import tensorflow as tfprint(tf.__version__)2.3.1
# This will be an int32 tensor by default; see "dtypes" below.
rank_0_tensor = tf.constant(4)
print(rank_0_tensor)tf.Tensor(4, shape=(), dtype=int32)
# Let's make this a float tensor.
rank_1_tensor = tf.constant([2.0, 3.0, 4.0])
print(rank_1_tensor)tf.Tensor([2. 3. 4.], shape=(3,), dtype=float32)
# If we want to be specific, we can set the dtype (see below) at creation time
rank_2_tensor = tf.constant([[1, 2],
[3, 4],
[5, 6]], dtype=tf.float16)
print(rank_2_tensor)tf.Tensor(
[[1. 2.]
[3. 4.]
[5. 6.]], shape=(3, 2), dtype=float16)
# There can be an arbitrary number of
# axes (sometimes called "dimensions")
rank_3_tensor = tf.constant([
[[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]],
[[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]],
[[20, 21, 22, 23, 24],
[25, 26, 27, 28, 29]],])
print(rank_3_tensor)tf.Tensor(
[[[ 0 1 2 3 4]
[ 5 6 7 8 9]]
[[10 11 12 13 14]
[15 16 17 18 19]]
[[20 21 22 23 24]
[25 26 27 28 29]]], shape=(3, 2, 5), dtype=int32)
a = tf.constant([[1, 2],
[3, 4]])
b = tf.constant([[1, 1],
[1, 1]]) # Could have also said `tf.ones([2,2])`
print(tf.add(a, b), "\n")
print(tf.multiply(a, b), "\n")
print(tf.matmul(a, b), "\n")tf.Tensor(
[[2 3]
[4 5]], shape=(2, 2), dtype=int32)
tf.Tensor(
[[1 2]
[3 4]], shape=(2, 2), dtype=int32)
tf.Tensor(
[[3 3]
[7 7]], shape=(2, 2), dtype=int32)
print(a + b, "\n") # element-wise addition
print(a * b, "\n") # element-wise multiplication
print(a @ b, "\n") # matrix multiplicationtf.Tensor(
[[2 3]
[4 5]], shape=(2, 2), dtype=int32)
tf.Tensor(
[[1 2]
[3 4]], shape=(2, 2), dtype=int32)
tf.Tensor(
[[3 3]
[7 7]], shape=(2, 2), dtype=int32)
rank_4_tensor = tf.zeros([3, 2, 4, 5])print("Type of every element:", rank_4_tensor.dtype)
print("Number of dimensions:", rank_4_tensor.ndim)
print("Shape of tensor:", rank_4_tensor.shape)
print("Elements along axis 0 of tensor:", rank_4_tensor.shape[0])
print("Elements along the last axis of tensor:", rank_4_tensor.shape[-1])
print("Total number of elements (3*2*4*5): ", tf.size(rank_4_tensor).numpy())Type of every element: <dtype: 'float32'>
Number of dimensions: 4
Shape of tensor: (3, 2, 4, 5)
Elements along axis 0 of tensor: 3
Elements along the last axis of tensor: 5
Total number of elements (3*2*4*5): 120
rank_1_tensor = tf.constant([0, 1, 1, 2, 3, 5, 8, 13, 21, 34])
print(rank_1_tensor.numpy())[ 0 1 1 2 3 5 8 13 21 34]
print("First:", rank_1_tensor[0].numpy())
print("Second:", rank_1_tensor[1].numpy())
print("Last:", rank_1_tensor[-1].numpy())First: 0
Second: 1
Last: 34
print("Everything:", rank_1_tensor[:].numpy())
print("Before 4:", rank_1_tensor[:4].numpy())
print("From 4 to the end:", rank_1_tensor[4:].numpy())
print("From 2, before 7:", rank_1_tensor[2:7].numpy()) # 区间左闭右开
print("Every other item:", rank_1_tensor[::2].numpy())
print("Reversed:", rank_1_tensor[::-1].numpy())Everything: [ 0 1 1 2 3 5 8 13 21 34]
