content: #  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.
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  #   http://www.apache.org/licenses/LICENSE-2.0
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  #  Unless required by applicable law or agreed to in writing, software
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  """Example of using Estimator with multiple GPUs to distribute one model.
  
  This example only runs if you have multiple GPUs to assign to.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from sklearn import cross_validation
  from sklearn import datasets
  from sklearn import metrics
  import tensorflow as tf
  
  layers = tf.contrib.layers
  learn = tf.contrib.learn
  
  
  def my_model(features, target):
    """DNN with three hidden layers, and dropout of 0.1 probability.
  
    Note: If you want to run this example with multiple GPUs, Cuda Toolkit 7.0 and
    CUDNN 6.5 V2 from NVIDIA need to be installed beforehand.
  
    Args:
      features: `Tensor` of input features.
      target: `Tensor` of targets.
  
    Returns:
      Tuple of predictions, loss and training op.
    """
    # Convert the target to a one-hot tensor of shape (length of features, 3) and
    # with a on-value of 1 for each one-hot vector of length 3.
    target = tf.one_hot(target, 3, 1, 0)
  
    # Create three fully connected layers respectively of size 10, 20, and 10 with
    # each layer having a dropout probability of 0.1.
    normalizer_fn = layers.dropout
    normalizer_params = {'keep_prob': 0.5}
    with tf.device('/gpu:1'):
      features = layers.stack(
          features,
          layers.fully_connected, [10, 20, 10],
          normalizer_fn=normalizer_fn,
          normalizer_params=normalizer_params)
  
    with tf.device('/gpu:2'):
      # Compute logits (1 per class) and compute loss.
      logits = layers.fully_connected(features, 3, activation_fn=None)
      loss = tf.losses.softmax_cross_entropy(target, logits)
  
      # Create a tensor for training op.
      train_op = tf.contrib.layers.optimize_loss(
          loss,
          tf.contrib.framework.get_global_step(),
          optimizer='Adagrad',
          learning_rate=0.1)
  
    return ({
        'class': tf.argmax(logits, 1),
        'prob': tf.nn.softmax(logits)
    }, loss, train_op)
  
  
  def main(unused_argv):
    iris = datasets.load_iris()
    x_train, x_test, y_train, y_test = cross_validation.train_test_split(
        iris.data, iris.target, test_size=0.2, random_state=42)
  
    classifier = learn.Estimator(model_fn=my_model)
    classifier.fit(x_train, y_train, steps=1000)
  
    y_predicted = [
        p['class'] for p in classifier.predict(
            x_test, as_iterable=True)
    ]
    score = metrics.accuracy_score(y_test, y_predicted)
    print('Accuracy: {0:f}'.format(score))
  
  
  if __name__ == '__main__':
    tf.app.run()
  