MXNet中自定義eval_metric的可能錯誤使用

我正在解決一個問題，並試圖使用MXNet解決問題。我試圖在代碼中使用自定義指標。爲同一代碼是：MXNet中自定義eval_metric的可能錯誤使用

def calculate_sales_from_bucket(bucketArray): 
    return numpy.asarray(numpy.power(10, calculate_max_index_from_bucket(bucketArray))) 

def calculate_max_index_from_bucket(bucketArray): 
    answerArray = [] 
    for bucketValue in bucketArray: 
     index, value = max(enumerate(bucketValue), key=operator.itemgetter(1)) 
     answerArray.append(index) 
    return answerArray 


def custom_metric(label, bucketArray): 
    return numpy.mean(numpy.power(calculate_sales_from_bucket(label)-calculate_sales_from_bucket(bucketArray),2)) 

model.fit(
    train_iter,   # training data 
    eval_data=val_iter, # validation data 
    batch_end_callback = mx.callback.Speedometer(batch_size, 1000), # output progress for each 1000 data batches 
    num_epoch = 10,  # number of data passes for training 
    optimizer = 'adam', 
    eval_metric = mx.metric.create(custom_metric), 
    optimizer_params=(('learning_rate', 1),) 
)

我得到的輸出：

INFO:root:Epoch[0] Validation-custom_metric=38263835679935.953125 
INFO:root:Epoch[1] Batch [1000]  Speed: 91353.72 samples/sec  Train-custom_metric=39460550891.057487 
INFO:root:Epoch[1] Batch [2000]  Speed: 96233.05 samples/sec Train-custom_metric=9483.127650 
INFO:root:Epoch[1] Batch [3000] Speed: 90828.09 samples/sec Train-custom_metric=57538.891485 
INFO:root:Epoch[1] Batch [4000] Speed: 93025.54 samples/sec Train-custom_metric=59861.927745 
INFO:root:Epoch[1] Train-custom_metric=8351.460495 
INFO:root:Epoch[1] Time cost=9.466 
INFO:root:Epoch[1] Validation-custom_metric=38268.250469 
INFO:root:Epoch[2] Batch [1000]  Speed: 94028.96 samples/sec  Train-custom_metric=58864.659051 
INFO:root:Epoch[2] Batch [2000]  Speed: 94562.38 samples/sec  Train-custom_metric=9482.873310 
INFO:root:Epoch[2] Batch [3000]  Speed: 93198.68 samples/sec  Train-custom_metric=57538.891485 
INFO:root:Epoch[2] Batch [4000]  Speed: 93722.89 samples/sec  Train-custom_metric=59861.927745 
INFO:root:Epoch[2] Train-custom_metric=8351.460495 
INFO:root:Epoch[2] Time cost=9.341 
INFO:root:Epoch[2] Validation-custom_metric=38268.250469

在這種情況下，不論列車custom_metric的批次變化，列車custom_metric仍一樣。就像在第一個時期和第二個時期的批量1000的情況一樣。

我相信這是一個問題，因爲Train-custom_metric和Validation-custom_metric不會改變，而與時代步驟的值無關。我是MXNet的初學者，在這個假設中我可能是錯的。

你能否確認我是否以正確的方式傳遞了eval_metric？

來源

2017-08-09 Abhinav Aggarwal