text: #  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 DNNClassifier for Iris plant dataset, with exponential decay."""
  
  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."""
    # Create three fully connected layers respectively of size 10, 20, and 10.
    net = features[X_FEATURE]
    for units in [10, 20, 10]:
      net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
  
    # 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)
  
    # Compute loss.
    loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
  
    # Create training op with exponentially decaying learning rate.
    if mode == tf.estimator.ModeKeys.TRAIN:
      global_step = tf.train.get_global_step()
      learning_rate = tf.train.exponential_decay(
          learning_rate=0.1, global_step=global_step,
          decay_steps=100, decay_rate=0.001)
      optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
      train_op = optimizer.minimize(loss, global_step=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.compat.v1.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=1000)
  
    # Predict.
    test_input_fn = tf.compat.v1.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()
  