本文整理匯總了Python中tensorflow.contrib.cudnn_rnn.python.ops.cudnn_rnn_ops.CudnnGRU方法的典型用法代碼示例。如果您正苦於以下問題：Python cudnn_rnn_ops.CudnnGRU方法的具體用法？Python cudnn_rnn_ops.CudnnGRU怎麽用？Python cudnn_rnn_ops.CudnnGRU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tensorflow.contrib.cudnn_rnn.python.ops.cudnn_rnn_ops的用法示例。
在下文中一共展示了cudnn_rnn_ops.CudnnGRU方法的2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: create_symbol
# 需要導入模塊: from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops [as 別名]
# 或者: from tensorflow.contrib.cudnn_rnn.python.ops.cudnn_rnn_ops import CudnnGRU [as 別名]
def create_symbol(X, num_classes=0, is_training=False, CUDNN=False,
maxf=30000, edim=125, nhid=100, batchs=64):
word_vectors = tf.contrib.layers.embed_sequence(X, vocab_size=maxf, embed_dim=edim)
word_list = tf.unstack(word_vectors, axis=1)
if not CUDNN:
cell1 = tf.contrib.rnn.LSTMCell(nhid)
cell2 = tf.contrib.rnn.GRUCell(nhid)
stacked_cell = tf.nn.rnn_cell.MultiRNNCell([cell1, cell2])
outputs, states = tf.nn.static_rnn(stacked_cell, word_list, dtype=tf.float32)
logits = tf.layers.dense(outputs[-1], 2, activation=None, name='output')
else:
# Using cuDNN since vanilla RNN
from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
cudnn_cell = cudnn_rnn_ops.CudnnGRU(num_layers=1,
num_units=nhid,
input_size=edim,
input_mode='linear_input')
params_size_t = cudnn_cell.params_size()
params = tf.Variable(tf.random_uniform([params_size_t], -0.1, 0.1), validate_shape=False)
input_h = tf.Variable(tf.zeros([1, batchs, nhid]))
outputs, states = cudnn_cell(input_data=word_list,
input_h=input_h,
params=params)
logits = tf.layers.dense(outputs[-1], 2, activation=None, name='output')
return logits, logits 示例2: build
# 需要導入模塊: from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops [as 別名]
# 或者: from tensorflow.contrib.cudnn_rnn.python.ops.cudnn_rnn_ops import CudnnGRU [as 別名]
def build(self, input_shape):
super(CuDNNGRU, self).build(input_shape)
if isinstance(input_shape, list):
input_shape = input_shape[0]
input_dim = input_shape[-1]
from tensorflow.contrib.cudnn_rnn.python.ops import cudnn_rnn_ops
self._cudnn_gru = cudnn_rnn_ops.CudnnGRU(
num_layers=1,
num_units=self.units,
input_size=input_dim,
input_mode='linear_input')
self.kernel = self.add_weight(shape=(input_dim, self.units * 3),
name='kernel',
initializer=self.kernel_initializer,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.recurrent_kernel = self.add_weight(
shape=(self.units, self.units * 3),
name='recurrent_kernel',
initializer=self.recurrent_initializer,
regularizer=self.recurrent_regularizer,
constraint=self.recurrent_constraint)
self.bias = self.add_weight(shape=(self.units * 6,),
name='bias',
initializer=self.bias_initializer,
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
self.kernel_z = self.kernel[:, :self.units]
self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units]
self.kernel_r = self.kernel[:, self.units: self.units * 2]
self.recurrent_kernel_r = self.recurrent_kernel[:,
self.units:
self.units * 2]
self.kernel_h = self.kernel[:, self.units * 2:]
self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:]
self.bias_z_i = self.bias[:self.units]
self.bias_r_i = self.bias[self.units: self.units * 2]
self.bias_h_i = self.bias[self.units * 2: self.units * 3]
self.bias_z = self.bias[self.units * 3: self.units * 4]
self.bias_r = self.bias[self.units * 4: self.units * 5]
self.bias_h = self.bias[self.units * 5:]
self.built = True