101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Example of DNNClassifier for Iris plant dataset, with exponential decay."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from sklearn import datasets
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from sklearn import metrics
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from sklearn import model_selection
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import tensorflow as tf
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X_FEATURE = 'x' # Name of the input feature.
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def my_model(features, labels, mode):
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"""DNN with three hidden layers."""
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# Create three fully connected layers respectively of size 10, 20, and 10.
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net = features[X_FEATURE]
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for units in [10, 20, 10]:
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net = tf.layers.dense(net, units=units, activation=tf.nn.relu)
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# Compute logits (1 per class).
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logits = tf.layers.dense(net, 3, activation=None)
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# Compute predictions.
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predicted_classes = tf.argmax(logits, 1)
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if mode == tf.estimator.ModeKeys.PREDICT:
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predictions = {
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'class': predicted_classes,
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'prob': tf.nn.softmax(logits)
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}
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return tf.estimator.EstimatorSpec(mode, predictions=predictions)
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# Compute loss.
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loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
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# Create training op with exponentially decaying learning rate.
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if mode == tf.estimator.ModeKeys.TRAIN:
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global_step = tf.train.get_global_step()
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learning_rate = tf.train.exponential_decay(
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learning_rate=0.1, global_step=global_step,
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decay_steps=100, decay_rate=0.001)
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optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)
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train_op = optimizer.minimize(loss, global_step=global_step)
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return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
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# Compute evaluation metrics.
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eval_metric_ops = {
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'accuracy': tf.metrics.accuracy(
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labels=labels, predictions=predicted_classes)
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}
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return tf.estimator.EstimatorSpec(
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mode, loss=loss, eval_metric_ops=eval_metric_ops)
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def main(unused_argv):
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iris = datasets.load_iris()
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x_train, x_test, y_train, y_test = model_selection.train_test_split(
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iris.data, iris.target, test_size=0.2, random_state=42)
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classifier = tf.estimator.Estimator(model_fn=my_model)
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# Train.
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train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
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x={X_FEATURE: x_train}, y=y_train, num_epochs=None, shuffle=True)
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classifier.train(input_fn=train_input_fn, steps=1000)
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# Predict.
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test_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(
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x={X_FEATURE: x_test}, y=y_test, num_epochs=1, shuffle=False)
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predictions = classifier.predict(input_fn=test_input_fn)
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y_predicted = np.array(list(p['class'] for p in predictions))
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y_predicted = y_predicted.reshape(np.array(y_test).shape)
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# Score with sklearn.
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score = metrics.accuracy_score(y_test, y_predicted)
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print('Accuracy (sklearn): {0:f}'.format(score))
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# Score with tensorflow.
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scores = classifier.evaluate(input_fn=test_input_fn)
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print('Accuracy (tensorflow): {0:f}'.format(scores['accuracy']))
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if __name__ == '__main__':
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tf.app.run()
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