【发布时间】：2020-02-27 19:45:23
【问题描述】：
当我尝试将 RNN 模型用于我的数据集时，我遇到了这个错误。这是我的模型和数据集的精简版本，但它产生完全相同的错误 - dropbox.com/sh/1a2guzsk4afp94r/AACkOQ1UibLZAhzGoyZJtVcqa?dl=0
训练数据由每 1000 个浮点数长的浮点数列表组成。例如，
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0.55 0.2 0.5 0.85 0.8 0.2
0.2 1. 0.2 0.2 0.5 0.75 0.3 0.5 0.5 0.3 0.85 0.65 0.15 0.4
0.3 0.6 0.05 0.15 0.85 0.5 0.45 0.45 0.05 0.25 0.5 0.45 0.05 0.1
0.45 0.9 0.35 0.35 0.65 0.15 0.45 0.3 0.3 0.6 0.2 0.2 0.05 0.55
0.45 0.75 0.5 0.5 1. 0.5 1. 0.6 0.45 0.05 0.45 0.4 0.05 0.05
0.8 0.05 0.85 0.85 0.8 0.7 0.9 0.65 0.2 1. 0.3 0.85 0.8 0.7
0.95 0.2 0.7 0.95 0.95 0.2 0.2 0.25 0.6 0.7 0.3 0.25 0.15 0.2
0.7 0.15 0.5 0.35 0.1 0.15 0.2 0.2 0.5 0.2 0.65 0.8 0.15 0.6
0.2 0.2 0.2 0.6 0.65 0.05 0.3 0.8 0.7 0.05 0.65 0.3 0.8 0.7
0.05 0.85 0.65 0.65 0.45 0.45 0.65 0.75 0.85 0.8 0.65 0.15 0.25 0.65
0.2 0.9 0.5 0.45 0.2 1. 0.9 0.8 0.6 0.05 0.5 0.25 0.85 0.6
0.75 0.85 1. 0.6 0.1 0.25 0.4 0.4 0.25 0.85 0.85 0.05 0.2 0.45
0.3 0.45 0.2 0.5 1. 0.65 0.1 0.4 0.7 0.05 0.05 1. 0.45 0.1
0.85 0.25 0.4 0.05 0.5 1. 0.55 1. 0.6 0.3 0.15 0.8 0.9 0.5
1. 0.4 0.4 0.85 0.9 0.3 0.45 0.35 0.75 0.9 0.6 0.05 0.55 0.2
0.6 0.5 0.1 0.8 0.45 0.45 0.1 0.85 0.9 0.8 0.25 0.5 0.7 0.05
0.45 0.3 0.9 0.5 0.45 0.65 0.7 0.2 0.05 1. 0.6 0.9 0.1 0.1
0.85 0.25 0.2 0.5 0.4 0.8 0.7 0.6 0.4 0.7 0.3 0.3 0.5 0.65
0.8 0.8 0.25 0.25 0.6 0.65 0.9 0.7 0.2 0.2 0.2 0.45 0.8 0.9
0.6 0.95 0.45 0.5 0.4 0.8 0.2 0.25 0.05 0.1 0.8 0.4 0.45 0.1
0.85 0.15 0.65 0.5 0.5 0.5 0.55 0.05 0.5 1. 0.5 0.2 0.25 0.85
0.85 0.05 0.65 0.2 0.05 0.1 0.45 0.9 0.1 0.15 0.6 0.65 0.75 0.75
0.5 0.2 0.35 0.75 0.75 0.35 0.35 0.85 0.45 0.15 0.35 0.85 0.5 0.6
0.05 0.5 0.5 0.25 0.7 0.15 0.8 0.45 0.85 0.7 0.45 0.85 0.4 0.1
0.6 0.7 0.05 0.1 0.15 0.85 0.9 0.5 0.05 0.45 0.75 0.75 0.5 0.15
0.55 0.45 0.85 0.5 0.85 0.75 0.6 0.2 0.5 0.5 0.9 0.7 0.75 0.95
0.7 0.3 0.5 0.25 0.7 0.2 0.55 0.2 0.15 0.5 0.25 0.3 0.05 0.75
0.3 0.2 0.2 0.35 0.5 0.05 0.35 0.75 0.55 0.05 0.05 0.9 0.55 0.95
0.5 0.6 0.05 0.5 0.35 0.65 0.6 0.55 0.65 0.15 0.9 0.4 0.25 0.6
1. 0.4 0.45 0.55 0.9 0.9 0.2 0.6 0.45 0.65 0.45 0.7 0.75 1.
