Thanks for the great tutorial but how exactly can we use it to predict the result for next input? I tried adding 4,8 in the input and it would cause error as:
input:
Traceback (most recent call last):
[[0.5 1. ]
[0.25 0.55555556]
[0.75 0.66666667]
[1. 0.88888889]]
Actual Output:
File "D:/try.py", line 58, in
[[0.92]
[0.86]
[0.89]]
print ("Loss: \n" + str(np.mean(np.square(y - NN.forward(X))))) # mean sum squared loss
Predicted Output:
[[0.17124108]
ValueError: operands could not be broadcast together with shapes (3,1) (4,1)
[0.17259949]
[0.20243644]
[0.20958544]]
Thanks for the great tutorial but how exactly can we use it to predict the result for next input? I tried adding 4,8 in the input and it would cause error as:
input:
Traceback (most recent call last):
[[0.5 1. ]
[0.25 0.55555556]
[0.75 0.66666667]
[1. 0.88888889]]
Actual Output:
File "D:/try.py", line 58, in
[[0.92]
[0.86]
[0.89]]
print ("Loss: \n" + str(np.mean(np.square(y - NN.forward(X))))) # mean sum squared loss
Predicted Output:
[[0.17124108]
ValueError: operands could not be broadcast together with shapes (3,1) (4,1)
[0.17259949]
[0.20243644]
[0.20958544]]
Process finished with exit code 1
after training done, you can make it like
Q = np.array(([4, 8]), dtype=float)
print "Input: \n" + str(Q)
print "Predicted Output: \n" + str(NN.forward(Q))