本文整理汇总了Python中tensorflow.contrib.legacy_seq2seq.rnn_decoder方法的典型用法代码示例。如果您正苦于以下问题：Python legacy_seq2seq.rnn_decoder方法的具体用法？Python legacy_seq2seq.rnn_decoder怎么用？Python legacy_seq2seq.rnn_decoder使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.contrib.legacy_seq2seq的用法示例。
在下文中一共展示了legacy_seq2seq.rnn_decoder方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from tensorflow.contrib import legacy_seq2seq [as 别名]
# 或者: from tensorflow.contrib.legacy_seq2seq import rnn_decoder [as 别名]
def __init__(self, args, training=True):
"""Initialize RNN model"""
self.args = args
if not training:
args.batch_size = 1
args.seq_length = 1
cell_fn = rnn.GRUCell
cells = [cell_fn(args.rnn_size) for _ in range(args.num_layers)]
self.cell = cell = rnn.MultiRNNCell(cells, state_is_tuple=True)
self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
self.targets = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
self.initial_state = cell.zero_state(args.batch_size, tf.float32)
with tf.variable_scope('rnn'):
softmax_w = tf.get_variable("softmax_w", [args.rnn_size, args.vocab_size])
softmax_b = tf.get_variable("softmax_b", [args.vocab_size])
embedding = tf.get_variable("embedding", [args.vocab_size, args.rnn_size])
inputs = tf.nn.embedding_lookup(embedding, self.input_data)
inputs = tf.split(inputs, args.seq_length, 1)
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
def loop(prev, _):
prev = tf.matmul(prev, softmax_w) + softmax_b
prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
return tf.nn.embedding_lookup(embedding, prev_symbol)
outputs, last_state = legacy_seq2seq.rnn_decoder(inputs, self.initial_state, cell,
loop_function=loop if not training else None, scope='rnnlm')
output = tf.reshape(tf.concat(outputs, 1), [-1, args.rnn_size])
self.logits = tf.nn.xw_plus_b(output, softmax_w, softmax_b)
self.probs = tf.nn.softmax(self.logits)
loss = legacy_seq2seq.sequence_loss_by_example(
[self.logits],
[tf.reshape(self.targets, [-1])],
[tf.ones([args.batch_size * args.seq_length])])
with tf.name_scope('loss'):
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
self.final_state = last_state
self.learning_rate = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars), args.grad_clip)
with tf.name_scope('optimization'):
optimizer = tf.train.AdamOptimizer(self.learning_rate)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))