本文整理匯總了Python中keras.layers.Dense.get_weights方法的典型用法代碼示例。如果您正苦於以下問題：Python Dense.get_weights方法的具體用法？Python Dense.get_weights怎麽用？Python Dense.get_weights使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類keras.layers.Dense的用法示例。
在下文中一共展示了Dense.get_weights方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: Attention
# 需要導入模塊: from keras.layers import Dense [as 別名]
# 或者: from keras.layers.Dense import get_weights [as 別名]
class Attention(Model):
"""Implements standard attention.
Given some memory, a memory mask and a query, outputs the weighted memory cells.
"""
def __init__(self, memory_cells, query, project_query=False):
"""Define Attention.
Args:
memory_cells (SequenceBatch): a SequenceBatch containing a Tensor of shape (batch_size, num_cells, cell_dim)
query (Tensor): a tensor of shape (batch_size, query_dim).
project_query (bool): defaults to False. If True, the query goes through an extra projection layer to
coerce it to cell_dim.
"""
cell_dim = memory_cells.values.get_shape().as_list()[2]
if project_query:
# project the query up/down to cell_dim
self._projection_layer = Dense(cell_dim, activation='linear')
query = self._projection_layer(query) # (batch_size, cand_dim)
memory_values, memory_mask = memory_cells.values, memory_cells.mask
# batch matrix multiply to compute logit scores for all choices in all batches
query = tf.expand_dims(query, 2) # (batch_size, cell_dim, 1)
logit_values = tf.batch_matmul(memory_values, query) # (batch_size, num_cells, 1)
logit_values = tf.squeeze(logit_values, [2]) # (batch_size, num_cells)
# set all pad logits to negative infinity
logits = SequenceBatch(logit_values, memory_mask)
logits = logits.with_pad_value(-float('inf'))
# normalize to get probs
probs = tf.nn.softmax(logits.values) # (batch_size, num_cells)
retrieved = tf.batch_matmul(tf.expand_dims(probs, 1), memory_values) # (batch_size, 1, cell_dim)
retrieved = tf.squeeze(retrieved, [1]) # (batch_size, cell_dim)
self._logits = logits.values
self._probs = probs
self._retrieved = retrieved
@property
def logits(self):
return self._logits # (batch_size, num_cells)
@property
def probs(self):
return self._probs # (batch_size, num_cells)
@property
def retrieved(self):
return self._retrieved # (batch_size, cell_dim)
@property
def projection_weights(self):
"""Get projection weights.
Returns:
(np.array, np.array): a pair of numpy arrays, (W, b) used to project the query tensor to
match the predicate embedding dimension.
"""
return self._projection_layer.get_weights()
@projection_weights.setter
def projection_weights(self, value):
W, b = value
self._projection_layer.set_weights([W, b])