本文共 1281 字，大约阅读时间需要 4 分钟。

通过对点云进行，训练神经网络分类器，对点云进行分类。ESF特征提取见上述链接。

import tensorflow as tfimport numpy as npdataset =tf.data.Dataseta3 = np.loadtxt('')train1 = np.array(a3)label1 = np.loadtxt('')label1 = tf.one_hot(label1,depth=3)a4 = np.loadtxt('')text = np.array(a4)label_text = np.loadtxt('')label_text = tf.one_hot(label_text,depth=3)input_data = tf.keras.Input(shape=(640,),name='input_data')##out=tf.keras.layers.Flatten()(input_data)##out=tf.keras.layers.Dense(512,activation='relu',name='dense_1')(input_data)##ou=tf.keras.layers.Dense(256,activation='relu',name='dense_2')(out)out1=tf.keras.layers.Dense(128,activation='relu',name='dense_3')(input_data)out2=tf.keras.layers.Dense(128,activation='relu',name='dense_4')(out1)out3=tf.keras.layers.Dense(32,activation='relu',name='dense_5')(out2)pre=tf.keras.layers.Dense(3,activation='softmax',name='predictions')(out3)model=tf.keras.Model(inputs=input_data,outputs=pre)print(model.summary())model.compile(optimizer=tf.keras.optimizers.Adam(1e-4),loss='categorical_crossentropy',metrics=['accuracy'])tensorboard = tf.keras.callbacks.TensorBoard(histogram_freq=1)model.fit(x=train1,y=label1,epochs=500,batch_size=1,verbose=2,callbacks=[tensorboard])model.evaluate(x=text,y=label_text)y_pred = model.predict(text,batch_size = 1)

转载地址：http://exxzi.baihongyu.com/