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):
pile(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())
pile()
cnn.fit()
