我一直在愚弄我试图获得我创建的简单示例，因为我发现大型复杂数据集给出的示例很难直观地理解。下面的程序采用权重列表[x_0 x_1 ... x_n]，并使用它们在平面上创建随机散射的点，并添加一些随机噪声。然后我在这些数据上训练简单的神经网络并检查结果。
当我使用Graph模型执行此操作时，一切都运行良好，当模型收敛于给定的权重时，损失分数可预测地降至零。但是，当我尝试使用顺序模型时，没有任何反应。代码
如果你愿意，我可以发布使用Graph而不是顺序的其他脚本，并显示它完美地找到输入权重。
#!/usr/bin/env python
from keras.models import Sequential, Graph
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import SGD
import numpy as np
import theano, sys
NUM_TRAIN = 100000
NUM_TEST = 10000
INDIM = 3
mn = 1
def myrand(a, b) :
return (b)*(np.random.random_sample()-0.5)+a
def get_data(count, ws, xno, bounds=100, rweight=0.0) :
xt = np.random.rand(count, len(ws))
xt = np.multiply(bounds, xt)
yt = np.random.rand(count, 1)
ws = np.array(ws, dtype=np.float)
xno = np.array([float(xno) + rweight*myrand(-mn, mn) for x in xt], dtype=np.float)
yt = np.dot(xt, ws)
yt = np.add(yt, xno)
return (xt, yt)
if __name__ == '__main__' :
if len(sys.argv) > 1 :
EPOCHS = int(sys.argv[1])
XNO = float(sys.argv[2])
WS = [float(x) for x in sys.argv[3:]]
mx = max([abs(x) for x in (WS+[XNO])])
mn = min([abs(x) for x in (WS+[XNO])])
mn = min(1, mn)
WS = [float(x)/mx for x in WS]
XNO = float(XNO)/mx
INDIM = len(WS)
else :
INDIM = 3
WS = [2.0, 1.0, 0.5]
XNO = 2.2
X_test, y_test = get_data(10000, WS, XNO, 10000, rweight=0.4)
X_train, y_train = get_data(100000, WS, XNO, 10000)
model = Sequential()
model.add(Dense(INDIM, input_dim=INDIM, init='uniform', activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(2, init='uniform', activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(1, init='uniform', activation='softmax'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='mean_squared_error', optimizer=sgd)
model.fit(X_train, y_train, shuffle="batch", show_accuracy=True, nb_epoch=EPOCHS)
score, acc = model.evaluate(X_test, y_test, batch_size=16, show_accuracy=True)
print score
print acc
predict_data = np.random.rand(100*100, INDIM)
predictions = model.predict(predict_data)
for x in range(len(predict_data)) :
print "%s --> %s" % (str(predict_data[x]), str(predictions[x]))
输出如下
$ ./keras_hello.py 20 10 5 4 3 2 1
Using gpu device 0: GeForce GTX 970 (CNMeM is disabled)
Epoch 1/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 2/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 3/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 4/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 5/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 6/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 7/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 8/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 9/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 10/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 11/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 12/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 13/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 14/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 15/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 16/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 17/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 18/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 19/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
Epoch 20/20
100000/100000 [==============================] - 0s - loss: 60726734.3061 - acc: 1.0000
10000/10000 [==============================] - 0s
60247198.6661
1.0
[ 0.06698217 0.70033048 0.4317502 0.78504855 0.26173543] --> [ 1.]
[ 0.28940025 0.21746189 0.93097653 0.94885535 0.56790348] --> [ 1.]
[ 0.69430499 0.1622601 0.22802859 0.75709315 0.88948355] --> [ 1.]
[ 0.90714721 0.99918648 0.31404901 0.83920051 0.84081288] --> [ 1.]
[ 0.02214092 0.03132355 0.14417082 0.33901317 0.91491426] --> [ 1.]
[ 0.31426055 0.80830795 0.46686523 0.58353359 0.50000842] --> [ 1.]
[ 0.27649579 0.77914451 0.33572287 0.08703303 0.50865592] --> [ 1.]
[ 0.99280349 0.24028343 0.05556034 0.31411902 0.41912574] --> [ 1.]
[ 0.91897031 0.96840695 0.23561379 0.16005505 0.06567748] --> [ 1.]
[ 0.27392867 0.44021533 0.44129147 0.40658522 0.47582736] --> [ 1.]
[ 0.82063221 0.95182938 0.64210378 0.69578691 0.2946907 ] --> [ 1.]
[ 0.12672415 0.35700418 0.89303047 0.80726545 0.79870725] --> [ 1.]
[ 0.6662085 0.41358115 0.76637022 0.82093095 0.76973305] --> [ 1.]
[ 0.96201937 0.29706843 0.22856618 0.59924945 0.05653825] --> [ 1.]
[ 0.34120276 0.71866377 0.18758929 0.52424856 0.64061623] --> [ 1.]
[ 0.25471237 0.35001821 0.63248632 0.45442404 0.96967989] --> [ 1.]
[ 0.79390087 0.00100834 0.49645204 0.55574269 0.33487764] --> [ 1.]
[ 0.41330261 0.38061826 0.33766183 0.23133121 0.80999653] --> [ 1.]
[ 0.49603561 0.33414841 0.10180184 0.9227252 0.35073833] --> [ 1.]
[ 0.17960345 0.05259438 0.565135 0.40465603 0.91518233] --> [ 1.]
[ 0.36129943 0.903603 0.63047644 0.96553285 0.94006713] --> [ 1.]
[ 0.7150973 0.93945141 0.31802763 0.15849441 0.92902078] --> [ 1.]
[ 0.23730571 0.65360248 0.68776259 0.79697206 0.86814652] --> [ 1.]
[ 0.47414382 0.75421265 0.32531333 0.43218305 0.4680773 ] --> [ 1.]
[ 0.4887811 0.66130135 0.79913557 0.68948405 0.48376372] --> [ 1.]
....
答案 0 :(得分：3)
出现巨大错误的原因是您的标签不是二进制且非常大，但softmax的输出是二进制的。例如，如果标签是10000但你只能预测0到1之间的某个东西，那么无论你预测什么，都会有很大的错误。你的意思是在最后一层中activation='linear'进行回归吗？或者您是否想在get_data()结束时将标签放入softmax？
答案 1 :(得分：0)
因为y_train由5个元素组成，所以输出模型也应该是5个元素，
[ 0.06698217 0.70033048 0.4317502 0.78504855 0.26173543] --> [ 1.]
[ 0.28940025 0.21746189 0.93097653 0.94885535 0.56790348] --> [ 1.]
[ 0.69430499 0.1622601 0.22802859 0.75709315 0.88948355] --> [ 1.]
例如试试这个网络
model = Sequential()
model.add(Dense(INDIM, input_dim=INDIM, init='uniform', activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(10, init='uniform', activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(5, init='uniform', activation='softmax'))