# ... code from previous section here classOurNeuralNetwork: ''' A neural network with: - 2 inputs - a hidden layer with 2 neurons (h1, h2) - an output layer with 1 neuron (o1) Each neuron has the same weights and bias: - w = [0, 1]...
weight_in[i][j]=random_number(0.1,0.1) for i in range(self.num_hidden): # 对weight_out矩阵赋初值 for j in range(self.num_out): self.weight_out[i][j]=random_number(0.1,0.1) # 偏差 for j in range(self.num_hidden): self.weight_in[0][j]=0.1 for j in range(self.num_out)...
实在是觉得LaTeX编译出来的公式太好看了,所以翻译了一下,原文地址: Machine Learning for Beginners: An Introduction to Neural Networks - victorzhou.comvictorzhou.com/blog/intro-to-neural-networks/ 有个事情可能会让初学者惊讶:神经网络并不复杂!『神经网络』这个词让人觉得很高大上,但实际上神经网络算法...
importnumpyasnp# ... code from previous section hereclassOurNeuralNetwork:''' A neural network with: - 2 inputs - a hidden layer with 2 neurons (h1, h2) - an output layer with 1 neuron (o1) Each neuron has the same weights and bias: - w = [0, 1] - b = 0 '''def__init_...
A neural network with: - 2 inputs - a hidden layer with 2 neurons (h1, h2) - an output layer with 1 neuron (o1) Each neuron has the same weights and bias: - w = [0, 1] - b = 0 ''' def__init__(self): weights = np.array([0,1]) ...
# The inputs for o1 are the outputs from h1 and h2 out_o1 = self.o1.feedforward(np.array([out_h1, out_h2])) return out_o1 network = OurNeuralNetworks() x = np.array([2, 3]) print(network.feedforward(x)) # 0.7216325609518421 ...
# The inputs for o1 are the outputs from h1 and h2 out_o1 = self.o1.feedforward(np.array([out_h1, out_h2])) return out_o1 network = OurNeuralNetworks() x = np.array([2, 3]) print(network.feedforward(x)) # 0.7216325609518421 ...
本文主要内容是使用python实现神经网络。neural-networks-and-deep-learning神经网络的实现,虽然在原理上易懂,但是不够模块化,layer、net、loss、optimizer间的耦合性太高。通用的深度学习框架,例如caffe都是将每个模块分离实现,这样提升了代码的可阅读,扩展性。
L= len(parameters) // 2#number of layers in the neural network#Implement [LINEAR -> RELU]*(L-1). Add "cache" to the "caches" list.forlinrange(1, L): A_prev=A A, cache=linear_activation_forward(A_prev, parameters['W'+str(l)], ...
Finally, here comes the function to train our Neural Network. It implements batch gradient descent using the backpropagation derivates we found above. # This function learns parameters for the neural network and returns the model. # - nn_hdim: Number of nodes in the hidden layer ...