Tensorflow二进制概率分类：要使用哪些损耗功能/优化器和模型？
我正在尝试制作一个可以从这样的数据集训练的TF模型：
| F1 | F2 | ... | F3 ... F30 | R1 |
|---|---|---|---|---|
| 0.5 | 0.66 | ... | 0.11 | 1 |
| 0.3 | 0.7 | ... 0.7 ... | 0.3 | 0 |
...是R1中的1。我看到了一种用于从此笔记本中分类的MNIST图片的模型： https://github.com/luisroque/probabilistic/probabilistic-deep-deep-learning/blob/blob/main/main/main/workshops/workshops/workshops/primer%20on%20on%20probabilistic; 20-％20模型％20 aleatoric％20 unclinety.ipynb ，并试图使其适应我自己的使用。这就是我到目前为止所拥有的：
import tensorflow as tf
import tensorflow_probability as tfp
import pandas as pd
from keras import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop
import tensorflow_datasets as tfds
tfd = tfp.distributions
tfpl = tfp.layers
def load_data(name):
dataset = pd.read_csv(name)
inputShape = len(dataset.columns)-1
x_train = dataset.loc[:, dataset.columns != 'is_fraud']
y_train = dataset['is_fraud'].values
x_test = dataset.loc[:, dataset.columns != 'is_fraud']
y_test = dataset['is_fraud'].values
return x_train, y_train, x_test, y_test, inputShape
def get_probabilistic_model(input_shape, loss, optimizer, metrics):
model = Sequential([
Dense(30, input_dim=input_shape, activation='relu'),
#Dense(50, activation='relu'),
Dense(units=tfpl.OneHotCategorical.params_size(2)),
tfpl.OneHotCategorical(event_size=2,
convert_to_tensor_fn=tfd.Distribution.mode)
])
model.compile(loss = loss,
optimizer=optimizer,
metrics=metrics,
experimental_run_tf_function=False)
return model
def nll(y_true, y_pred):
return -y_pred.log_prob(y_true)
x_train, y_train, x_test, y_test, inputShape = load_data('hashed_txns.csv')
tf.random.set_seed(0)
probabilistic_model = get_probabilistic_model(
input_shape=inputShape,
loss=nll,
optimizer=RMSprop(),
metrics=['accuracy'])
probabilistic_model.fit(x_train, tf.keras.utils.to_categorical(y_train), epochs=5)
print('Accuracy on test set: ',
str(probabilistic_model.evaluate(x_test, tf.keras.utils.to_categorical(y_test), verbose=False)[1]))
但是，因为模型中使用的各种东西可能不是用于二进制概率分类的目的，所以我只获得了50％的精度。我尝试使用二进制crossentropy进行损失功能，就像我对正常的分类代码所做的那样，但是它说的是没有变量的梯度。有任何改进建议吗？如果需要，我使用的数据集在这里：
profileType,state,nearScams,category,is_fraud,acct_num-1,acct_num-2,acct_num-3,acct_num-4,acct_num-5,acct_num-6,acct_num-7,acct_num-8,acct_num-9,acct_num-10,acct_num-11,acct_num-12,acct_num-13,acct_num-14,acct_num-15,acct_num-16,acct_num-17,acct_num-18,acct_num-19,acct_num-20
0.0,0.0,0.14285714285714285,0.0,1.0,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.0,0.14285714285714285,0.07692307692307693,0.0,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.020833333333333332,0.14285714285714285,0.15384615384615385,1.0,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.020833333333333332,0.14285714285714285,0.23076923076923078,0.0,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.04166666666666667,0.14285714285714285,0.3076923076923077,1.0,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.04166666666666667,0.14285714285714285,0.3076923076923077,0.0,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.0625,0.14285714285714285,0.38461538461538464,1.0,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.0625,0.14285714285714285,0.07692307692307693,0.0,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.08333333333333333,0.14285714285714285,0.4615384615384616,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.08333333333333333,0.14285714285714285,0.07692307692307693,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.10416666666666667,0.2857142857142857,0.15384615384615385,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.10416666666666667,0.2857142857142857,0.5384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.12499999999999999,0.14285714285714285,0.6153846153846154,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5
0.0,0.12499999999999999,0.14285714285714285,0.6923076923076923,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5
0.0,0.14583333333333331,0.2857142857142857,0.0,1.0,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.14583333333333331,0.2857142857142857,0.3076923076923077,0.0,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.16666666666666666,0.14285714285714285,0.38461538461538464,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0
0.0,0.16666666666666666,0.14285714285714285,0.23076923076923078,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0
0.0,0.18749999999999997,0.14285714285714285,0.6923076923076923,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.18749999999999997,0.14285714285714285,0.23076923076923078,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.20833333333333331,0.2857142857142857,0.6923076923076923,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5
0.0,0.20833333333333331,0.2857142857142857,0.7692307692307694,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5
0.0,0.22916666666666666,0.2857142857142857,0.38461538461538464,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5
0.0,0.22916666666666666,0.2857142857142857,0.6923076923076923,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5
0.0,0.24999999999999997,0.14285714285714285,0.0,1.0,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.24999999999999997,0.14285714285714285,0.6153846153846154,0.0,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.22916666666666666,0.2857142857142857,0.8461538461538463,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5
0.0,0.22916666666666666,0.2857142857142857,0.15384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5
