Python rnn.static_rnn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 static_rnn [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 static_rnn [as 别名]
def rnn_seq2seq(encoder_inputs,
                decoder_inputs,
                encoder_cell,
                decoder_cell=None,
                dtype=dtypes.float32,
                scope=None):
  """RNN Sequence to Sequence model.

  Args:
    encoder_inputs: List of tensors, inputs for encoder.
    decoder_inputs: List of tensors, inputs for decoder.
    encoder_cell: RNN cell to use for encoder.
    decoder_cell: RNN cell to use for decoder, if None encoder_cell is used.
    dtype: Type to initialize encoder state with.
    scope: Scope to use, if None new will be produced.

  Returns:
    List of tensors for outputs and states for trianing and sampling sub-graphs.
  """
  with vs.variable_scope(scope or "rnn_seq2seq"):
    _, last_enc_state = rnn.static_rnn(
        encoder_cell, encoder_inputs, dtype=dtype)
    return rnn_decoder(decoder_inputs, last_enc_state, decoder_cell or
                       encoder_cell) 

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