如何评估使用样本序列分类cod训练的CNTK模型

Question

我使用https://github.com/Microsoft/CNTK/blob/v2.0/Examples/SequenceClassification/SimpleExample/Python/SequenceClassification.py中给出的代码来训练模型。我如何评价它？在

Answer 1

如果要用Python计算模型，请参见here。如果你想用其他语言，比如C++ +C，你可以在Model Evalaution页面中找到细节。在

谢谢

Answer 2

我是通过以下方式得到的：

import cntk as C
from cntk.ops.functions import load_model # Note this
...
...
# saved the model after epochs
for i in range(500):
    mb = reader.next_minibatch(minibatch_size, input_map=input_map)
    trainer.train_minibatch(mb)
    classifier_output.save("model.dnn") # Note this
...
...
# loading the model
model = load_model("model.dnn") # Note this

# converted sentence to numbers and given as sequence
predScores = model(C.Value.one_hot([[1,238,4,4990,7223,1357,2]], 50466)) # Note this
predClass = np.argmax(predScores)
print(predClass)

其中[[1,238,4,4990,7223,1357,2]]是词汇索引的序列（基本上是训练所依据的序列，50466是词汇的大小。在

Answer 3

不太可能，当你在CNTK中训练你的模型时，你不需要使用create_reader/Minibatch工具。主要是因为测试/生产文件通常非常小。模型评估实际上非常简单：

import cntk as C
import pandas as pd
import numpy as np

model = C.load_model(path_to_where_the_model_is_saved) # load your CNTK model

ds = pd.read_csv(filename, delimiter=",") # load your data of course
                                          # we are assuming all data come 
                                          # together in a single matrix

X = ds.values[:,0:28].astype('float32') # ensures the right type for CNTK
Y = ds.values[:,28].astype('float32')   # last column is the label

X= X / 255 # perform any necessary transformation if any

pred = model(X) # evaluate your test data

pred[pred > 0.5]=1
pred[pred!=1]=0
maxa=np.mean(Y==pred)

print("Accuracy {} ".format(maxa*100.0))