本文整理汇总了Python中layers.LSTM属性的典型用法代码示例。如果您正苦于以下问题：Python layers.LSTM属性的具体用法？Python layers.LSTM怎么用？Python layers.LSTM使用的例子？那么, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类layers的用法示例。
在下文中一共展示了layers.LSTM属性的3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: language_model_graph
# 需要导入模块: import layers [as 别名]
# 或者: from layers import LSTM [as 别名]
def language_model_graph(self, compute_loss=True):
"""Constructs LM graph from inputs to LM loss.
* Caches the VatxtInput object in `self.lm_inputs`
* Caches tensors: `lm_embedded`
Args:
compute_loss: bool, whether to compute and return the loss or stop after
the LSTM computation.
Returns:
loss: scalar float.
"""
inputs = _inputs('train', pretrain=True)
self.lm_inputs = inputs
return self._lm_loss(inputs, compute_loss=compute_loss) 示例2: __init__
# 需要导入模块: import layers [as 别名]
# 或者: from layers import LSTM [as 别名]
def __init__(self, cl_logits_input_dim=None):
self.global_step = tf.contrib.framework.get_or_create_global_step()
self.vocab_freqs = _get_vocab_freqs()
# Cache VatxtInput objects
self.cl_inputs = None
self.lm_inputs = None
# Cache intermediate Tensors that are reused
self.tensors = {}
# Construct layers which are reused in constructing the LM and
# Classification graphs. Instantiating them all once here ensures that
# variable reuse works correctly.
self.layers = {}
self.layers['embedding'] = layers_lib.Embedding(
FLAGS.vocab_size, FLAGS.embedding_dims, FLAGS.normalize_embeddings,
self.vocab_freqs, FLAGS.keep_prob_emb)
self.layers['lstm'] = layers_lib.LSTM(
FLAGS.rnn_cell_size, FLAGS.rnn_num_layers, FLAGS.keep_prob_lstm_out)
self.layers['lm_loss'] = layers_lib.SoftmaxLoss(
FLAGS.vocab_size,
FLAGS.num_candidate_samples,
self.vocab_freqs,
name='LM_loss')
cl_logits_input_dim = cl_logits_input_dim or FLAGS.rnn_cell_size
self.layers['cl_logits'] = layers_lib.cl_logits_subgraph(
[FLAGS.cl_hidden_size] * FLAGS.cl_num_layers, cl_logits_input_dim,
FLAGS.num_classes, FLAGS.keep_prob_cl_hidden) 示例3: __init__
# 需要导入模块: import layers [as 别名]
# 或者: from layers import LSTM [as 别名]
def __init__(self, cl_logits_input_dim=None):
self.global_step = tf.train.get_or_create_global_step()
self.vocab_freqs = _get_vocab_freqs()
# Cache VatxtInput objects
self.cl_inputs = None
self.lm_inputs = None
# Cache intermediate Tensors that are reused
self.tensors = {}
# Construct layers which are reused in constructing the LM and
# Classification graphs. Instantiating them all once here ensures that
# variable reuse works correctly.
self.layers = {}
self.layers['embedding'] = layers_lib.Embedding(
FLAGS.vocab_size, FLAGS.embedding_dims, FLAGS.normalize_embeddings,
self.vocab_freqs, FLAGS.keep_prob_emb)
self.layers['lstm'] = layers_lib.LSTM(
FLAGS.rnn_cell_size, FLAGS.rnn_num_layers, FLAGS.keep_prob_lstm_out)
self.layers['lm_loss'] = layers_lib.SoftmaxLoss(
FLAGS.vocab_size,
FLAGS.num_candidate_samples,
self.vocab_freqs,
name='LM_loss')
cl_logits_input_dim = cl_logits_input_dim or FLAGS.rnn_cell_size
self.layers['cl_logits'] = layers_lib.cl_logits_subgraph(
[FLAGS.cl_hidden_size] * FLAGS.cl_num_layers, cl_logits_input_dim,
FLAGS.num_classes, FLAGS.keep_prob_cl_hidden)