tensorflow学习笔记

文章目录

• • 一、用Tensorflow API: tf.keras搭建网络八股
  • • 1. Sequential
    • 2. compile
    • 3. fit
    • 4. summary
    • 5. 使用keras中的sequential重现鸢尾花分类
    • 6. 使用class搭建神经网络
    • • 6.1 用class实现鸢尾花分类
  • 二、手写数字识别

一、用Tensorflow API: tf.keras搭建网络八股

1. Sequential

描述各种网络，例如：

2. compile

3. fit

4. summary

5. 使用keras中的sequential重现鸢尾花分类

代码：

# author:qilin02811
# -*- coding = utf-8 -*-
# @Time:2022/4/27 23:02
import tensorflow as tf
from sklearn import datasets
import numpy as np

x_train = datasets.load_iris().data
y_train = datasets.load_iris().target

np.random.seed(116)
np.random.shuffle(x_train)
np.random.seed(116)
np.random.shuffle(y_train)
tf.random.set_seed(116)

model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(3,activation='softmax',kernel_regularizer=tf.keras.regularizers.l2())
])

model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.1),
    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
    metrics=['sparse_categorical_accuracy'])

model.fit(x_train,y_train,batch_size=32,epochs = 500,validation_split = 0.2,validation_freq=20)

model.summary()

model.summary():可以显示网络结构：

6. 使用class搭建神经网络

因为sequential只能搭建数据从前往后传递的神经网络，如果前面的输入对应后面不同层的输入，则需要使用class搭建神经网络。

6.1 用class实现鸢尾花分类

代码：

# author:qilin02811
# -*- coding = utf-8 -*-
# @Time:2022/4/27 23:02
import tensorflow as tf
from tensorflow.keras.layers import Dense
from tensorflow.keras import Model
from sklearn import datasets
import numpy as np

x_train = datasets.load_iris().data
y_train = datasets.load_iris().target

np.random.seed(116)
np.random.shuffle(x_train)
np.random.seed(116)
np.random.shuffle(y_train)
tf.random.set_seed(116)

class IrisModel(Model):
    def __init__(self):
        super(IrisModel,self).__init__()
        self.d1 = Dense(3,activation='sigmoid',kernel_regularizer=tf.keras.regularizers.l2())

    def call(self,x):
        y = self.d1(x)
        return y

model = IrisModel()

model.compile(optimizer=tf.keras.optimizers.SGD(lr=0.1),
    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
    metrics=['sparse_categorical_accuracy'])

model.fit(x_train,y_train,batch_size=32,epochs = 500,validation_split = 0.2,validation_freq=20)

model.summary()

结果：

二、手写数字识别

import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

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

model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1)
model.summary()