写一个CNN文本分类代码
时间: 2023-03-19 22:20:58 浏览: 92
你可以使用Python开发这个CNN文本分类代码。下面是一个简单的CNN文本分类代码示例:import torch
import torch.nn as nnclass TextCNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, num_class):
super(TextCNN, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.conv1 = nn.Conv1d(embedding_dim, 64, 3, padding=1)
self.conv2 = nn.Conv1d(64, 32, 3, padding=1)
self.fc = nn.Linear(32, num_class)
def forward(self, x):
x = self.embedding(x)
x = self.conv1(x)
x = self.conv2(x)
x = x.max(dim=2)[0]
x = self.fc(x)
return x
相关问题
TensorFlow CNN文本分类python代码
以下是一个使用TensorFlow实现的CNN文本分类的Python代码示例:
```python
import tensorflow as tf
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.datasets import imdb
# 加载IMDB数据集
(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=10000)
# 对序列进行填充,保证输入的长度相同
maxlen = 100
x_train = pad_sequences(x_train, maxlen=maxlen)
x_test = pad_sequences(x_test, maxlen=maxlen)
# 定义模型
model = tf.keras.models.Sequential([
tf.keras.layers.Embedding(input_dim=10000, output_dim=100, input_length=maxlen),
tf.keras.layers.Conv1D(filters=32, kernel_size=5, activation='relu'),
tf.keras.layers.MaxPooling1D(pool_size=2),
tf.keras.layers.Conv1D(filters=64, kernel_size=5, activation='relu'),
tf.keras.layers.MaxPooling1D(pool_size=2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(units=64, activation='relu'),
tf.keras.layers.Dense(units=1, activation='sigmoid')
])
# 编译模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(x_train, y_train, epochs=10, batch_size=128, validation_data=(x_test, y_test))
```
这个模型使用了一个Embedding层将输入的单词序列转换为向量形式,然后通过两个卷积层和池化层提取特征,最后通过全连接层输出结果。在训练过程中,使用了Adam优化器和二元交叉熵损失函数。
LSTM-CNN文本分类算法代码
以下是基于LSTM-CNN的文本分类算法的Python代码:
```python
import numpy as np
import tensorflow as tf
class LSTM_CNN_Model:
def __init__(self, sequence_length, num_classes, vocab_size, embedding_size, filter_sizes, num_filters, hidden_size, dropout_rate):
self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name='input_x')
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name='input_y')
self.dropout_rate = tf.placeholder(tf.float32, name='dropout_rate')
self.global_step = tf.Variable(0, trainable=False, name='global_step')
with tf.name_scope('embedding'):
self.W = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0), name='W')
self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.name_scope('conv-maxpool-%s' % filter_size):
filter_shape = [filter_size, embedding_size, 1, num_filters]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name='W')
b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name='b')
conv = tf.nn.conv2d(self.embedded_chars_expanded, W, strides=[1, 1, 1, 1], padding='VALID', name='conv')
h = tf.nn.relu(tf.nn.bias_add(conv, b), name='relu')
pooled = tf.nn.max_pool(h, ksize=[1, sequence_length - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='VALID', name='pool')
pooled_outputs.append(pooled)
num_filters_total = num_filters * len(filter_sizes)
self.h_pool = tf.concat(pooled_outputs, 3)
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
with tf.name_scope('lstm'):
lstm_cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
lstm_cell = tf.contrib.rnn.DropoutWrapper(lstm_cell, output_keep_prob=1.0 - self.dropout_rate)
outputs, _ = tf.nn.dynamic_rnn(lstm_cell, self.embedded_chars, dtype=tf.float32)
lstm_out = tf.reduce_mean(outputs, axis=1)
with tf.name_scope('output'):
W = tf.Variable(tf.truncated_normal([num_filters_total + hidden_size, num_classes], stddev=0.1), name='W')
b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name='b')
self.scores = tf.nn.xw_plus_b(tf.concat([self.h_pool_flat, lstm_out], axis=1), W, b, name='scores')
self.predictions = tf.argmax(self.scores, 1, name='predictions')
with tf.name_scope('loss'):
losses = tf.nn.softmax_cross_entropy_with_logits_v2(logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses)
with tf.name_scope('accuracy'):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, 'float'), name='accuracy')
self.optimizer = tf.train.AdamOptimizer(learning_rate=1e-3).minimize(self.loss, global_step=self.global_step)
```
这段代码实现了一个LSTM和CNN结合的文本分类模型,使用了dropout和Adam优化器。其中,`sequence_length`表示输入序列的长度,`num_classes`表示分类数,`vocab_size`表示词汇表的大小,`embedding_size`表示词向量的维度,`filter_sizes`表示卷积核的大小,`num_filters`表示卷积核的数量,`hidden_size`表示LSTM隐藏层的大小,`dropout_rate`表示dropout的概率。在模型的构建中,首先将输入的词序列转换为词向量,然后通过卷积核进行卷积和池化操作,得到一个固定长度的向量表示。接着,将词向量输入到LSTM中,得到一个平均池化的向量表示。最后将卷积和LSTM的输出拼接在一起,通过一个全连接层进行分类。