本片文章的目的是:利用Tensorflow API tf.keras搭建網絡八股(六步法)
六步法:
- 導入相關的模塊,也就是 import
- 加載訓練集和測試集,也就是加載train(x_train數據、y_train標籤)、test(x_test數據、y_test標籤)數據
- 前向傳播(搭建神經網絡結構,逐層描述每層網絡),也就是model = tf.keras.models.Sequential
- 配置訓練時所用的方法(也就是優化器,損失函數,評測指標的選擇),也就是model.compile
- 進行數據的訓練(告訴訓練集和測試集的輸入特徵和標籤,batch的值,以及迭代多少次數據集),也即是model.fit
- 利用summary()函數打印出網絡的結構和參數統計
對上述用到的tf.keras模塊中的函數進行進一步的介紹
- model = tf.keras.models.Sequential([網絡結構]) #描述各層網絡
網絡結構舉例:- 拉直層:tf.keras.layers.Flatten()
- 全連接層:tf.keras.layers.Dense(神經元個數,activation=“激活函數”,kernel_regularizer=“正則化函數”)
activation可選的字符串:“relu”、“softmax”、“sigmoid”、“tanh”
kernel_regularizer可選:tf.keras.regularizers.l1()、tf.keras.regularizers.l2() - 卷積層:tf.keras.layers.Conv2D(filters = 卷積核個數,kernel_size = 卷積核尺寸,strides = 卷積步長,padding = “valid”or“same”)
- LSTM層:tf.keras.layers.LSTM()
- model.compile(optimizer=優化器,loss=損失函數,metrics=[“準確率”])
- Optimizer可選:
i. ‘sgd’ or tf.keras.optimizers.SGD(lr=學習率,momentum=動量參數)
ii. 'adagrad’or tf.keras.optimizers.Adagrad(lr=學習率)
iii. 'adadelta’or tf.keras.optimizers.Adadelta(lr=學習率)
iv. 'adam’or tf.keras.optimizers.Adam(lr=學習率,beta_1=0.9,beta_1=0.999) - loss可選:
i. ‘mse’ or tf.keras.losses.MeanSquaredError()
ii. ‘sparse_categorical_crossentropy’ or tf.keras.losses.SparseCategoricalCrossentropy(from_logots=False) - Metrics可選:
i. ‘accuracy’:y_和y都是數值,如y_=[1] y=[1]
ii. ‘categorical_accuracy’:y_和y都是獨熱碼(概率分佈),如y_=[0,1,0] y=[0.256,0.695,0.048]
iii. ‘sparse_categorical_accuracy’:y_是數值,y是獨熱碼(概率分佈),y_=[1] y=[0.256,0.695,0.048]
- Optimizer可選:
- model.fit(訓練集的輸入特徵,訓練集的標籤,batch_size=,epochs=,validation_data=(測試集的輸入特徵,測試集的標籤),validation_split=從訓練集劃分多少比例給測試集,validation_freq=多少次epoch測試一次)
- model.summary() 打印出網絡的結構和參數統計
案例實戰
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案例1:利用tf.keras實現鳶尾花分類
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()
將上述代碼封裝成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()
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案例2:利用tf.keras實現mnist手寫數字識別
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()
將上述代碼封裝成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()
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