How can one validate a Tensor Flow installation on Windows

Question

Is there a simple test that will confirm whether a Tensor Flow installation is valid once one has successfully installed it using pip install --upgrade tensorflow as per current instructions from the main Tensor Flow website?

I'm simply confused by subsequent instructions for making Tensor Flow work on Windows with Visual Studio and C++. Specifically, these undated instructions that indicate only Python 3.5 is compatible with Tensor Flow. In contrast, answers to this question seem to indicate that Python 3.6 will work, at least for the 64-bit installation. Is there something that will prove my installation based on Python 3.6 64-bit is valid, and that I can proceed?

score 3 · Accepted Answer · answered Jan 22 '18 at 03:41

3

TensorFlow versions 1.1.0 and higher have been compiled for Python 3.6 (as well as 3.5 in most cases).

You can check the current installation of TensorFlow using command:

python -c "import tensorflow as tf; print(tf.__version__)"

answered Jan 22 '18 at 03:41

James

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If that's the case, I wish the Tensor Flow folk who provided those instructions would edit them to state "Python 3.5 or later"... – omatai Jan 22 '18 at 04:02

score 0 · Answer 2 · answered Jan 22 '18 at 10:10

Recent versions of Tensorflow are fully working with Python 3.6 on windows.

You can try the command line proposed by @James, or if you want to try a more extensive script.

You will need to install scikit-learn and scipy if you don't already have them.

Script Source: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/learn/boston.py

#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
"""Example of DNNRegressor for Housing dataset."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
from sklearn import preprocessing

import tensorflow as tf


def main(unused_argv):
  # Load dataset
  boston = datasets.load_boston()
  x, y = boston.data, boston.target

  # Split dataset into train / test
  x_train, x_test, y_train, y_test = model_selection.train_test_split(
      x, y, test_size=0.2, random_state=42)

  # Scale data (training set) to 0 mean and unit standard deviation.
  scaler = preprocessing.StandardScaler()
  x_train = scaler.fit_transform(x_train)

  # Build 2 layer fully connected DNN with 10, 10 units respectively.
  feature_columns = [
      tf.feature_column.numeric_column('x', shape=np.array(x_train).shape[1:])]
  regressor = tf.estimator.DNNRegressor(
      feature_columns=feature_columns, hidden_units=[10, 10])

  # Train.
  train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={'x': x_train}, y=y_train, batch_size=1, num_epochs=None, shuffle=True)
  regressor.train(input_fn=train_input_fn, steps=2000)

  # Predict.
  x_transformed = scaler.transform(x_test)
  test_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={'x': x_transformed}, y=y_test, num_epochs=1, shuffle=False)
  predictions = regressor.predict(input_fn=test_input_fn)
  y_predicted = np.array(list(p['predictions'] for p in predictions))
  y_predicted = y_predicted.reshape(np.array(y_test).shape)

  # Score with sklearn.
  score_sklearn = metrics.mean_squared_error(y_predicted, y_test)
  print('MSE (sklearn): {0:f}'.format(score_sklearn))

  # Score with tensorflow.
  scores = regressor.evaluate(input_fn=test_input_fn)
  print('MSE (tensorflow): {0:f}'.format(scores['average_loss']))


if __name__ == '__main__':
  tf.app.run()

How can one validate a Tensor Flow installation on Windows

2 Answers2