在RNN中实现ADAM优化器的代码
时间: 2024-05-15 11:19:34 浏览: 124
以下是在RNN中实现ADAM优化器的代码示例:
```
import tensorflow as tf
learning_rate = 0.001
beta1 = 0.9
beta2 = 0.999
epsilon = 1e-8
# define RNN model
rnn_cell = tf.nn.rnn_cell.BasicRNNCell(num_units=hidden_size)
state = rnn_cell.zero_state(batch_size, tf.float32)
outputs = []
for i in range(sequence_length):
if i > 0:
tf.get_variable_scope().reuse_variables()
output, state = rnn_cell(inputs[:, i, :], state)
outputs.append(output)
output = tf.reshape(tf.concat(outputs, 1), [-1, hidden_size])
# define loss and optimizer
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=targets))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1, beta2=beta2, epsilon=epsilon)
grads_and_vars = optimizer.compute_gradients(loss)
capped_grads_and_vars = [(tf.clip_by_norm(grad, clip_norm=5), var) for grad, var in grads_and_vars]
train_op = optimizer.apply_gradients(capped_grads_and_vars)
```
在这个例子中,我们首先定义了一个基本的RNN模型,然后使用ADAM优化器来训练模型。我们指定了学习率、beta1、beta2和epsilon等参数。在优化器中,我们计算梯度并对梯度进行裁剪,然后使用apply_gradients函数将梯度应用到模型参数上。
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