Trying Run Python Code Importing Tnsorflow Installed Cpu Version Gpu Version Dd Go Well Li Q26132280

I have been trying to run a python code by importing TnsorFlow.I had installed a cpu version my GPU version dd not go through verywell

This is a linear regression code

When I tried to run it it gave me errors. I have attached thecode file as well as Error file

Can a expert help me in trouble shooting the error?


# -*- coding: utf-8 -*-
Spyder Editor

This is a temporary script file.
import pandas as pd
import numpy as np
from random import sample
from sklearn.metrics import r2_score
import os.path
from sklearn.preprocessing import LabelEncoder
import tensorflow as tf

TotalFeatures = 376 # we have ‘376’ features
FeaturesInUse = 150 # number of features used for predictionstarting from feature[0]
TotalSampleSize = 4209 # there are total 4209 trainingsamples.
MiniTestSampleSize = 409 # Lets keep ‘MiniTestSampleSize’ samplesas test data

# subroutine for input data processing
def PreProcessInputData(test_data, train_data) :
# first lets process columns, apply LabelEncoder to map features tointegers
# thanks to :
for c in train_data.columns:
if train_data[c].dtype == ‘object’:
lbl = LabelEncoder()[c].values) +list(test_data[c].values))
train_data[c] = lbl.transform(list(train_data[c].values))
test_data[c] = lbl.transform(list(test_data[c].values))
# you may normalize values here if you want, but again, it didn’thelped me much
#maxVal = np.amax(pd.concat([test_data[c],train_data[c]],ignore_index=True))
#minVal = np.amin(pd.concat([test_data[c],train_data[c]],ignore_index=True))
#train_data[c] = (train_data[c] – minVal + 1)/(maxVal + 1)
#test_data[c] = (test_data[c] – minVal + 1)/(maxVal + 1)

y_train_all = train_data[‘y’].as_matrix();
y_train_all = np.reshape(y_train_all, (-1, 1));

x_train_all = train_data.drop([‘y’,’ID’] , axis = 1);
x_train_all =x_train_all.drop(x_train_all.columns[FeaturesInUse:TotalFeatures],axis = 1); # drop lat few features to keep first ‘FeaturesInUse’features
x_train_all = x_train_all.as_matrix();
x_train_all = np.reshape(x_train_all, (-1, FeaturesInUse));

# let’s randomly pick ‘MiniTestSampleSize’ samples and keep themas mini test data
indices = sample(range(len(y_train_all)),MiniTestSampleSize)
# our mini test data
y_mini_test = y_train_all[indices]
x_mini_test = x_train_all[indices]

# remaining is out training data
y_train = np.delete(y_train_all, indices, axis=0)
x_train = np.delete(x_train_all, indices, axis=0)

# lets processes master test sample data
x_test_ID = test_data[‘ID’].as_matrix();
x_test = test_data.drop(‘ID’, axis = 1);
x_test = x_test.drop(x_test.columns[FeaturesInUse:TotalFeatures],axis = 1);
x_test = x_test.as_matrix();
x_test = np.reshape(x_test, (-1, FeaturesInUse));

return (x_train, y_train, x_mini_test, y_mini_test, x_test_ID,x_test)
# read input files

# process input data
# x_data, y_data : our training set
# x_mini_test, and y_mini_test are our mini test samples to testconvergence during training
# test_data_ID and test_data are to be used predict y
(x_data, y_data, x_mini_test, y_mini_test, test_data_ID, x_test) =PreProcessInputData(test_data, train_data)

# now we can refer one row of input features as x_data[0],x_data[1]
x = tf.placeholder(tf.float32, [None, FeaturesInUse]);

