Python numpyは行列を正しく乗算しません。

Question

これらのy値をニューラルネットワークから引き出しようとしています。現在の問題は、私が期待したように行列を掛け合わせることはないようです。あなたのレビューにコードと出力を含めました。あなたが提供できる洞察力を事前に感謝します。

def columnToRow(column): 
    newarray = np.array([column]) 
    return newarray 


def calcIndividualOutput(indivInputs,weights,biases): 
    # finds the resulting y values for one set of input data 
    I_transposed= columnToRow(indivInputs) 
    output = np.multiply(I_transposed, weights) + biases 
    return output 


def getOutputs(inputs,weights,biases): 
    # iterates over each set of inputs to find corresponding outputs 
    # returns output matrix 
    i_len = len(inputs)-1 
    outputs = [] 
    for i in range(0,i_len): 
    result = calcIndividualOutput(inputs[i],weights,biases) 
    outputs.append(np.tanh(result)) 
    if (i==i_len): 
     print("Final Input reached:", i) 
    return outputs 



# Test Single line of Outputs should 
#print("Resulting Outputs0:\n\n",resultingOutputs[0,0:]) 

# Testing 
currI=data[0] 
Itrans=columnToRow(currI) 
print(" THE CURRENT I0\n\n",currI,"\n\n") 
print("transposed I:\n\n",Itrans,"\n\n") 
print("Itrans shape:\n\n",Itrans.shape,"\n\n") 

print("Current biases:\n\n",model_l1_b,"\n\n") 
print("Current biases shape:\n\n",model_l1_b.shape,"\n\n") 
print("B trans:",b_trans,"\n\n") 
print("B trans shape:",b_trans.shape,"\n\n") 

print("Current weights:\n\n",model_l1_W,"\n\n") 
print("Transposed weights\n\n",w_transposed,"\n\n") 
print("wtrans shape:\n\n",w_transposed.shape,"\n\n") 



#Test calcIndividualOutput 

testOutput= calcIndividualOutput(currI,w_transposed,b_trans) 
print("Test calcIndividualOutput:\n\n",testOutput,"\n\n") 
print("Test calcIndividualOutput Shape:\n\n",testOutput.shape,"\n\n") 

# Transpose weights to match dimensions of input 
b_trans=columnToRow(model_l1_b) 
w_transposed=np.transpose(model_l1_W) 
resultingOutputs = getOutputs(data,w_transposed,b_trans) 

出力：

THE CURRENT I0 

[-0.66399151 -0.59143853 0.5230611 -0.52583802 -0.31089544 0.47396523 
-0.7301591 -0.21042131 0.92044264 -0.48792791 -1.54127669] 


transposed I: 

[[-0.66399151 -0.59143853 0.5230611 -0.52583802 -0.31089544 0.47396523 
    -0.7301591 -0.21042131 0.92044264 -0.48792791 -1.54127669]] 


Itrans shape: 

(1, 11) 


Current biases: 

[ 0.04497563 -0.01878226 0.03285328 0.00443657 -0.10408497 0.03982726 
-0.07724283] 


Current biases shape: 

(7,) 


B trans: [[ 0.04497563 -0.01878226 0.03285328 0.00443657 -0.10408497 0.03982726 
    -0.07724283]] 


B trans shape: (1, 7) 


Current weights: 

[[ 0.02534341 0.01163373 -0.20102289 0.23845847 0.20859972 -0.09515963 
    0.00744185 -0.06694793 -0.03806938 0.02241485 0.34134269] 
[ 0.0828636 -0.14711063 0.44623381 0.0095899 0.41908434 -0.25378567 
    0.35789928 0.21531652 -0.05924326 -0.18556432 0.23026766] 
[-0.23547475 -0.18090464 -0.15210266 0.10483326 -0.0182989 0.52936584 
    0.15671678 -0.64570689 -0.27296376 0.28720504 0.21922119] 
[-0.17561196 -0.42502806 -0.34866759 -0.07662395 -0.02361901 -0.10330012 
    -0.2626377 0.19807351 0.20543958 -0.34499851 0.29347673] 
[-0.04404973 -0.31600055 -0.22984107 0.21733086 -0.15065287 0.18301299 
    0.13399698 0.11884601 0.04380761 -0.03720044 0.0146924 ] 
[ 0.25086868 0.15678053 0.30350113 0.13065964 -0.30319506 0.47015968 
    0.00549904 0.32486886 -0.00331726 0.22858304 0.16789439] 
[-0.10196115 -0.03687141 -0.28674102 0.01066647 0.2475083 0.15808311 
    -0.1452509 0.09170815 -0.14578934 -0.07375327 -0.16524883]] 


Transposed weights 

[[ 0.02534341 0.0828636 -0.23547475 -0.17561196 -0.04404973 0.25086868 
    -0.10196115] 
[ 0.01163373 -0.14711063 -0.18090464 -0.42502806 -0.31600055 0.15678053 
    -0.03687141] 
[-0.20102289 0.44623381 -0.15210266 -0.34866759 -0.22984107 0.30350113 
    -0.28674102] 
[ 0.23845847 0.0095899 0.10483326 -0.07662395 0.21733086 0.13065964 
    0.01066647] 
[ 0.20859972 0.41908434 -0.0182989 -0.02361901 -0.15065287 -0.30319506 
    0.2475083 ] 
[-0.09515963 -0.25378567 0.52936584 -0.10330012 0.18301299 0.47015968 
    0.15808311] 
[ 0.00744185 0.35789928 0.15671678 -0.2626377 0.13399698 0.00549904 
    -0.1452509 ] 
[-0.06694793 0.21531652 -0.64570689 0.19807351 0.11884601 0.32486886 
    0.09170815] 
[-0.03806938 -0.05924326 -0.27296376 0.20543958 0.04380761 -0.00331726 
    -0.14578934] 
[ 0.02241485 -0.18556432 0.28720504 -0.34499851 -0.03720044 0.22858304 
    -0.07375327] 
[ 0.34134269 0.23026766 0.21922119 0.29347673 0.0146924 0.16789439 
    -0.16524883]] 


wtrans shape: 

(11, 7) 


--------------------------------------------------------------------------- 
ValueError        Traceback (most recent call last) 
<ipython-input-162-7e8be1d52690> in <module>() 
    48 #Test calcIndividualOutput 
    49 
---> 50 testOutput= calcIndividualOutput(currI,w_transposed,b_trans) 
    51 print("Test calcIndividualOutput:\n\n",testOutput,"\n\n") 
    52 print("Test calcIndividualOutput Shape:\n\n",testOutput.shape,"\n\n") 

<ipython-input-162-7e8be1d52690> in calcIndividualOutput(indivInputs, weights, biases) 
     7 # finds the resulting y values for one set of input data 
     8 I_transposed= columnToRow(indivInputs) 
----> 9 output = np.multiply(I_transposed, weights) + biases 
    10 return output 
    11 

ValueError: operands could not be broadcast together with shapes (1,11) (11,7) 

Answer 1

np.multiplyは、配列の要素単位を掛けるためですが、あなたのデータの大きさから、私はあなたの行列乗算を探していることを推測します。それを得るにはnp.dot。

Answer 2

ドットプロダクトはR^n x R^n - > Rをマップします。これはおそらくあなたが望むものです。 Matlabから来ている場合は、A * BとAと同じ違いです。* B