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我试图让我如何使用由Keras模型产生的权重来计算Python中的结果清楚的认识（即预测值，而不使用Keras模型来预测）。当我有比下面的一个简单的案件（2个功能，没有隐藏层或1个功能隐藏层），我得到的结果，我希望（我计算出的值作为模型预测值相同）。在下面的代码中，输出值不一致。所以我要么不理解，要么看不到什么。如果病人可以用简单的英语解释我怎么能做到这一点，我会很高兴。我无法使用Keras的权重来计算模型
示例代码（具有2点的特征和1隐藏层）
from keras.models import Sequential
from keras.layers import Dense
import numpy
# sigmoid function: nonlinearity.
def nonlin(x, deriv = False):
if (deriv == True):
return x * (1 - x)
return 1/(1 + numpy.exp(-x))
# fix random seed for reproducibility
numpy.random.seed(5)
# load dataset
dataset = numpy.loadtxt("values2.txt", delimiter=",") # 4 samples 2 factors 1 y
# split into input (X) and output (Y) variables
X = dataset[:,0:2]
Y = dataset[:,2]
# create model (1 hidden layer)
model = Sequential()
model.add(Dense(2, input_dim=2, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=200, batch_size=1)
# calculate predictions
predictions = model.predict(X)
# round predictions
rounded = [round(x[0]) for x in predictions]
#print(rounded)
#model.summary()
for layer in model.layers:
weights = layer.get_weights() # list of numpy arrays
print(weights)
M = model.get_weights()
# Example where X = 0()
print (X[0,0], X[0,1], "=", Y[0])
L1 = numpy.array([ [0.0], [0.0] ]) # to hold result of input * weights
L1[0] = X[0,0] * M[0][0][0] + X[0,1] * M[0][1][0] + M[1][0]
L1[1] = X[0,0] * M[0][0][1] + X[0,1] * M[0][1][1] + M[1][1]
L2 = numpy.array([ [0.0] ])
L2[0] = L1[0] * M[2][0][0] + L1[1] * M[2][1][0] + M[3][0]
print (nonlin(L2[0]), predictions[0]) # compare
输入（Values2.txt：所述第一X确定Y）
0,0,0
0,1,0
1,0,1
1,1,1
输出 ...
Epoch 200/200
4/4 [==============================] - 0s - loss: 0.4380 - acc: 1.0000
[0.0, 0.0, 1.0, 1.0]
[array([[ 1.69063699, 0.1998844 ],
[ 0.07858475, 0.89751321]], dtype=float32), array([-0.00052627, -0.17762977], dtype=float32)]
[array([[ 0.83898878],
[-0.54218996]], dtype=float32), array([-0.05681464], dtype=float32)]
0.0 0.0 = 0.0
[ 0.50976198] [ 0.48580015]
感谢的Matias Valdenegro
响应于你的意见我已经改变的代码下面并可以产生期望的输出。我在第一层将激活从relu改为sigmoid，并在第二层使用nonlin函数对第一层的结果使用nonlin函数。那是对的吗？从修改后的代码
修改后的代码
from keras.models import Sequential
from keras.layers import Dense
import numpy
# sigmoid function: nonlinearity.
def nonlin(x, deriv = False):
if (deriv == True):
return x * (1 - x)
return 1/(1 + numpy.exp(-x))
# fix random seed for reproducibility
numpy.random.seed(5)
# load dataset
dataset = numpy.loadtxt("values2.txt", delimiter=",") # 4 samples 2 factors 1 y
# split into input (X) and output (Y) variables
X = dataset[:,0:2]
Y = dataset[:,2]
# create model (1 hidden layer)
model = Sequential()
model.add(Dense(2, input_dim=2, activation='sigmoid'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=200, batch_size=1)
# calculate predictions
predictions = model.predict(X)
# round predictions
rounded = [round(x[0]) for x in predictions]
#print(rounded)
#model.summary()
for layer in model.layers:
weights = layer.get_weights() # list of numpy arrays
print(weights)
M = model.get_weights()
# Example where X = 0()
print (X[0,0], X[0,1], "=", Y[0])
L1 = numpy.array([ [0.0], [0.0] ]) # to hold result of input * weights
L1[0] = X[0,0] * M[0][0][0] + X[0,1] * M[0][1][0] + M[1][0]
L1[1] = X[0,0] * M[0][0][1] + X[0,1] * M[0][1][1] + M[1][1]
L2 = numpy.array([ [0.0] ])
L2[0] = nonlin(L1[0]) * M[2][0][0] + nonlin(L1[1]) * M[2][1][0] + M[3][0]
print (nonlin(L2[0]), predictions[0]) # compare
输出
Epoch 200/200
4/4 [==============================] - 0s - loss: 0.6463 - acc: 1.0000
[array([[ 1.70278633, 0.0848918 ],
[-0.0271775 , 0.92663836]], dtype=float32), array([-0.14723039, 0.00718958], dtype=float32)]
[array([[ 0.56880862],
[-0.60756117]], dtype=float32), array([ 0.03559623], dtype=float32)]
0.0 0.0 = 0.0
[ 0.4985573] [ 0.4985573]
我添加了一个RELU函数（X *（X> 0）） 计算是现在为遵循并产生预期的结果。
L1 = numpy.array([ [0.0], [0.0] ]) # to hold result of input * weights
L1[0] = relu(X[0,0] * M[0][0][0] + X[0,1] * M[0][1][0] + M[1][0])
L1[1] = relu(X[0,0] * M[0][0][1] + X[0,1] * M[0][1][1] + M[1][1])
L2 = numpy.array([ [0.0] ])
L2[0] = L1[0] * M[2][0][0] + L1[1] * M[2][1][0] + M[3][0]
print (nonlin(L2[0]), predictions[0])
谢谢。 （假设这是最基本的答案，我不知道我应该做的事情标记作为这里的答案是什么......？）
我没有看到你在你的Python代码应用RELU。这可能是问题所在。 –
谢谢。我已经修改了我的代码作为回应。查看修改。 –
仍然在新代码中没有RELU ... –