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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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import random
import tensorflow as tf
from tensorflow.contrib import learn
from tensorflow.contrib.learn import datasets
# Load Iris Data
iris = datasets.load_iris()
# Initialize a deep neural network autoencoder
# You can also add noise and add dropout if needed
# Details see TensorFlowDNNAutoencoder documentation.
autoencoder = learn.TensorFlowDNNAutoencoder(hidden_units=[10, 20])
# Fit with Iris data
transformed = autoencoder.fit_transform(iris.data)
print(transformed)
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