本文整理匯總了Python中tensorflow.contrib.rnn.BasicLSTMCell方法的典型用法代碼示例。如果您正苦於以下問題:Python rnn.BasicLSTMCell方法的具體用法?Python rnn.BasicLSTMCell怎麽用?Python rnn.BasicLSTMCell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.rnn
的用法示例。
在下文中一共展示了rnn.BasicLSTMCell方法的10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: RNN
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def RNN(x, weights, biases):
# reshape to [1, n_input]
x = tf.reshape(x, [-1, n_input])
# Generate a n_input-element sequence of inputs
# (eg. [had] [a] [general] -> [20] [6] [33])
x = tf.split(x, n_input, 1)
# 2-layer LSTM, each layer has n_hidden units.
# Average Accuracy= 95.20% at 50k iter
rnn_cell = rnn.MultiRNNCell([rnn.BasicLSTMCell(n_hidden), rnn.BasicLSTMCell(n_hidden)])
# 1-layer LSTM with n_hidden units but with lower accuracy.
# Average Accuracy= 90.60% 50k iter
# Uncomment line below to test but comment out the 2-layer rnn.MultiRNNCell above
# rnn_cell = rnn.BasicLSTMCell(n_hidden)
# generate prediction
outputs, states = rnn.static_rnn(rnn_cell, x, dtype=tf.float32)
# there are n_input outputs but
# we only want the last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
示例2: __init__
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def __init__(self,
num_units,
forget_bias=1,
state_is_tuple=True,
output_is_tuple=True):
def cell_fn(n):
return rnn.BasicLSTMCell(num_units=n, forget_bias=forget_bias)
super(Grid1BasicLSTMCell, self).__init__(
num_units=num_units,
num_dims=1,
input_dims=0,
output_dims=0,
priority_dims=0,
tied=False,
cell_fn=cell_fn,
state_is_tuple=state_is_tuple,
output_is_tuple=output_is_tuple)
示例3: bidirectionnal_rnn
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def bidirectionnal_rnn(self, input_tensor, input_sequence_length):
lstm_num_units = self.config.lstm_num_units
print("rnn input tensor ===> ", input_tensor)
with tf.variable_scope('lstm_layers'):
fw_cell_list = [rnn.BasicLSTMCell(nh, forget_bias=1.0, name='fw_cell_%d'%(nh)) for nh in [lstm_num_units] * 2]
bw_cell_list = [rnn.BasicLSTMCell(nh, forget_bias=1.0, name='bw_cell_%d'%(nh)) for nh in [lstm_num_units] * 2]
stack_lstm_layer, _, _ = rnn.stack_bidirectional_dynamic_rnn(
cells_fw=fw_cell_list,
cells_bw=bw_cell_list,
inputs=input_tensor,
sequence_length=input_sequence_length,
dtype=tf.float32)
hidden_num = lstm_num_units * 2
rnn_reshaped = tf.nn.dropout(stack_lstm_layer, keep_prob=self.keep_prob)
w = tf.get_variable(initializer=tf.truncated_normal([hidden_num, self.num_classes], stddev=0.02), name="w")
w_t = tf.tile(tf.expand_dims(w, 0),[self.batch_size,1,1])
logits = tf.matmul(rnn_reshaped, w_t, name="nn_logits")
self.logits = tf.identity(tf.transpose(logits, (1, 0, 2)), name='logits')
return logits
示例4: build_lstm
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def build_lstm(self):
def build_cell():
cell = rnn.BasicLSTMCell(self._hidden_size, forget_bias=1.0, state_is_tuple=True)
cell = rnn.DropoutWrapper(cell, output_keep_prob=self._keep_prob)
return cell
mul_cell = rnn.MultiRNNCell([build_cell() for _ in range(self._num_layer)],
state_is_tuple=True)
self._init_state = mul_cell.zero_state(self._num_seq, dtype=tf.float32)
outputs, self._final_state = tf.nn.dynamic_rnn(mul_cell, self._inputs,
initial_state=self._init_state)
outputs = tf.reshape(outputs, [-1, self._hidden_size])
W = tf.Variable(tf.truncated_normal([self._hidden_size, self._corpus.word_num],
stddev=0.1, dtype=tf.float32))
bais = tf.Variable(tf.zeros([1, self._corpus.word_num],
dtype=tf.float32), dtype=tf.float32)
self._prediction = tf.nn.softmax(tf.matmul(outputs, W) + bais)
示例5: RNN
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def RNN(x, weights, biases):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, timesteps, n_input)
# Required shape: 'timesteps' tensors list of shape (batch_size, n_input)
# Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)
with tf.name_scope('RNN'):
x = tf.unstack(x, timesteps, 1)
variable_summaries(x)
# Define a lstm cell with tensorflow
lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
#variable_summaries(lstm_cell)
# Get lstm cell output
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
#variable_summaries(outputs)
#variable_summaries(states)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
示例6: LSTMs
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def LSTMs(x, weights, biases, timesteps , num_hidden):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, timesteps, n_input)
# Required shape: 'timesteps' tensors list of shape (batch_size, n_input)
# Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)
x = tf.unstack(x, timesteps, 1)
# Define a lstm cell with tensorflow
lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
# Get lstm cell output
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
示例7: RNN
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def RNN(x, weights, biases, timesteps , num_hidden):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, timesteps, n_input)
# Required shape: 'timesteps' tensors list of shape (batch_size, n_input)
# Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)
x = tf.unstack(x, timesteps, 1)
# Define a lstm cell with tensorflow
lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
# Get lstm cell output
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
示例8: RNN
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def RNN(x, weights, biases):
timesteps = 28 # timesteps
num_hidden = 128 # hidden layer num of features
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, timesteps, n_input)
# Required shape: 'timesteps' tensors list of shape (batch_size, n_input)
# Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)
x = tf.unstack(x, timesteps, 1)
# Define a lstm cell with tensorflow
lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
# Get lstm cell output
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
示例9: RNN
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def RNN(x, weights, biases):
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, timesteps, n_input)
# Required shape: 'timesteps' tensors list of shape (batch_size, n_input)
# Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)
x = tf.unstack(x, timesteps, 1)
# Define a lstm cell with tensorflow
lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
# Get lstm cell output
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']
示例10: RNN
# 需要導入模塊: from tensorflow.contrib import rnn [as 別名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 別名]
def RNN(x, weights, biases):
timesteps = 83 # timesteps
num_hidden = 128 # hidden layer num of features
# Prepare data shape to match `rnn` function requirements
# Current data input shape: (batch_size, timesteps, n_input)
# Required shape: 'timesteps' tensors list of shape (batch_size, n_input)
# Unstack to get a list of 'timesteps' tensors of shape (batch_size, n_input)
x = tf.unstack(x, timesteps, 1)
# Define a lstm cell with tensorflow
lstm_cell = rnn.BasicLSTMCell(num_hidden, forget_bias=1.0)
# Get lstm cell output
outputs, states = rnn.static_rnn(lstm_cell, x, dtype=tf.float32)
# Linear activation, using rnn inner loop last output
return tf.matmul(outputs[-1], weights['out']) + biases['out']