Before 4: [0 1 1 2]
From 4 to the end: [ 3 5 8 13 21 34]
From 2, before 7: [1 2 3 5 8]
Every other item: [ 0 1 3 8 21]
Reversed: [34 21 13 8 5 3 2 1 1 0]
my_tensor = tf.constant([[1.0, 2.0], [3.0, 4.0]])
my_variable = tf.Variable(my_tensor) # 需给定初始值才能创建变量
# Variables can be all kinds of types, just like tensors，数据类型不限
bool_variable = tf.Variable([False, False, False, True])
complex_variable = tf.Variable([5 + 4j, 6 + 1j])print("Shape: ",my_variable.shape)
print("DType: ",my_variable.dtype)
print("As NumPy: ", my_variable.numpy)Shape: (2, 2)
DType: <dtype: 'float32'>
As NumPy: <bound method BaseResourceVariable.numpy of <tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy=
array([[1., 2.],
[3., 4.]], dtype=float32)>>
print("A variable:",my_variable)
print("\nViewed as a tensor:", tf.convert_to_tensor(my_variable))
print("\nIndex of highest value:", tf.argmax(my_variable))
# This creates a new tensor; it does not reshape the variable.
print("\nCopying and reshaping: ", tf.reshape(my_variable, ([1,4])))A variable: <tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy=
array([[1., 2.],
[3., 4.]], dtype=float32)>
Viewed as a tensor: tf.Tensor(
[[1. 2.]
[3. 4.]], shape=(2, 2), dtype=float32)
Index of highest value: tf.Tensor([1 1], shape=(2,), dtype=int64)
Copying and reshaping: tf.Tensor([[1. 2. 3. 4.]], shape=(1, 4), dtype=float32)
print("\nIndex of highest value:", tf.argmax(my_variable, 0))
print("\nIndex of highest value:", tf.argmax(my_variable, 1))Index of highest value: tf.Tensor([1 1], shape=(2,), dtype=int64)
Index of highest value: tf.Tensor([1 1], shape=(2,), dtype=int64)
变量一旦创建形状无法更改，因此可重新创建
a = tf.Variable([2.0, 3.0])
# Create b based on the value of a
b = tf.Variable(a)
a.assign([5, 6])
# a and b are different
print(a.numpy())
print(b.numpy())
# There are other versions of assign
print(a.assign_add([2,3]).numpy()) # [7. 9.]
print(a.assign_sub([7,9]).numpy()) # [0. 0.][5. 6.]
[2. 3.]
[7. 9.]
[0. 0.]
# 关闭梯度
step_counter = tf.Variable(1, trainable=False)x = tf.constant(1.0)
with tf.GradientTape(persistent=True) as t:
t.watch(x)
y = tf.multiply(x, x) + tf.exp(x)
dy_dx = t.gradient(y, x) print(dy_dx.numpy())4.7182817
import os
import tensorflow as tf
import cProfilex = [[2.]]
m = tf.matmul(x, x)
print("hello, {}".format(m))hello, [[4.]]
def fizzbuzz(max_num):
counter = tf.constant(0)
max_num = tf.convert_to_tensor(max_num)
for num in range(1, max_num.numpy()+1):
num = tf.constant(num)
if int(num % 3) == 0 and int(num % 5) == 0:
print('FizzBuzz')
elif int(num % 3) == 0:
print('Fizz')
elif int(num % 5) == 0:
print('Buzz')
else:
print(num.numpy())
counter += 1fizzbuzz(15)1
2
Fizz
4
Buzz
Fizz
7
8
Fizz
Buzz
11
Fizz
13
14
FizzBuzz
w = tf.Variable([[1.0]])
with tf.GradientTape() as tape:
loss = w * w
grad = tape.gradient(loss, w)
print(grad) # => tf.Tensor([[ 2.]], shape=(1, 1), dtype=float32)tf.Tensor([[2.]], shape=(1, 1), dtype=float32)