用python代码实现循环神经网络
时间: 2023-11-19 10:09:08 浏览: 35
好的,以下是使用Python实现循环神经网络的代码:
```
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def rnn_cell_forward(xt, a_prev, parameters):
Wax = parameters["Wax"]
Waa = parameters["Waa"]
Wya = parameters["Wya"]
ba = parameters["ba"]
by = parameters["by"]
a_next = np.tanh(np.dot(Wax, xt) + np.dot(Waa, a_prev) + ba)
yt_pred = softmax(np.dot(Wya, a_next) + by)
cache = (a_next, a_prev, xt, parameters)
return a_next, yt_pred, cache
def rnn_forward(x, a0, parameters):
n_x, m, T_x = x.shape
n_y, n_a = parameters["Wya"].shape
a = np.zeros((n_a, m, T_x))
y_pred = np.zeros((n_y, m, T_x))
caches = []
a_next = a0
for t in range(T_x):
a_next, yt_pred, cache = rnn_cell_forward(x[:,:,t], a_next, parameters)
a[:,:,t] = a_next
y_pred[:,:,t] = yt_pred
caches.append(cache)
caches = (caches, x)
return a, y_pred, caches
def lstm_cell_forward(xt, a_prev, c_prev, parameters):
Wf = parameters["Wf"]
bf = parameters["bf"]
Wi = parameters["Wi"]
bi = parameters["bi"]
Wc = parameters["Wc"]
bc = parameters["bc"]
Wo = parameters["Wo"]
bo = parameters["bo"]
Wy = parameters["Wy"]
by = parameters["by"]
n_x, m = xt.shape
n_y, n_a = Wy.shape
concat = np.zeros((n_a+n_x, m))
concat[:n_a, :] = a_prev
concat[n_a:, :] = xt
ft = sigmoid(np.dot(Wf, concat) + bf)
it = sigmoid(np.dot(Wi, concat) + bi)
cct = np.tanh(np.dot(Wc, concat) + bc)
c_next = ft * c_prev + it * cct
ot = sigmoid(np.dot(Wo, concat) + bo)
a_next = ot * np.tanh(c_next)
yt_pred = softmax(np.dot(Wy, a_next) + by)
cache = (a_next, c_next, a_prev, c_prev, ft, it, cct, ot, xt, parameters)
return a_next, c_next, yt_pred, cache
def lstm_forward(x, a0, parameters):
n_x, m, T_x = x.shape
n_y, n_a = parameters["Wy"].shape
a = np.zeros((n_a, m, T_x))
c = np.zeros((n_a, m, T_x))
y_pred = np.zeros((n_y, m, T_x))
caches = []
a_next = a0
c_next = np.zeros((n_a, m))
for t in range(T_x):
a_next, c_next, yt_pred, cache = lstm_cell_forward(x[:,:,t], a_next, c_next, parameters)
a[:,:,t] = a_next
y_pred[:,:,t] = yt_pred
caches.append(cache)
c[:,:,t] = c_next
caches = (caches, x)
return a, y_pred, c, caches
```
这是一个实现循环神经网络的基本方法,其中包括了RNN和LSTM两种类型的循环神经网络的代码。通过这些代码可以实现基本的文本分类、时间序列预测等任务。