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Error in building a LSTM model due to tf version
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I am building a LSTM model to predict the evolution of time series but since I am refering to some previous version of tensorflow in my sources I have an error.
My code is :
from tensorflow.contrib import rnn
def LSTM(x):
layer = {'weights':tf.Variable(tf.random_normal([rnn_size, 1])),'biases':tf.Variable(tf.random_normal([1]))}
x = tf.transpose(x, [1,0,2])
x = tf.reshape(x, [-1, chunk_size])
x = tf.split(x, n_chunks, 0)
lstm_cell = tf.nn.rnn_cell.LSTMCell(rnn_size, state_is_tuple=True, activation=tf.nn.relu)
outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)
output = tf.matmul(outputs[-1], layer['weights']) + layer['biases']
return output
And I end up with the error message:
module 'tensorflow.contrib.rnn' has no attribute 'rnn'
How can I modify my code to make it work with tensorflow 1.12.0 version?
python tensorflow lstm recurrent-neural-network
asked Jan 3 at 13:00
ASUCBASUCB
163
add a comment |
I am building a LSTM model to predict the evolution of time series but since I am refering to some previous version of tensorflow in my sources I have an error.
My code is :
from tensorflow.contrib import rnn
def LSTM(x):
layer = {'weights':tf.Variable(tf.random_normal([rnn_size, 1])),'biases':tf.Variable(tf.random_normal([1]))}
x = tf.transpose(x, [1,0,2])
x = tf.reshape(x, [-1, chunk_size])
x = tf.split(x, n_chunks, 0)
lstm_cell = tf.nn.rnn_cell.LSTMCell(rnn_size, state_is_tuple=True, activation=tf.nn.relu)
outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)
output = tf.matmul(outputs[-1], layer['weights']) + layer['biases']
return output
And I end up with the error message:
module 'tensorflow.contrib.rnn' has no attribute 'rnn'
How can I modify my code to make it work with tensorflow 1.12.0 version?
python tensorflow lstm recurrent-neural-network
asked Jan 3 at 13:00
ASUCBASUCB
163
add a comment |
I am building a LSTM model to predict the evolution of time series but since I am refering to some previous version of tensorflow in my sources I have an error.
My code is :
from tensorflow.contrib import rnn
def LSTM(x):
layer = {'weights':tf.Variable(tf.random_normal([rnn_size, 1])),'biases':tf.Variable(tf.random_normal([1]))}
x = tf.transpose(x, [1,0,2])
x = tf.reshape(x, [-1, chunk_size])
x = tf.split(x, n_chunks, 0)
lstm_cell = tf.nn.rnn_cell.LSTMCell(rnn_size, state_is_tuple=True, activation=tf.nn.relu)
outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)
output = tf.matmul(outputs[-1], layer['weights']) + layer['biases']
return output
And I end up with the error message:
module 'tensorflow.contrib.rnn' has no attribute 'rnn'
How can I modify my code to make it work with tensorflow 1.12.0 version?
python tensorflow lstm recurrent-neural-network
asked Jan 3 at 13:00
ASUCBASUCB
163
I am building a LSTM model to predict the evolution of time series but since I am refering to some previous version of tensorflow in my sources I have an error.
My code is :
from tensorflow.contrib import rnn
def LSTM(x):
layer = {'weights':tf.Variable(tf.random_normal([rnn_size, 1])),'biases':tf.Variable(tf.random_normal([1]))}
x = tf.transpose(x, [1,0,2])
x = tf.reshape(x, [-1, chunk_size])
x = tf.split(x, n_chunks, 0)
lstm_cell = tf.nn.rnn_cell.LSTMCell(rnn_size, state_is_tuple=True, activation=tf.nn.relu)
outputs, states = rnn.rnn(lstm_cell, x, dtype=tf.float32)
output = tf.matmul(outputs[-1], layer['weights']) + layer['biases']
return output
And I end up with the error message:
module 'tensorflow.contrib.rnn' has no attribute 'rnn'
How can I modify my code to make it work with tensorflow 1.12.0 version?
python tensorflow lstm recurrent-neural-network
python tensorflow lstm recurrent-neural-network
asked Jan 3 at 13:00
ASUCBASUCB
163
asked Jan 3 at 13:00
ASUCBASUCB
163
asked Jan 3 at 13:00
ASUCBASUCB
163
asked Jan 3 at 13:00
ASUCBASUCB
163
asked Jan 3 at 13:00
ASUCBASUCB
163
163
add a comment |
add a comment |
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