Python代写代考

FullyConnectedNets-checkpoint Fully-Connected Neural Nets¶ In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. […]

ml-ass2 In [14]: import scipy.io as sio import pandas as pd import numpy as np import matplotlib.pyplot as plt %matplotlib inline In [15]: arr = sio.loadmat(‘DataD.mat’) fea = arr[‘fea’] gnd = arr[‘gnd’] gnd = gnd[:,0] 1)¶ In [16]: from sklearn import preprocessing zscoreScaler = preprocessing.StandardScaler() normalizedFea = pd.DataFrame(zscoreScaler.fit_transform(fea)) In [17]: halfNum = normalizedFea.shape[0]//2 trainFea = normalizedFea[:halfNum] testFea = normalizedFea[halfNum+1:]

BatchNormalization-checkpoint Batch Normalization¶ One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy is to change the architecture of the network to make it easier to train. One idea along these lines is batch normalization which was recently proposed by [3].