model = keras.models.Sequential([ #卷积层1 keras.layers.Conv2D(32,kernel_size=5,strides=1,padding="same",data_format="channels_last",activation=tf.nn.relu,kernel_regularizer=keras.regularizers.l2(0.01)), #池化层1 keras.layers.MaxPool2D(pool_size=2,strides=2,padding="same"), #卷积层2 keras.layers.Conv2D(64,kernel_size=5,strides=1,padding="same",data_format="channels_last",activation=tf.nn.relu), #池化层2 keras.layers.MaxPool2D(pool_size=2,strides=2,padding="same"), #数据整理 keras.layers.Flatten(), #1024个,全连接层 keras.layers.Dense(1024,activation=tf.nn.relu), #100个,全连接层 keras.layers.Dense(100,activation=tf.nn.softmax) ])
import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' from tensorflow.python.keras.datasets import cifar100 from tensorflow.python import keras import tensorflow as tf class CNNMnist(object): model = keras.models.Sequential([ #卷积层1 keras.layers.Conv2D(32,kernel_size=5,strides=1,padding="same",data_format="channels_last",activation=tf.nn.relu,kernel_regularizer=keras.regularizers.l2(0.01)), #池化层1 keras.layers.MaxPool2D(pool_size=2,strides=2,padding="same"), #卷积层2 keras.layers.Conv2D(64,kernel_size=5,strides=1,padding="same",data_format="channels_last",activation=tf.nn.relu), #池化层2 keras.layers.MaxPool2D(pool_size=2,strides=2,padding="same"), #数据整理 keras.layers.Flatten(), #1024个,全连接层 keras.layers.Dense(1024,activation=tf.nn.relu), #100个,全连接层 keras.layers.Dense(100,activation=tf.nn.softmax) ]) def __init__(self): (self.x_train,self.y_train),(self.x_test,self.y_test) = cifar100.load_data() self.x_train = self.x_train/255.0 self.x_test = self.x_test/255.0 def compile(self): CNNMnist.model.compile(optimizer=keras.optimizers.Adam(),loss=keras.losses.sparse_categorical_crossentropy,metrics=["accuracy"]) def fit(self): CNNMnist.model.fit(self.x_train,self.y_train,epochs=1,batch_size=32) def evaluate(self): test_loss,test_acc = CNNMnist.model.evaluate(self.x_test,self.y_test) print(test_loss,test_acc) if __name__ == '__main__': cnn = CNNMnist() print(CNNMnist.model.summary()) cnn.compile() cnn.fit()
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