基于TensorFlow1.4实现的mmRNN的代码示例
时间: 2023-06-14 15:06:37 浏览: 63
以下是基于 TensorFlow1.4 实现的 mmRNN 的代码示例:
```python
import tensorflow as tf
class mmRNN(object):
def __init__(self, num_classes, num_steps, hidden_size, embedding_size, vocab_size, learning_rate):
self.inputs = tf.placeholder(tf.int32, [None, num_steps])
self.targets = tf.placeholder(tf.int32, [None, num_classes])
self.batch_size = tf.placeholder(tf.int32, [])
with tf.variable_scope("embedding"):
embedding = tf.get_variable("embedding", [vocab_size, embedding_size])
inputs = tf.nn.embedding_lookup(embedding, self.inputs)
inputs = tf.split(inputs, num_steps, 1)
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
with tf.variable_scope("mm_rnn"):
W_xh = tf.get_variable("W_xh", [embedding_size, hidden_size])
W_hh = tf.get_variable("W_hh", [hidden_size, hidden_size])
W_hy = tf.get_variable("W_hy", [hidden_size, num_classes])
b_h = tf.get_variable("b_h", [hidden_size])
b_y = tf.get_variable("b_y", [num_classes])
h_t = tf.zeros([self.batch_size, hidden_size])
for t in range(num_steps):
x_t = inputs[t]
h_t = tf.nn.tanh(tf.matmul(x_t, W_xh) + tf.matmul(h_t, W_hh) + b_h)
logits = tf.matmul(h_t, W_hy) + b_y
with tf.variable_scope("loss"):
self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=self.targets))
with tf.variable_scope("train"):
optimizer = tf.train.AdamOptimizer(learning_rate)
self.train_op = optimizer.minimize(self.loss)
with tf.variable_scope("predict"):
self.predictions = tf.argmax(tf.nn.softmax(logits), 1)
def train(self, sess, inputs, targets):
feed_dict = {self.inputs: inputs, self.targets: targets, self.batch_size: inputs.shape[0]}
loss, _ = sess.run([self.loss, self.train_op], feed_dict=feed_dict)
return loss
def predict(self, sess, inputs):
feed_dict = {self.inputs: inputs, self.batch_size: inputs.shape[0]}
predictions = sess.run(self.predictions, feed_dict=feed_dict)
return predictions
```
注意,在 TensorFlow1.4 中,`tf.contrib.rnn.MultiRNNCell` 方法已被废弃,因此我们需要手动实现多层 LSTM 或 GRU。在此示例中,我们只使用了单层 LSTM。
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