0.0,0.2708333333333333,0.14285714285714285,0.8461538461538463,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0
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0.0,0.14583333333333331,0.0,0.6923076923076923,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5
0.0,0.2916666666666667,0.5714285714285714,0.8461538461538463,1.0,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.2916666666666667,0.5714285714285714,0.6153846153846154,0.0,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.2708333333333333,0.14285714285714285,0.38461538461538464,1.0,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.2708333333333333,0.14285714285714285,0.38461538461538464,0.0,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.3125,0.14285714285714285,0.4615384615384616,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.3125,0.14285714285714285,0.15384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.0,0.14285714285714285,0.38461538461538464,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5
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0.0,0.24999999999999997,0.14285714285714285,0.23076923076923078,1.0,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
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0.0,0.18749999999999997,0.14285714285714285,0.8461538461538463,1.0,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
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0.0,0.14583333333333331,0.14285714285714285,0.9230769230769231,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5
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0.09090909090909091,0.3125,0.14285714285714285,0.0,0.0,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.020833333333333332,0.14285714285714285,0.6153846153846154,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5
0.09090909090909091,0.020833333333333332,0.14285714285714285,0.15384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5
0.09090909090909091,0.5833333333333333,0.14285714285714285,0.6923076923076923,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.5833333333333333,0.14285714285714285,0.15384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.3333333333333333,0.2857142857142857,0.7692307692307694,1.0,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.3333333333333333,0.2857142857142857,0.0,0.0,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.0625,0.14285714285714285,0.7692307692307694,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.0625,0.14285714285714285,0.38461538461538464,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.49999999999999994,0.14285714285714285,0.15384615384615385,1.0,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.49999999999999994,0.14285714285714285,0.15384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.6041666666666666,0.14285714285714285,0.15384615384615385,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.6041666666666666,0.14285714285714285,0.15384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.10416666666666667,0.14285714285714285,0.6153846153846154,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.10416666666666667,0.14285714285714285,0.38461538461538464,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.020833333333333332,0.2857142857142857,1.0000000000000002,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5
0.09090909090909091,0.020833333333333332,0.2857142857142857,0.8461538461538463,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5
0.09090909090909091,0.5624999999999999,0.14285714285714285,0.6923076923076923,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.5624999999999999,0.14285714285714285,0.5384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.6041666666666666,0.0,0.3076923076923077,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.3541666666666667,0.14285714285714285,0.6153846153846154,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.3541666666666667,0.14285714285714285,0.5384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.09090909090909091,0.22916666666666666,0.42857142857142855,0.0,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5
0.09090909090909091,0.22916666666666666,0.42857142857142855,0.7692307692307694,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5
I am trying to make a tf model that can train from a dataset like this:
| f1 | f2 | ... | f30 | r1 |
|---|---|---|---|---|
| 0.5 | 0.66 | ... | 0.11 | 1 |
| 0.3 | 0.7 | ... | 0.3 | 0 |
and return a percentage of how confident it is that an entry is 1 in r1. I saw a model used for classifying MNIST pictures from this notebook: https://github.com/luisroque/probabilistic-deep-learning/blob/main/workshops/Primer%20on%20Probabilistic%20Deep%20Learning%20-%20Modeling%20Aleatoric%20Uncertainty.ipynb and tried to adapt it for my own use. This is what I have so far:
import tensorflow as tf
import tensorflow_probability as tfp
import pandas as pd
from keras import Sequential
from keras.layers import Dense
from keras.optimizers import RMSprop
import tensorflow_datasets as tfds
tfd = tfp.distributions
tfpl = tfp.layers
def load_data(name):
dataset = pd.read_csv(name)
inputShape = len(dataset.columns)-1
x_train = dataset.loc[:, dataset.columns != 'is_fraud']
y_train = dataset['is_fraud'].values
x_test = dataset.loc[:, dataset.columns != 'is_fraud']
y_test = dataset['is_fraud'].values
return x_train, y_train, x_test, y_test, inputShape
def get_probabilistic_model(input_shape, loss, optimizer, metrics):
model = Sequential([
