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Issue:

I have just started to learn tensor flow, what i have done is i have used window, powershell and executing following program , via -master> python .\execute.py

 import tensorflow as tf

hello = tf.constant('Welcome to tensorflow')

sess = tf.Session()

2017-01-16 12:11:02.578204: I C:\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

Traceback (most recent call last):

File ".\execute.py", line 322, in

decode()

File ".\execute.py", line 211, in decode

model = create_model(sess, True)

File ".\execute.py", line 104, in create_model

model = seq2seq_model.Seq2SeqModel( gConfig['enc_vocab_size'], gConfig['dec_vocab_size'], _buckets, gConfig['layer_size'], gConfig['num_layers'], gConfig['max_gradient_norm'], gConfig['batch_size'], gConfig['learning_rate'], gConfig['learning_rate_decay_factor'], forward_only=forward_only)

File "C:\Users\user\Desktop\new\python source\PycharmProjects\untitled2\tensor\tensorflow_chatbot-master\tensorflow_chatbot-master\seq2seq_model.py", line 142, in init

self.outputs, self.losses = tf.nn.seq2seq.model_with_buckets(

AttributeError: module 'tensorflow.python.ops.nn' has no attribute 'seq2seq'

by (720 points)  
edited by

1 Answer

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Warning Cause? 

After TensorFlow 1.6, the pairs presently use AVX guidelines which may not keep running on more seasoned CPUs any longer. So the more established CPUs will be not able run the AVX, while for the more up to date ones, the client needs to manufacture the tensorflow from hotspot for their CPU. The following is all the data you have to think about this specific cautioning. Likewise, a technique about disposing of this notice for sometime later.

Solution:

Use following command to get latest version of tensorflow binary on your CPU & OS 

pip install --ignore-installed --upgrade " URL"

You can download on your pc and use this command too:

pip install --ignore-installed --upgrade /path/target.whl

by (4k points)  
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