)建3維張量
tensor = tf.constant([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

# 從Python列表創(chuàng)建
tensor_from_list = tf.convert_to_tensor([1, 2, 3])

# 從Numpy數(shù)組創(chuàng)建
import numpy as np
array = np.array([[1, 2], [3, 4]])
tensor_from_np = tf.convert_to_tensor(array)

# 生成全零張量
zeros = tf.zeros([2, 3])

# 生成全一張量
ones = tf.ones([3, 2])

# 生成隨機(jī)正態(tài)分布張量
randn = tf.random.normal([2, 2], mean=0.0, stddev=1.0)

# 生成均勻分布張量
randu = tf.random.uniform([3, 3], minval=0, maxval=1)

a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 6], [7, 8]])

# 逐元素加法
add = tf.add(a, b)

# 逐元素乘法
mul = tf.multiply(a, b)

# 矩陣乘法
matmul = tf.matmul(a, b)

# 張量求和
sum_all = tf.reduce_sum(a)
sum_axis0 = tf.reduce_sum(a, axis=0)
sum_axis1 = tf.reduce_sum(a, axis=1)

# 張量平均值
mean = tf.reduce_mean(a)

# 張量最大值
max_val = tf.reduce_max(a)

tensor = tf.constant([[1, 2], [3, 4], [5, 6]])

# 獲取張量形狀
shape = tensor.shape

# 改變張量形狀
reshaped = tf.reshape(tensor, [2, 3])

# 轉(zhuǎn)置張量
transposed = tf.transpose(tensor)

# 擴(kuò)展維度
expanded = tf.expand_dims(tensor, axis=0)

# 壓縮維度
squeezed = tf.squeeze(expanded)

tensor = tf.constant([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# 獲取單個元素
elem = tensor[1, 2]  # 獲取第2行第3列的元素

# 獲取行切片
row_slice = tensor[1:, :]  # 獲取第2行及以后的所有行

# 獲取列切片
col_slice = tensor[:, 1]  # 獲取第2列

# 使用步長切片
strided_slice = tensor[::2, ::2]  # 每隔2個元素取一個

a = tf.constant([[1, 2, 3]])  # 形狀(1,3)
b = tf.constant([[4], [5], [6]])  # 形狀(3,1)

# 廣播加法
c = a + b  # 結(jié)果形狀(3,3)

tensor = tf.constant([[1, 2, 3], [4, 5, 6]])

# 沿軸0求和
sum0 = tf.reduce_sum(tensor, axis=0)  # [5,7,9]

# 沿軸1求最大值
max1 = tf.reduce_max(tensor, axis=1)  # [3,6]

# 計(jì)算邏輯與
logical = tf.reduce_all(tensor > 3)  # False

# 計(jì)算均值
mean = tf.reduce_mean(tensor)  # 3.5

a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 6], [7, 8]])

# 沿軸0拼接
concat0 = tf.concat([a, b], axis=0)

# 沿軸1拼接
concat1 = tf.concat([a, b], axis=1)

# 張量分割
split0 = tf.split(a, num_or_size_splits=2, axis=0)
split1 = tf.split(a, num_or_size_splits=[1, 1], axis=1)

tensor = tf.constant([[3, 1, 4], [1, 5, 9]])

# 排序
sorted_values, sorted_indices = tf.sort(tensor, direction='DESCENDING')

# argsort
argsort = tf.argsort(tensor)

# top-k
top_k_values, top_k_indices = tf.math.top_k(tensor, k=2)

# 張量收集
tensor = tf.constant([[0, 1, 2], [3, 4, 5]])
indices = tf.constant([0, 1])
gathered = tf.gather(tensor, indices)  # 收集第0行和第1行

# 張量分散
updates = tf.constant([10, 20])
scattered = tf.tensor_scatter_nd_update(tensor, [[0, 0], [1, 1]], updates)

# 張量條件操作
cond = tf.where(tensor > 3, tensor, tf.zeros_like(tensor))  # 大于3保留原值，否則設(shè)為0

x = tf.Variable(3.0)
with tf.GradientTape() as tape:
    y = x ** 2 + 2 * x + 1
dy_dx = tape.gradient(y, x)  # 2x + 2 = 8

# 張量轉(zhuǎn)Numpy數(shù)組
tensor = tf.constant([[1, 2], [3, 4]])
numpy_array = tensor.numpy()

# Numpy數(shù)組轉(zhuǎn)張量
new_tensor = tf.convert_to_tensor(numpy_array)