### 1. Tensorflow #### （1）安装 ```python import tensorflow as tf ``` ```python print(tf.__version__) ``` 2.3.1 #### （2）创建 ```python # 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) ```python # 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) ```python # 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) ```python # 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) #### （3）运算 ```python 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) ```python print(a + b, "\n") # element-wise addition print(a * b, "\n") # element-wise multiplication print(a @ b, "\n") # matrix multiplication ``` 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) ### 2. 张量 #### （1）形状 ```python rank_4_tensor = tf.zeros([3, 2, 4, 5]) ``` ```python 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: 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 #### （2）索引 ```python 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] ```python 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 ```python 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] ### 3. 变量 #### （1）创建 ```python 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]) ``` ```python print("Shape: ",my_variable.shape) print("DType: ",my_variable.dtype) print("As NumPy: ", my_variable.numpy) ``` Shape: (2, 2) DType: As NumPy: > ```python 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: 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) ```python 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 ```python 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.] ```python # 关闭梯度 step_counter = tf.Variable(1, trainable=False) ``` ### 3. 自动微分 #### （1）函数求值 ```python 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) ``` ```python print(dy_dx.numpy()) ``` 4.7182817 #### （2）即刻执行 ```python import os import tensorflow as tf import cProfile ``` ```python x = [[2.]] m = tf.matmul(x, x) print("hello, {}".format(m)) ``` hello, [[4.]] ```python 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 += 1 ``` ```python fizzbuzz(15) ``` 1 2 Fizz 4 Buzz Fizz 7 8 Fizz Buzz 11 Fizz 13 14 FizzBuzz ```python 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) ```python ```