Dense(30, input_dim=input_shape, activation='relu'),
#Dense(50, activation='relu'),
Dense(units=tfpl.OneHotCategorical.params_size(2)),
tfpl.OneHotCategorical(event_size=2,
convert_to_tensor_fn=tfd.Distribution.mode)
])
model.compile(loss = loss,
optimizer=optimizer,
metrics=metrics,
experimental_run_tf_function=False)
return model
def nll(y_true, y_pred):
return -y_pred.log_prob(y_true)
x_train, y_train, x_test, y_test, inputShape = load_data('hashed_txns.csv')
tf.random.set_seed(0)
probabilistic_model = get_probabilistic_model(
input_shape=inputShape,
loss=nll,
optimizer=RMSprop(),
metrics=['accuracy'])
probabilistic_model.fit(x_train, tf.keras.utils.to_categorical(y_train), epochs=5)
print('Accuracy on test set: ',
str(probabilistic_model.evaluate(x_test, tf.keras.utils.to_categorical(y_test), verbose=False)[1]))
However, because the various things used in the model probably is not used for the purpose of binary probability classification, I only got a 50% accuracy for mine. I've tried to use binary crossentropy for the loss function like I did with my normal classification code, but it was saying something about there not being gradients for variables. Any recommendations for improvement? The dataset I used is here if needed:
profileType,state,nearScams,category,is_fraud,acct_num-1,acct_num-2,acct_num-3,acct_num-4,acct_num-5,acct_num-6,acct_num-7,acct_num-8,acct_num-9,acct_num-10,acct_num-11,acct_num-12,acct_num-13,acct_num-14,acct_num-15,acct_num-16,acct_num-17,acct_num-18,acct_num-19,acct_num-20
0.0,0.0,0.14285714285714285,0.0,1.0,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.0,0.14285714285714285,0.07692307692307693,0.0,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.020833333333333332,0.14285714285714285,0.15384615384615385,1.0,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.020833333333333332,0.14285714285714285,0.23076923076923078,0.0,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.04166666666666667,0.14285714285714285,0.3076923076923077,1.0,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.04166666666666667,0.14285714285714285,0.3076923076923077,0.0,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.0625,0.14285714285714285,0.38461538461538464,1.0,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.0625,0.14285714285714285,0.07692307692307693,0.0,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.08333333333333333,0.14285714285714285,0.4615384615384616,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.08333333333333333,0.14285714285714285,0.07692307692307693,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.10416666666666667,0.2857142857142857,0.15384615384615385,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.10416666666666667,0.2857142857142857,0.5384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.12499999999999999,0.14285714285714285,0.6153846153846154,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5
0.0,0.12499999999999999,0.14285714285714285,0.6923076923076923,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5
0.0,0.14583333333333331,0.2857142857142857,0.0,1.0,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.14583333333333331,0.2857142857142857,0.3076923076923077,0.0,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.16666666666666666,0.14285714285714285,0.38461538461538464,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0
0.0,0.16666666666666666,0.14285714285714285,0.23076923076923078,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0
0.0,0.18749999999999997,0.14285714285714285,0.6923076923076923,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.18749999999999997,0.14285714285714285,0.23076923076923078,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.20833333333333331,0.2857142857142857,0.6923076923076923,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5
0.0,0.20833333333333331,0.2857142857142857,0.7692307692307694,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5
0.0,0.22916666666666666,0.2857142857142857,0.38461538461538464,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5
0.0,0.22916666666666666,0.2857142857142857,0.6923076923076923,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5
0.0,0.24999999999999997,0.14285714285714285,0.0,1.0,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.24999999999999997,0.14285714285714285,0.6153846153846154,0.0,0.5,0.5,0.5,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
0.0,0.22916666666666666,0.2857142857142857,0.8461538461538463,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5
0.0,0.22916666666666666,0.2857142857142857,0.15384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5
0.0,0.2708333333333333,0.14285714285714285,0.8461538461538463,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0
0.0,0.2708333333333333,0.14285714285714285,0.3076923076923077,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.0
0.0,0.14583333333333331,0.0,0.6923076923076923,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5
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0.09090909090909091,0.3541666666666667,0.14285714285714285,0.5384615384615385,0.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5
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好吧，嗯，刚才证明我很愚蠢，忘了您需要超过5个以上的基于基于图像的培训的时期，将其提高到300，并且它的起作用仍然会感谢任何改进的反馈。
Ok uh, just turned out I was dumb and forgot that you need way more than 5 epoch for non image based training, upped it to 300 and it works, would still appreciate any feedback for improvement tho.