Python中的MXNet分类总是给出相同的预测

我在这里尝试的是，通过使用网球比赛统计数据集作为输入，我建立了一个神经网络模型，预测比赛结果（1或0）

通过遵循mxnet官方文件，我开发了以下程序。 我尝试了各种配置参数，如批次大小、单位大小、动作类型、学习率，但无论我能想到什么样的修改，我得到的精度始终在0.5左右，并且总是预测全部1或0

import numpy as np
from sklearn.preprocessing import normalize
import mxnet as mx
import logging
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)

logging.basicConfig(level=logging.DEBUG, format='%(asctime)-15s %(message)s')

batch_size = 100
train_data = np.loadtxt("dm.csv",delimiter=",")
train_data = normalize(train_data, norm='l1', axis=0)
train_lbl = np.loadtxt("dm_lbl.csv",delimiter=",")
eval_data = np.loadtxt("dw.csv",delimiter=",")
eval_data = normalize(eval_data, norm='l1', axis=0)
eval_lbl = np.loadtxt("dw_lbl.csv",delimiter=",")


train_iter = mx.io.NDArrayIter(train_data, train_lbl, batch_size=batch_size, shuffle=True)
val_iter = mx.io.NDArrayIter(eval_data, eval_lbl, batch_size=batch_size)


data = mx.sym.var('data')
# The first fully-connected layer and the corresponding activation function
fc1  = mx.sym.FullyConnected(data=data, num_hidden=220)
#bn1 = mx.sym.BatchNorm(data = fc1, name="bn1")
act1 = mx.sym.Activation(data=fc1, act_type="sigmoid")

# The second fully-connected layer and the corresponding activation function
fc2  = mx.sym.FullyConnected(data=act1, num_hidden = 220)
#bn2 = mx.sym.BatchNorm(data = fc2, name="bn2")
act2 = mx.sym.Activation(data=fc2, act_type="sigmoid")

# The third fully-connected layer and the corresponding activation function
fc3  = mx.sym.FullyConnected(data=act2, num_hidden = 110)
#bn3 = mx.sym.BatchNorm(data = fc3, name="bn3")
act3 = mx.sym.Activation(data=fc3, act_type="sigmoid")

# output class(es)
fc4  = mx.sym.FullyConnected(data=act3, num_hidden=2)
# Softmax with cross entropy loss
mlp  = mx.sym.SoftmaxOutput(data=fc4, name='softmax')

mod = mx.mod.Module(symbol=mlp,
                    context=mx.cpu(),
                    data_names=['data'],
                    label_names=['softmax_label'])
mod.fit(train_iter,
        eval_data=val_iter,
        optimizer='sgd',
        optimizer_params={'learning_rate':0.03},
        eval_metric='rmse',
        num_epoch=10,
        batch_end_callback = mx.callback.Speedometer(batch_size, 100)) # output progress for each 200 data batches)

prob = mod.predict(val_iter).asnumpy()
#print(prob)

for unit in prob:
    print 'Classified as %d with probability %f' % (unit.argmax(), max(unit))

以下是日志输出：

2017-06-19 17:18:34,961 Epoch[0] Train-rmse=0.500574
2017-06-19 17:18:34,961 Epoch[0] Time cost=0.007
2017-06-19 17:18:34,968 Epoch[0] Validation-rmse=0.500284
2017-06-19 17:18:34,975 Epoch[1] Train-rmse=0.500703
2017-06-19 17:18:34,975 Epoch[1] Time cost=0.007
2017-06-19 17:18:34,982 Epoch[1] Validation-rmse=0.500301
2017-06-19 17:18:34,990 Epoch[2] Train-rmse=0.500713
2017-06-19 17:18:34,990 Epoch[2] Time cost=0.008
2017-06-19 17:18:34,998 Epoch[2] Validation-rmse=0.500302
2017-06-19 17:18:35,005 Epoch[3] Train-rmse=0.500713
2017-06-19 17:18:35,005 Epoch[3] Time cost=0.007
2017-06-19 17:18:35,012 Epoch[3] Validation-rmse=0.500302
2017-06-19 17:18:35,019 Epoch[4] Train-rmse=0.500713
2017-06-19 17:18:35,019 Epoch[4] Time cost=0.007
2017-06-19 17:18:35,027 Epoch[4] Validation-rmse=0.500302
2017-06-19 17:18:35,035 Epoch[5] Train-rmse=0.500713
2017-06-19 17:18:35,035 Epoch[5] Time cost=0.008
2017-06-19 17:18:35,042 Epoch[5] Validation-rmse=0.500302
2017-06-19 17:18:35,049 Epoch[6] Train-rmse=0.500713
2017-06-19 17:18:35,049 Epoch[6] Time cost=0.007
2017-06-19 17:18:35,056 Epoch[6] Validation-rmse=0.500302
2017-06-19 17:18:35,064 Epoch[7] Train-rmse=0.500712
2017-06-19 17:18:35,064 Epoch[7] Time cost=0.008
2017-06-19 17:18:35,071 Epoch[7] Validation-rmse=0.500302
2017-06-19 17:18:35,079 Epoch[8] Train-rmse=0.500712
2017-06-19 17:18:35,079 Epoch[8] Time cost=0.007
2017-06-19 17:18:35,085 Epoch[8] Validation-rmse=0.500301
2017-06-19 17:18:35,093 Epoch[9] Train-rmse=0.500712
2017-06-19 17:18:35,093 Epoch[9] Time cost=0.007
2017-06-19 17:18:35,099 Epoch[9] Validation-rmse=0.500301
Classified as 0 with probability 0.530638
Classified as 0 with probability 0.530638
Classified as 0 with probability 0.530638
.
.
.
Classified as 0 with probability 0.530638

有人能告诉我哪里错了吗

python version == 2.7.10
mxnet == 0.10.0
numpy==1.12.0

我从数据集中删除了一些非信息列和标题，然后将其转换为csv格式

train_data.shape == (491, 22)
train_lbl.shape == (491,)
eval_data.shape == (452, 22)
eval_lbl.shape == (452,) 

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网络的定义似乎是正确的。你能打印火车和火车的数据，看看在标准化后数据是否仍然是你所期望的吗？另外，您从原始数据中删除了哪些列？

我上传了此程序中使用的数据集。非常感谢您在@Naveen发表的评论。火车和火车看起来不错，正态分布。但无论是否使用规范化代码，它们都会产生很差的结果：没有准确性/rmse改进。我从原始数据中删除了很多列，比如球员姓名，但是你能看看我上传的文件吗？这个链接有我在这个程序中使用的确切数据。