本文整理汇总了Python中blocks.bricks.recurrent.LSTM.initial_states方法的典型用法代码示例。如果您正苦于以下问题：Python LSTM.initial_states方法的具体用法？Python LSTM.initial_states怎么用？Python LSTM.initial_states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blocks.bricks.recurrent.LSTM的用法示例。
在下文中一共展示了LSTM.initial_states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: RNN
# 需要导入模块: from blocks.bricks.recurrent import LSTM [as 别名]
# 或者: from blocks.bricks.recurrent.LSTM import initial_states [as 别名]
class RNN(Initializable):
@lazy()
def __init__(self, config, rec_input_len=2, output_dim=2, **kwargs):
super(RNN, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder')
in1 = rec_input_len + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec')
self.rec = LSTM(
dim = config.hidden_state_dim,
name = 'recurrent'
)
in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, output_dim], name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [ self.pre_context_embedder, self.post_context_embedder, self.input_to_rec, self.rec, self.rec_to_output ]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_cells")
def _push_initialization_config(self):
for mlp in [self.input_to_rec, self.rec_to_output]:
mlp.weights_init = self.config.weights_init
mlp.biases_init = self.config.biases_init
self.rec.weights_init = self.config.weights_init
def get_dim(self, name):
return self.rec.get_dim(name)
def process_rto(self, rto):
return rto
def rec_input(self, latitude, longitude, **kwargs):
return (tensor.shape_padright(latitude), tensor.shape_padright(longitude))
@recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells'])
def predict_all(self, **kwargs):
pre_emb = tuple(self.pre_context_embedder.apply(**kwargs))
itr_in = tensor.concatenate(pre_emb + self.rec_input(**kwargs), axis=1)
itr = self.input_to_rec.apply(itr_in)
itr = itr.repeat(4, axis=1)
(next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=kwargs['latitude_mask'], iterate=False)
post_emb = tuple(self.post_context_embedder.apply(**kwargs))
rto = self.rec_to_output.apply(tensor.concatenate(post_emb + (next_states,), axis=1))
rto = self.process_rto(rto)
return (rto, next_states, next_cells)
@predict_all.property('sequences')
def predict_all_sequences(self):
return self.sequences
@application(outputs=predict_all.states)
def initial_states(self, *args, **kwargs):
return self.rec.initial_states(*args, **kwargs)
@predict_all.property('contexts')
def predict_all_context(self):
return self.context
def before_predict_all(self, kwargs):
kwargs['latitude'] = (kwargs['latitude'].T - data.train_gps_mean[0]) / data.train_gps_std[0]
kwargs['longitude'] = (kwargs['longitude'].T - data.train_gps_mean[1]) / data.train_gps_std[1]
kwargs['latitude_mask'] = kwargs['latitude_mask'].T
@application(outputs=['destination'])
def predict(self, **kwargs):
self.before_predict_all(kwargs)
res = self.predict_all(**kwargs)[0]
last_id = tensor.cast(kwargs['latitude_mask'].sum(axis=0) - 1, dtype='int64')
return res[last_id, tensor.arange(kwargs['latitude_mask'].shape[1])]
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost_matrix'])
def cost_matrix(self, **kwargs):
self.before_predict_all(kwargs)
res = self.predict_all(**kwargs)[0]
target = tensor.concatenate(
(kwargs['destination_latitude'].dimshuffle('x', 0, 'x'),
kwargs['destination_longitude'].dimshuffle('x', 0, 'x')),
axis=2)
target = target.repeat(kwargs['latitude'].shape[0], axis=0)
ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2)))
#.........这里部分代码省略.........