这个分类问题显然很简单，我不知道为什么它不起作用，也许我在做一个概念错误。我正在尝试制作一个预测器，它将时钟上的分钟分类0为无请求或1该时间瞬间的请求。到目前为止，该模型似乎无法学会0在所有请求发生之前给出一切或置零。到目前为止，我得到的最好的情况是：
我一直在尝试实现我学到的与神经网络相关的所有东西，我的代码以下列方式工作：
- 创建时钟。
- 创建一些请求（我使它们持续更长时间，因此数据集不那么稀疏）。
- 缩放（MinMax）自变量（时钟）。
- 为稀疏数据集创建一个自定义损失函数（我已经使用常规 logits 进行了尝试，但它只能对几乎所有内容进行归零；所以我惩罚了在请求瞬间犯下的错误）。
- 创建由 3 个堆叠的 LSTM 层组成的模型（已尝试过常规 NN 和 LSTM）。
其他也尝试过的技术是 Dropout、Gradient Clipping（用于爆炸梯度）和 ADAM 的极小学习率（大约）。基本上，我只能达到 73.33% 左右的准确率。
下面的代码确实比我想的要长，但我希望它也很容易阅读。谢谢你试图帮助我。
# Data Pre-processing
## The Clock
clock = []
for i in range(0,24):
for j in range(0,60):
if i < 10:
if j < 10:
clock.append('0' + str(i) + '0' + str(j))
else:
clock.append('0' + str(i) + str(j))
else:
if j < 10:
clock.append(str(i) + '0' + str(j))
else:
clock.append(str(i) + str(j))
## Requests
levels = 2
request_full = [0]*len(clock)
request_1300 = []
request_1500 = []
request_1800 = []
request_2100 = []
request_2300 = []
for i in range(0,60):
if i < 10:
request_1300.append(['13' + '0' + str(i), 1])
request_1500.append(['15' + '0' + str(i), 1])
request_1800.append(['18' + '0' + str(i), 1])
request_2100.append(['21' + '0' + str(i), 1])
request_2300.append(['23' + '0' + str(i), 1])
else:
request_1300.append(['13' + str(i), 1])
request_1500.append(['15' + str(i), 1])
request_1800.append(['18' + str(i), 1])
request_2100.append(['21' + str(i), 1])
request_2300.append(['23' + str(i), 1])
request_list = [i for i in
request_1300 +
request_1500 +
request_1800 +
request_2100 +
request_2300]
for i,j in request_list:
idx = clock.index(i)
request_full[idx] = j
import numpy as np
import pandas as pd
df = pd.DataFrame({'clock': clock, 'requests': request_full})
df.to_csv('test1.csv', sep = ',')
clock = df['clock'].values
clock = np.reshape(clock, (len(clock), 1))
requests = df['requests'].values
requests = np.reshape(requests, (len(requests), 1))
## Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(clock)
X_train = []
y_train = []
for i in range(60, len(clock)):
X_train.append(training_set_scaled[i-60:i, 0])
y_train.append(requests[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras import optimizers
import tensorflow as tf
def sparse_penalty_logits(y_true, y_pred):
penalty = 10
loss = tf.where(tf.greater(y_true, 0),
penalty*tf.nn.sigmoid_cross_entropy_with_logits(logits = y_pred,
labels = y_true),
tf.nn.sigmoid_cross_entropy_with_logits(logits = y_pred,
labels = y_true))
return loss
# Initialising the RNN
classifier = Sequential()
# Adding the first LSTM layer and some Dropout regularisation
classifier.add(LSTM(units = 60,
return_sequences = True,
input_shape = (X_train.shape[1], 1)))
classifier.add(Dropout(0.2))
# 2nd Layer
classifier.add(LSTM(units = 60,
return_sequences = True))
#classifier.add(Dropout(0.2))
# 3rd Layer
classifier.add(LSTM(units = 60,
return_sequences = False))
#classifier.add(Dropout(0.2))
# Adding the output layer
classifier.add(Dense(units = 1,
activation = 'sigmoid'))
# Compiling the RNN
adam = optimizers.Adam(lr = 10**(-5),
clipnorm = 1,
clipvalue = 0.5)
classifier.compile(optimizer = adam,
loss = sparse_penalty_logits,
metrics = ['binary_accuracy'])
# Fitting the RNN to the Training set
classifier.fit(X_train,
y_train,
epochs = 100)
# Making the predictions with the dataset
y_pred = classifier.predict(X_train)
y_pred_bin = (y_pred > 0.5)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_train, y_pred_bin)
# Plotting
import matplotlib.pyplot as plt
plt.plot(y_pred, color = 'green', label = 'model')
plt.plot(y_pred_bin, color = 'red', label = 'model binary')
plt.plot(y_train, color = 'blue', label = 'training')
plt.legend()
plt.show()
plt.close()