使用python實現三層神經網絡前向傳播推理。

#實現三層神經網絡前向傳播 import numpy as np #激活函數 def sigmoid(x):return 1 / (1 + np.exp(-x)) #初始化權重和偏置，保存在字典network中 def init_network():network = {}network['W1'] = np.array([[0.1,0.3,0.5],[0.2,0.4,0.6]])network['B1'] = np.array([0.1,0.2,0.3])network['W2'] = np.array([[0.1,0.4],[0.2,0.5],[0.3,0.6]])network['B2'] = np.array([0.1,0.2])network['W3'] = np.array([[0.1,0.3],[0.2,0.4]])network['B3'] = np.array([0.1,0.2])return network #處理前向傳播，從輸入層到輸出層，使用sigmoid激活函數 def forward(network,x):W1,W2,W3 = network['W1'],network['W2'],network['W3']B1,B2,B3 = network['B1'],network['B2'],network['B3']A1 = np.dot(x,W1) + B1Z1 = sigmoid(A1)A2 = np.dot(Z1,W2) + B2Z2 = sigmoid(A2)A3 = np.dot(Z2,W3) + B3Y = A3return Y #測試 network = init_network() x = np.array([1.0,0.01]) y = forward(network,x) print(y)

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