# this is our model
W = tf.Variable(tf.truncated_normal(shape=[FeaturesInUse,1],stddev=0.1))
b = tf.Variable(tf.constant(0.1, shape=[1]))
y = tf.matmul(x, W) + b; # predicted values
y_ = tf.placeholder(tf.float32, [None, 1]); # true y values

cross_entropy = tf.reduce_mean(tf.square(y – y_)); # this is outcost function
# alternatively, we can define error function to be R^2, but itdidn’t helped me
#total_error = tf.reduce_sum(tf.square(tf.subtract(y_,tf.reduce_mean(y_))))
#unexplained_error = tf.reduce_sum(tf.square(tf.subtract(y_,y)))
#R_squared = tf.subtract(1.0, tf.div(total_error,unexplained_error))
#cross_entropy = R_squared

train_step = tf.train.AdamOptimizer().minimize(cross_entropy); #lets use adam optimizer

sess = tf.InteractiveSession(); # start the session

# Trainning loop
for j in range(2000):
for i in range( int((TotalSampleSize-MiniTestSampleSize)/200) ): #lets take 200 samples at time
batch_x = x_data[i*200:i*200+200]
batch_y = y_data[i*200:i*200+200], feed_dict={x: batch_x, y_: batch_y})
# let’s print prediction error for our mini test data
if 0 == (j%10) :
# test error
y_pred =, feed_dict={x: x_mini_test})
TestError = r2_score(y_mini_test, y_pred)
# training error
y_pred =, feed_dict={x: x_data})
TrainError = r2_score(y_data, y_pred)
# print
print(“for iteration : {:06d}”.format(j), ” <TestErr> :{:10.4f}”.format(TestError), ” <TrainError> :{:10.4f}”.format(TrainError))
# you might want to break loop here by some means…say you foundtest error starts increasing

# Test trained model now
y_pred =, feed_dict={x: x_test})

# store to submist.csv
sub = pd.DataFrame()
sub[‘ID’] = test_data_ID
sub[‘y’] = y_pred
sub.to_csv(‘submit.csv’, index=False)



Traceback (most recent call last):

File “<ipython-input-5-5cb79189c050>”, line 1, in<module>


File””,line 710, in runfile

    execfile(filename, namespace)

File””,line 101, in execfile

  exec(compile(, filename, ‘exec’),namespace)

File “D:/Python/Test1/”, line 13, in<module>

    import tensorflow as tf

File””,line 24, in <module>

    from tensorflow.python import *

File””,line 49, in <module>

    from tensorflow.python importpywrap_tensorflow

File””,line 72, in <module>

    raise ImportError(msg)

ImportError: Traceback (most recent call last):

File””,line 18, in swig_import_helper

    return importlib.import_module(mname)

File “”, line126, in import_module

    return _bootstrap._gcd_import(name[level:],package, level)

File “<frozen importlib._bootstrap>”, line 994, in_gcd_import

File “<frozen importlib._bootstrap>”, line 971, in_find_and_load

File “<frozen importlib._bootstrap>”, line 955, in_find_and_load_unlocked

File “<frozen importlib._bootstrap>”, line 658, in_load_unlocked

File “<frozen importlib._bootstrap>”, line 571, inmodule_from_spec

File “<frozen importlib._bootstrap_external>”, line 922,in create_module

File “<frozen importlib._bootstrap>”, line 219, in_call_with_frames_removed

ImportError: DLL load failed: The specified module could not befound.

During handling of the above exception, another exceptionoccurred:

Traceback (most recent call last):

File””,line 58, in <module>

    fromtensorflow.python.pywrap_tensorflow_internal import *

File””,line 21, in <module>

    _pywrap_tensorflow_internal =swig_import_helper()

File””,line 20, in swig_import_helper


File “”, line126, in import_module

    return _bootstrap._gcd_import(name[level:],package, level)

ModuleNotFoundError: No module named’_pywrap_tensorflow_internal’

Failed to load the native TensorFlow runtime.


for some common reasons and solutions. Include the entire stacktrace

above this error message when asking for help.

"We Offer Paper Writing Services on all Disciplines, Make an Order Now and we will be Glad to Help"
0 replies

Leave a Reply

Want to join the discussion?
Feel free to contribute!

Leave a Reply

Your email address will not be published.