BP_Mnist_ezCode_MLP

ezCode of Backpropagation with sigle hidden layer to train Mnist and some Gate.
Please pip install sklearn matplotlib pylab numpy

Run mlp_DigitsMnist.py

just run , but you could change the model hyperparameters by below code line
def train(self, data, iterations=1000, alpha=0.01)
alpha: learning rate
iterations:The training times of the model you want.

Run mlp_Gate.py

1. just change the data inside below
   def demo():
   data = np.array([
   [[0,0,0], [0]],
   [[0,1,0], [1]],
   [[1,0,0], [1]],
   [[1,1,1], [0]]
   ])
   as you can see , here is four samples, each samlpe has 3 inputs feature , and one lable.
   it's XOR gate Truth table with extended feture(X1*X2)
2. change the hyperparameters baseon your data form of below code to built your NN
   n = NN(3, 10, 1)
   it means input layer with 3 node of feature . single hidden layer with 10 nodes, output layer with 1 output.
3. just run , but you could change the model hyperparameters by below code line
   def train(self, data, iterations=1000, alpha=0.01):
   alpha: learning rate
   iterations:The training times of the model you want.