有什么做ppt参考的网站,梧州网站优化,ssr网站怎么做,陕西省建设厅证件查询训练集数据 TrainSamples-400.csv#xff0c;含 100 个不同汉字#xff0c;每个汉字 400 个实例#xff0c;每个实例均为 64*64 的二值图像#xff1b; 训练集标注TrainSamples-400.csv#xff0c;为 40000 个 0 到 99 间的整数#xff0c;表示训练集中每个实例所属汉字类…训练集数据 TrainSamples-400.csv含 100 个不同汉字每个汉字 400 个实例每个实例均为 64*64 的二值图像 训练集标注TrainSamples-400.csv为 40000 个 0 到 99 间的整数表示训练集中每个实例所属汉字类别 测试集数据 TestSamples-300.csv为 30000 个实例每个实例格式同训练集。 要求标注测试集输出 Result.csv。
import numpy as np
import pandas as pd
from tensorflow.keras.utils import to_categorical
from tensorflow.keras import models, layersdef train():data pd.read_csv(TrainSamples-400.csv, headerNone)train_image data.to_numpy()data pd.read_csv(TrainLabels-400.csv, headerNone)train_label data.to_numpy()train_label to_categorical(train_label)network models.Sequential()network.add(layers.Input(shape (64, 64, 1)))network.add(layers.Conv2D(64, (5, 5), activation relu))network.add(layers.MaxPooling2D((2, 2)))network.add(layers.Conv2D(96, (3, 3), activation relu))network.add(layers.MaxPooling2D((2, 2)))network.add(layers.Conv2D(48, (3, 3), activation relu))network.add(layers.Flatten())network.add(layers.Dense(768, activation relu))network.add(layers.Dense(100, activation softmax))network.summary()network.compile(optimizer rmsprop, loss categorical_crossentropy, metrics [accuracy])network.fit(train_image.reshape(40000, 64, 64, 1), train_label, epochs 5, batch_size 64, validation_split 0.1, validation_freq 1)network.save(saved_model/my_model)def test():data pd.read_csv(TestSamples-300.csv, header None)test_image data.to_numpy()network models.load_model(saved_model/my_model)network.summary()test_label network.predict(test_image.reshape(30000, 64, 64, 1))test_label np.array([np.argmax(i) for i in test_label])pd.DataFrame(test_label).to_csv(Result.csv, header None, index False)if __name__ __main__:train()test()
