Tensorflow学习报告

import tensorflow as tf
print(tf.__version__)
a = tf.constant(2.0)
print(a)

 

 

#声明一个标量常量
t_1 = tf.constant(2)
t_2 = tf.constant(2)
#常量相加
t_add = tf.add(t_1,t_2)
#一个形如一行三列的常量向量可以用如下代码声明
t_3 = tf.constant([4,3,2])
#定义一个形状为[M,N]的全0张量和全1张量
zeros = tf.zeros(shape=[3,3])
ones = tf.ones(shape=[3,3])

#直接赋值初始化
import tensorflow as tf
#直接给变量赋值初始化
bias1 = tf.Variable(2)
#通过initial_value显示的赋值初始化
bias2 = tf.Variable(initial_value=3.)

#使用初始化函数初始化
a=tf.Variable(tf.zeros([2,1]))   #将形状为[2,1]张量初始化为0
b=tf.Variable(tf.zeros_like(a))  #返回一个和给定tensor同样shape的tensor，其中的元素全部置0
c=tf.Variable(tf.ones([2,1]))    #初始化为1
d=tf.Variable(tf.ones_like(a))   #将与a一个形状的张量初始化为1
e=tf.fill([2,3],4)                  #将指定形状的张量初始化为指定数值

import tensorflow as tf
a=tf.constant([[1.0,2.0],[3.0,4.0]])
print(a.shape)
print(a.dtype)
print(a.numpy())

 

 Tensorflow的基础运算操作

import tensorflow as tf
print(tf.add(1,2))                          #0维张量相加
print(tf.add([1,2],[3,4]))                 #一维张量相加
print(tf.matmul([[1,2,3]],[[4],[5],[6]]))  #矩阵相乘
print(tf.square(5))                        #计算5的平方
print(tf.pow(2,3))                         #计算2的3次方
print(tf.square(2)+tf.square(3))           #也支持操作符重载
print(tf.reduce_sum([1,2,3]))              #计算数值的和
print(tf.reduce_mean([1,2,3]))             #计算均值

 

 

模型搭建时常用的Tensor操作
(1)取最大索引:tf.argmax

 

 (2)扩张维度:tf.expand_dims

 

 (3)张量拼接:tf.concat

x=[[1,2,3],[4,5,61],[7,8,9]]
y=[[2,3,4],[5,6,7],[8,9,10]]
z1=tf.concat([x,y],axis=0)   #按照维度0拼接
z2=tf.concat([x,y],axis=1)   #按照维度1拼接
print(z1,z2)

 

 (4)形状变换:tf.reshape

 课后作业

import tensorflow as tf
from tensorflow import keras

import numpy as np
import matplotlib.pyplot as plt

fashion_mnist = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()


class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images.shape
len(train_labels)
train_labels
test_images.shape
len(test_labels)

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
train_images = train_images / 255.0

test_images = test_images / 255.0

plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10)
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10)

 

 

 

 

#模型对于全部 10 个类的预测
def plot_image(i, predictions_array, true_label, img):
  predictions_array, true_label, img = predictions_array, true_label[i], img[i]
  plt.grid(False)
  plt.xticks([])
  plt.yticks([])

  plt.imshow(img, cmap=plt.cm.binary)

  predicted_label = np.argmax(predictions_array)
  if predicted_label == true_label:
    color = 'blue'
  else:
    color = 'red'

  plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
                                100*np.max(predictions_array),
                                class_names[true_label]),
                                color=color)

def plot_value_array(i, predictions_array, true_label):
  predictions_array, true_label = predictions_array, true_label[i]
  plt.grid(False)
  plt.xticks(range(10))
  plt.yticks([])
  thisplot = plt.bar(range(10), predictions_array, color="#777777")
  plt.ylim([0, 1])
  predicted_label = np.argmax(predictions_array)

  thisplot[predicted_label].set_color('red')
  thisplot[true_label].set_color('blue')
  
i = 0
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i],  test_labels)
plt.show()

i = 12
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i],  test_labels)
plt.show()

 

标签：plt,报告,predictions,label,学习,print,tf,Tensorflow,array
来源： https://www.cnblogs.com/pcr-2020310143107/p/16191338.html