Tensorflow2.1基础知识---搭建神经网络八股以及小案例实战

本片文章的目的是:利用Tensorflow API tf.keras搭建网络八股(六步法)

六步法：

1. 导入相关的模块，也就是 import
2. 加载训练集和测试集，也就是加载train（x_train数据、y_train标签）、test（x_test数据、y_test标签）数据
3. 前向传播（搭建神经网络结构，逐层描述每层网络），也就是model = tf.keras.models.Sequential
4. 配置训练时所用的方法（也就是优化器，损失函数，评测指标的选择），也就是model.compile
5. 进行数据的训练（告诉训练集和测试集的输入特征和标签，batch的值，以及迭代多少次数据集），也即是model.fit
6. 利用summary（）函数打印出网络的结构和参数统计

对上述用到的tf.keras模块中的函数进行进一步的介绍

1. model = tf.keras.models.Sequential([网络结构]) #描述各层网络
   网络结构举例：
   1. 拉直层：tf.keras.layers.Flatten()
   2. 全连接层：tf.keras.layers.Dense(神经元个数，activation=“激活函数”，kernel_regularizer=“正则化函数”)
      activation可选的字符串：“relu”、“softmax”、“sigmoid”、“tanh”
      kernel_regularizer可选：tf.keras.regularizers.l1()、tf.keras.regularizers.l2()
   3. 卷积层：tf.keras.layers.Conv2D（filters = 卷积核个数，kernel_size = 卷积核尺寸，strides = 卷积步长，padding = “valid”or“same”）
   4. LSTM层：tf.keras.layers.LSTM()
2. model.compile(optimizer=优化器，loss=损失函数，metrics=[“准确率”])
   1. Optimizer可选：
      i. ‘sgd’ or tf.keras.optimizers.SGD(lr=学习率,momentum=动量参数)
      ii. 'adagrad’or tf.keras.optimizers.Adagrad(lr=学习率)
      iii. 'adadelta’or tf.keras.optimizers.Adadelta(lr=学习率)
      iv. 'adam’or tf.keras.optimizers.Adam(lr=学习率,beta_1=0.9,beta_1=0.999)
   2. loss可选：
      i. ‘mse’ or tf.keras.losses.MeanSquaredError()
      ii. ‘sparse_categorical_crossentropy’ or tf.keras.losses.SparseCategoricalCrossentropy(from_logots=False)
   3. Metrics可选：
      i. ‘accuracy’:y_和y都是数值，如y_=[1] y=[1]
      ii. ‘categorical_accuracy’:y_和y都是独热码（概率分布）,如y_=[0,1,0] y=[0.256,0.695,0.048]
      iii. ‘sparse_categorical_accuracy’:y_是数值，y是独热码（概率分布）,y_=[1] y=[0.256,0.695,0.048]
3. model.fit(训练集的输入特征，训练集的标签，batch_size=，epochs=，validation_data=(测试集的输入特征，测试集的标签)，validation_split=从训练集划分多少比例给测试集，validation_freq=多少次epoch测试一次)
4. model.summary() 打印出网络的结构和参数统计

案例实战

• 案例1：利用tf.keras实现鸢尾花分类

  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()
  

  将上述代码封装成class

  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.dl = Dense(3,activation = 'sigmoid',kernel_regularizer = tf.keras.regularizers.l2())
      def call(self,x):
          y = self.dl(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()
  

• 案例2：利用tf.keras实现mnist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()
  

  将上述代码封装成class

  import tensorflow as tf
  from tensorflow.keras.layers import Dense,Flatten
  from tensorflow.keras import Model
  
  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
  
  class MnistModel(Model):
      def __init__(self):
          super(MnistModel,self).__init__()
          self.flatten = Flatten()
          self.d1 = Dense(128,activation = 'relu')
          self.d2 = Dense(10,activation = 'softmax')
      def call(self,x):
          x = self.flatten(x)
          x = self.d1(x)
          y = self.d2(x)
          return y
      
  model = MnistModel()
  
  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()
  

下面的是笔者的微信公众号，欢迎关注，会持续更新c++、python、tensorflow、机器学习、深度学习等系列文章