如何在 Windows 上验证 Tensor Flow 安装答案

【问题标题】：How can one validate a Tensor Flow installation on Windows如何在 Windows 上验证 Tensor Flow 安装
【发布时间】：2018-06-30 15:46:07
【问题描述】：

如果按照the main Tensor Flow website 的当前说明使用pip install --upgrade tensorflow 成功安装Tensor Flow，是否有一个简单的测试可以确认它是否有效？

我只是对随后的关于使Tensor Flow 使用 Visual Studio 和 C++ 在 Windows 上工作的说明感到困惑。具体来说，these undated instructions 表示只有 Python 3.5 与Tensor Flow 兼容。相比之下，this question 的答案似乎表明 Python 3.6 可以工作，至少对于 64 位安装。有什么东西可以证明我基于 Python 3.6 64 位的安装是有效的，并且我可以继续吗？

【问题讨论】：

标签： python windows tensorflow

【解决方案1】：

TensorFlow 1.1.0 及更高版本已针对 Python 3.6（以及大多数情况下的 3.5）进行编译。

您可以使用以下命令检查 TensorFlow 的当前安装：

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

【讨论】：

如果是这样的话，我希望提供这些说明的 Tensor Flow 人员能够将它们编辑为“Python 3.5 或更高版本”...

【解决方案2】：

Tensorflow 的最新版本在 Windows 上与 Python 3.6 完全兼容。

您可以尝试@James 提出的命令行，或者如果您想尝试更广泛的脚本。

如果您还没有 scikit-learn 和 scipy，则需要安装它们。

脚本来源：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()

【讨论】：