#  Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#  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
#
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#  distributed under the License is distributed on an "AS IS" BASIS,
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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

import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn import model_selection
import tensorflow as tf


X_FEATURE = 'x'  # Name of the input feature.


def my_model(features, labels, mode):
  """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: Dict of input `Tensor`.
    labels: Label `Tensor`.
    mode: One of `ModeKeys`.

  Returns:
    `EstimatorSpec`.
  """
  # Create three fully connected layers respectively of size 10, 20, and 10 with
  # each layer having a dropout probability of 0.1.
  net = features[X_FEATURE]
  with tf.device('/gpu:1'):
    for units in [10, 20, 10]:
      net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
      net = tf.layers.dropout(net, rate=0.1)

  with tf.device('/gpu:2'):
    # Compute logits (1 per class).
    logits = tf.layers.dense(net, 3, activation=None)

    # Compute predictions.
    predicted_classes = tf.argmax(logits, 1)
    if mode == tf.estimator.ModeKeys.PREDICT:
      predictions = {
          'class': predicted_classes,
          'prob': tf.nn.softmax(logits)
      }
      return tf.estimator.EstimatorSpec(mode, predictions=predictions)

    # Convert the labels 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.
    onehot_labels = tf.one_hot(labels, 3, 1, 0)
    # Compute loss.
    loss = tf.losses.softmax_cross_entropy(
        onehot_labels=onehot_labels, logits=logits)

    # Create training op.
    if mode == tf.estimator.ModeKeys.TRAIN:
      optimizer = tf.train.AdagradOptimizer(learning_rate=0.1)
      train_op = optimizer.minimize(
          loss, global_step=tf.train.get_global_step())
      return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)

    # Compute evaluation metrics.
    eval_metric_ops = {
        'accuracy': tf.metrics.accuracy(
            labels=labels, predictions=predicted_classes)
    }
    return tf.estimator.EstimatorSpec(
        mode, loss=loss, eval_metric_ops=eval_metric_ops)


def main(unused_argv):
  iris = datasets.load_iris()
  x_train, x_test, y_train, y_test = model_selection.train_test_split(
      iris.data, iris.target, test_size=0.2, random_state=42)

  classifier = tf.estimator.Estimator(model_fn=my_model)

  # Train.
  train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
  classifier.train(input_fn=train_input_fn, steps=100)

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

  # Score with sklearn.
  score = metrics.accuracy_score(y_test, y_predicted)
  print('Accuracy (sklearn): {0:f}'.format(score))

  # Score with tensorflow.
  scores = classifier.evaluate(input_fn=test_input_fn)
  print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))


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