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kero.multib.NeuralNetwork.py def MSE(Y_set,Y0_set): return mse

**Arguments/Return**

Y_set | List of numpy matrix, [Y]. Each numpy matrix Y is a column vector |

Y0_set | List of numpy matrix, [Y0]. Each numpy matrix Y is a column vector. There should be equal number of Y in Y_set as Y0 in Y0_set |

mse | Mean squared values computed by the formula above. |

**Example Usage 1**

**testMSE.py**

Y0_set= [ [1,2,3], [4,5,6] ] Y_set = [[1.1*x for x in y] for y in Y0_set] print("Y0_set=",Y0_set) print("Y_set=",Y_set) import numpy as np # convert to list of numpy matrix Y0_set = [np.transpose(np.matrix(x)) for x in Y0_set] Y_set = [np.transpose(np.matrix(x)) for x in Y_set] import kero.multib.NeuralNetwork as nn mse=nn.MSE(Y_set,Y0_set) print("MSE test:\n mse=",mse) # manual computation mse_= 0.01*(1**2+2**2+3**2+4**2+5**2+6**2) mse_=mse_/(2*2) print(" compare: ",mse_) import kero.utils.utils as ut print("nabla MSE test:\n nabla mse = ") nabla_mse = nn.nabla_MSE(Y_set,Y0_set) ut.print_numpy_matrix(nabla_mse,formatting="%6.2f",no_of_space=5) nabla_mse_ = 1/2* ((Y_set[0]-Y0_set[0])+(Y_set[1]-Y0_set[1])) print(" compare: ") ut.print_numpy_matrix(nabla_mse_,formatting="%6.2f",no_of_space=5)

This will print

Y0_set= [[1, 2, 3], [4, 5, 6]] Y_set= [[1.1, 2.2, 3.3000000000000003], [4.4, 5.5, 6.6000000000000005]] MSE test: mse= 0.22750000000000029 compare: 0.2275 nabla MSE test: nabla mse = 0.25 0.35 0.45 compare: 0.25 0.35 0.45

*kero version: 0.6.2*