CS231n课程笔记：Leture6 Training Neural Networks I

目录

 pART1

 Activation Functions

 Data Preprocessing

Weight Initialization

Batch Normalization***

 Babysetting the learning process

 

 

 pART1

 Activation Functions

 

   

 

 

 

 

 

 In practice

 Data Preprocessing

 about PCA:

PCA(principal component analysis,主成分分析) - 简书 (jianshu.com)

Weight Initialization

four ways for weights initialization

(21条消息) 权值初始化——高斯初始化，Xavier初始化，MSRA初始化，He初始化_caicaiatnbu的博客-优快云博客_高斯分布初始化

  

A good general rule of thumb is basically use the Xavier initialization to start with, and then you can also think about some of these other kinds of methods.

Batch Normalization***

 We just estimate this at training time

 Babysetting the learning process

How do we monitor training?and how do we adjust hyperparameters as we go to get a good learning result?

codes for initial network

def init_two_layer_model(input_size, hidden_size, output_size):
    model = {}
    model['W1'] = 0.0001 * np.random.randn(input_size, hidden_size)
    model['b1'] = np.zeros(hidden_size)
    model['W2'] = 0.0001 * np.random.randn(hidden_size, output_size)
    model['b2'] = np.zeros(output_size)
    return model

model = init_two_layer_model(32*32*3, 50, 10) # input_size , hidden_size ,number of classes
loss, grad = two_layer_net(X_train, model, y_train, 0.0)  # 0.0 is the disable regularization
print loss 

trying to train

trainer = ClassifierTrainer()
X_tiny = X_train[:20] # take 20 examples
y_tiny = y_train[:20]
best_model, stats = trainer.train(X_tiny, y_tiny, X_tiny, y_tiny, model, two_layer_net, num_epochs=200, reg = 0.0, update='sgd',learning_rate_decay=1, sample_batches=False, learning_rate=1e-3, sample_batches=True, verbose=True)