Python rnn.BasicLSTMCell方法代码示例

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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'] 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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) 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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) 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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'] 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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'] 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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'] 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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'] 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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'] 

# 需要导入模块: from tensorflow.contrib import rnn [as 别名]
# 或者: from tensorflow.contrib.rnn import BasicLSTMCell [as 别名]
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']