0.5 0.5 0.5 0.45 0.3 0.65 0.9 0.6 0.1 0.3 0.45 0.85 0.85 0.9
0.05 0.05 0.3 0.5 0.4 0.3 0.5 0.1 0.3 0.3 0.05 1. 0.5 0.6
0.4 0.6 0.1 0.65 0.65 0.2 0.75 0.5 0.05 0.25 0.2 0.5 0.3 0.55
0.05 0.4 0.15 0.7 0.25 0.85 0.9 0.9 0.25 0.2 0.15 0.9 0.45 0.3
0.45 0.45 0.8 0.8 0.45 0.8 0.8 0.5 0.7 0.85 0.3 0.4 0.3 0.25
0.2 0.6 0.5 0.15 0.6 0.5 0.75 0.15 0.35 0.5 0.15 0.3 0.05 0.9
0.65 0.9 0.6 0.5 0.2 0.75 0.45 0.35 0.7 0.6 0.45 0.9 0.85 0.7
0.4 0.25 0.65 0.65 0.3 0.4 0.9 0.85 0.1 0.6 0.2 1. 0.15 0.4
0.65 0.5 0.85 0.9 0.45 0.4 0.75 0.55 1. 0.7 0.45 0.9 0.2 0.5
0.5 0.35 0.6 1. 0.6 0.5 1. 0.45 0.8 0.5 0.45 0.6 0.85 0.2
0.2 0.9 0.3 0.45 0.45 0.75 1. 0.5 0.7 0.8 0.3 0.4 0.85 0.95
0.5 0.5 0.5 0.5 0.4 1. 0.25 0.75 0.8 0.9 0.15 0.15 0.25 0.85
0.2 0.45 0.5 0.7 0.2 0.1 0.9 0.9 0.45 0.95 0.45 0.2 0.75 0.4
0.2 0.85 0.2 0.9 0.3 0.15 0.55 0.95 0.5 0.5 0.85 0.55 0.45 0.2
0.6 0.4 0.2 0.7 0.3 0.45 0.6 0.4 0.5 0.2 0.45 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. ]
数据用 0 填充，因此所有序列的长度为 1000 个元素。
这是我正在使用的模型的代码（如果要下载，请使用保管箱链接）
import numpy as np
from sklearn.utils import shuffle
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
import numpy as np
def main():
posData = createListOfSequences('PositiveData.txt')
negData = createListOfSequences('NegativeData.txt')
# Shuffle both
np.random.shuffle(posData)
np.random.shuffle(negData)
# Create target lists
PosTargets = [1.0] * len(posData)
NegTargets = [0.0] * len(negData)
# Combine PosData and NegData into 1 list
Sequences = posData + negData
Targets = PosTargets + NegTargets
# shuffle sequences but maintain link to targets
(Sequences, Targets) = shuffle(Sequences, Targets)
# make all sequences 1000 in length
for sequence in Sequences:
sequence = padWithZero(1000, sequence)
Sequences = np.array(Sequences)
Targets = np.array(Targets)
FitModel(Sequences, Targets)
def FitModel(data, target):
trainPercentage = 0.8
splitValue = round(len(data) * trainPercentage)
(x_train, y_train) = data[0:splitValue], target[0:splitValue]
(x_test, y_test) = data[splitValue:len(
data)], target[splitValue:len(target)]
model = Sequential()
model.add(LSTM(128, activation='relu',
input_shape=(1000, 1), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(1, activation='sigmoid'))
opt = tf.keras.optimizers.Adam(lr=1e-3, decay=1e-5)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=3, validation_data=(x_test, y_test))
def padWithZero(targetLength, sequence):
i = 0
while not(len(sequence) >= targetLength):
if (i % 2 == 0):
sequence.append(0.0)
else:
sequence.insert(0, 0.0)
i += 1
return sequence
def createListOfSequences(dataPath):
DataAsString = []
for line in open(dataPath):
x = line.rstrip()
DataAsString.append((x.split(',')))
ListOfSequences = []
for seq in DataAsString:
listOfFloats = []
for val in seq:
if '.' in val:
listOfFloats.append(float(val))
ListOfSequences.append(listOfFloats)
return(ListOfSequences)
main()
任何帮助将不胜感激。谢谢。
【问题讨论】：
-
一份正式说明 - 请提供
minimal可重现的示例。问更有针对性的问题，不要提供这么长的代码和数据。如果需要，您可以将其分解为更多问题。 -
抱歉，我认为我的保管箱链接是一个最小的可重现示例。下次我会尽量减少它。
标签： python numpy keras lstm recurrent-neural-network