SRNN训练路透社代码
时间: 2023-09-22 19:10:03 浏览: 57
以下是使用SRNN模型训练路透社数据集的示例代码,使用Python和TensorFlow:
```python
import numpy as np
import tensorflow as tf
# 载入数据集
from tensorflow.contrib import learn
from sklearn.datasets import fetch_20newsgroups
newsgroups_train = fetch_20newsgroups(subset='train')
newsgroups_test = fetch_20newsgroups(subset='test')
# 创建词汇表
vocab_processor = learn.preprocessing.VocabularyProcessor(1000)
X_train = np.array(list(vocab_processor.fit_transform(newsgroups_train.data)))
X_test = np.array(list(vocab_processor.transform(newsgroups_test.data)))
y_train = newsgroups_train.target
y_test = newsgroups_test.target
# 设定超参数
embedding_size = 50
n_hidden = 100
n_classes = 20
batch_size = 50
n_epochs = 10
# 构建模型
tf.reset_default_graph()
inputs = tf.placeholder(tf.int32, shape=[None, X_train.shape[1]])
targets = tf.placeholder(tf.int32, shape=[None])
embedding = tf.Variable(tf.random_uniform([len(vocab_processor.vocabulary_), embedding_size], -1.0, 1.0))
inputs_embedded = tf.nn.embedding_lookup(embedding, inputs)
cell_fw = tf.contrib.rnn.BasicLSTMCell(n_hidden)
cell_bw = tf.contrib.rnn.BasicLSTMCell(n_hidden)
outputs, _ = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs_embedded, dtype=tf.float32)
output_fw, output_bw = outputs
outputs_concat = tf.concat([output_fw, output_bw], axis=-1)
last_output = outputs_concat[:, -1, :]
weights = tf.Variable(tf.truncated_normal([n_hidden * 2, n_classes], stddev=0.1))
biases = tf.Variable(tf.constant(0.1, shape=[n_classes]))
logits = tf.matmul(last_output, weights) + biases
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=targets))
train_op = tf.train.AdamOptimizer().minimize(loss)
predictions = tf.argmax(logits, axis=1)
accuracy = tf.reduce_mean(tf.cast(tf.equal(predictions, targets), tf.float32))
# 训练模型
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(n_epochs):
n_batches = int(X_train.shape[0] / batch_size)
for i in range(n_batches):
start = i * batch_size
end = (i + 1) * batch_size
batch_inputs = X_train[start:end]
batch_targets = y_train[start:end]
_, loss_value = sess.run([train_op, loss], feed_dict={inputs: batch_inputs, targets: batch_targets})
accuracy_value = sess.run(accuracy, feed_dict={inputs: X_test, targets: y_test})
print("Epoch %d, loss = %.4f, accuracy = %.4f" % (epoch + 1, loss_value, accuracy_value))
```
这是一个简单的SRNN模型,使用双向LSTM层和嵌入层。在训练过程中,我们使用Adam优化器并在每个epoch结束时计算测试集上的准确度。
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