class RNN: def init(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size # 初始化参数 self.Wxh = np.random.randn(hidden_size, input_size) * 0.01 # 输入层到隐藏层的权重矩阵 self.Whh = np.random.randn(hidden_size, hidden_size) * 0.01 # 隐藏层到隐藏层的权重矩阵 self.Why = np.random.randn(output_size, hidden_size) * 0.01 # 隐藏层到输出层的权重矩阵 self.bh = np.zeros((hidden_size, 1)) # 隐藏层偏置 self.by = np.zeros((output_size, 1)) # 输出层偏置 # 初始化隐藏状态 self.h = np.zeros((hidden_size, 1)) def forward(self, x): # 更新隐藏状态 self.h = np.tanh(np.dot(self.Wxh, x) + np.dot(self.Whh, self.h) + self.bh) # 计算输出 y = np.dot(self.Why, self.h) + self.by # 返回输出和隐藏状态 return y, self.h def backward(self, x, y, target, learning_rate): # 计算输出误差 dy = y - target # 计算隐藏状态误差 dh = np.dot(self.Why.T, dy) * (1 - self.h ** 2) # 计算权重和偏置的梯度 dWhy = np.dot(dy, self.h.T) dby = np.sum(dy, axis=1, keepdims=True) dWxh = np.dot(dh, x.T) dWhh = np.dot(dh, self.h.T) dbh = np.sum(dh, axis=1, keepdims=True) # 更新权重和偏置 self.Why -= learning_rate * dWhy self.by -= learning_rate * dby self.Wxh -= learning_rate * dWxh self.Whh -= learning_rate * dWhh self.bh -= learning_rate * dbh 帮写一下用online_shopping_10_cats数据集训练以上模型的代码和步骤
时间: 2024-02-25 11:54:16 浏览: 114
好的,以下是使用online_shopping_10_cats数据集训练以上模型的代码和步骤:
1. 数据预处理
首先,需要对数据集进行预处理,将每个商品评论转换为数字序列,并将其填充到相同的长度,以便于输入到模型中。可以使用Python中的keras.preprocessing.text.Tokenizer()类来实现这一步骤。代码如下:
```
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
# 读取数据集文件
with open('reviews.txt', 'r', encoding='utf8') as f:
reviews = f.readlines()
# 将每个评论转换为数字序列
tokenizer = Tokenizer()
tokenizer.fit_on_texts(reviews)
sequences = tokenizer.texts_to_sequences(reviews)
# 将序列填充到相同的长度
max_len = 100 # 设定最大长度为100
sequences = pad_sequences(sequences, maxlen=max_len, padding='post')
```
2. 划分训练集和测试集
将数据集划分为训练集和测试集,以便于在训练过程中进行模型评估。可以使用sklearn.model_selection中的train_test_split()函数来实现这一步骤。代码如下:
```
from sklearn.model_selection import train_test_split
# 读取标签文件
with open('labels.txt', 'r', encoding='utf8') as f:
labels = f.readlines()
# 划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(sequences, labels, test_size=0.2, random_state=42)
```
3. 构建和训练模型
使用以上给出的RNN模型,构建并训练模型。需要注意的是,模型的输出需要经过softmax激活函数,以保证输出结果是概率分布。代码如下:
```
import numpy as np
# 定义模型参数
input_size = len(tokenizer.word_index) + 1
hidden_size = 100
output_size = 10
learning_rate = 0.01
epochs = 10
batch_size = 128
# 初始化模型
model = RNN(input_size, hidden_size, output_size)
# 训练模型
for epoch in range(epochs):
print('Epoch', epoch+1)
for i in range(0, len(x_train), batch_size):
x_batch = x_train[i:i+batch_size]
y_batch = y_train[i:i+batch_size]
# 将标签转换为one-hot编码
y_batch = np.eye(output_size)[y_batch]
# 前向传播
y_pred, h = model.forward(x_batch.T)
# 计算损失函数
loss = -np.sum(y_batch*np.log(y_pred)) / len(x_batch)
# 反向传播
model.backward(x_batch.T, y_pred, y_batch, learning_rate)
# 在测试集上进行模型评估
y_test_pred, _ = model.forward(x_test.T)
y_test_pred = np.argmax(y_test_pred, axis=0)
accuracy = np.mean(np.equal(y_test_pred, y_test))
print('Test accuracy:', accuracy)
```
4. 模型评估
在训练过程中,可以在每个epoch结束后,在测试集上进行模型评估,以评估模型的性能。代码如下:
```
# 在测试集上进行模型评估
y_test_pred, _ = model.forward(x_test.T)
y_test_pred = np.argmax(y_test_pred, axis=0)
accuracy = np.mean(np.equal(y_test_pred, y_test))
print('Test accuracy:', accuracy)
```
以上就是使用online_shopping_10_cats数据集训练以上模型的完整代码和步骤。
阅读全文