import numpy as np # 1. 定义RNN的参数。 X = [1,2] state = [0.0, 0.0] w_cell_state = np.asarray([[0.1, 0.2], [0.3, 0.4]]) w_cell_input = np.asarray([0.5, 0.6]) b_cell = np.asarray([0.1, -0.1]) w_output = np.asarray([[1.0], [2.0]]) b_output = 0.1
# 2. 执行前向传播过程。 for i in range(len(X)): before_activation = np.dot(state, w_cell_state) + X[i] * w_cell_input + b_cell state = np.tanh(before_activation) final_output = np.dot(state, w_output) + b_output print("before activation: ", before_activation) print("state: ", state) print("output: ", final_output)