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# Copyright 2015-present The Scikit Flow 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 Estimator for Iris plant dataset."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import datasets, metrics, cross_validation
import tensorflow as tf
from tensorflow.contrib import layers
from tensorflow.contrib import learn
def my_model(features, target):
"""DNN with three hidden layers, and dropout of 0.1 probability."""
# 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': 0.9}
features = layers.stack(features, layers.fully_connected, [10, 20, 10],
normalizer_fn=normalizer_fn,
normalizer_params=normalizer_params)
# Create two tensors respectively for prediction and loss.
prediction, loss = (
tf.contrib.learn.models.logistic_regression(features, target)
)
# 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(prediction, 1), 'prob': prediction}, 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 = classifier.predict(x_test)
score = metrics.accuracy_score(y_test, y_predicted['class'])
print('Accuracy: {0:f}'.format(score))
if __name__ == '__main__':
tf.app.run()
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