小编Had*_*yat的帖子

我正在尝试构建一个基于 LSTM RNN 的深度学习网络，这是尝试过的

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
import numpy as np

train = np.loadtxt("TrainDatasetFinal.txt", delimiter=",")
test = np.loadtxt("testDatasetFinal.txt", delimiter=",")

y_train = train[:,7]
y_test = test[:,7]

train_spec = train[:,6]
test_spec = test[:,6]


model = Sequential()
model.add(LSTM(32, input_shape=(1415684, 8),return_sequences=True))
model.add(LSTM(64, input_dim=8, input_length=1415684, return_sequences=True))
##model.add(Embedding(1, 256, input_length=5000))
##model.add(LSTM(64,input_dim=1, input_length=10, activation='sigmoid',
##               return_sequences=True, inner_activation='hard_sigmoid'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='rmsprop')

model.fit(train_spec, y_train, batch_size=2000, nb_epoch=11)
score = model.evaluate(test_spec, y_test, batch_size=2000)

但它让我出现以下错误 …

我正在尝试使用 keras 在深度学习模型中测试我的拆分 这是我的代码

 from keras.models import Sequential
from keras.layers import Dense, Dropout
import numpy as np
from scipy import signal
import matplotlib.pyplot as plt
import pandas as pd
import itertools
np.random.seed(7)

train = np.loadtxt("TrainDatasetFinal.txt", delimiter=",")
test = np.loadtxt("testDatasetFinal.txt", delimiter=",")

y_train = train[:,7]
y_test = test[:,7]

magnitude_training = train[:,5]
norm_train = (magnitude_training - np.mean(magnitude_training))/np.std(magnitude_training)
magnitude_testing = test[:,5]
norm_test = (magnitude_testing - np.mean(magnitude_testing))/np.std(magnitude_testing)

model = Sequential()
model.add(Dense(12, input_dim=1, activation='relu'))
model.add(Dense(8,  activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy' , optimizer='adam', metrics=['accuracy'])

model.fit(norm_train, y_train, epochs=2, batch_size=64, …

我正在尝试计算用 KERAS 编写的 y 模型中训练准确度的平均值，我有 200 个时期。所以最后我想将每个时期的每个训练准确度与前一个相加，然后除以 200..

这是我的代码

num = 200
total_sum = 0
for n in range(num):
    avg_train=np.array(model.fit(x_train,y_train, epochs=200, batch_size=64, verbose=2))
    total_sum = avg_train + total_sum
avg = total_sum/num

score=model.evaluate(x_test, y_test, verbose=2)
print(score)
print('the average is',avg)

我试图将每个精度存储在一个 numpy 数组中，以便能够在求和运算中使用它，但它给了我以下错误

Traceback (most recent call last):
  File "G:\Master Implementation\MLPADAM.py", line 87, in <module>
    total_sum = avg_train + total_sum
TypeError: unsupported operand type(s) for +: 'History' and 'int'