1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
|
# 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
from sklearn import datasets, cross_validation, metrics
import tensorflow as tf
from tensorflow.contrib import learn
from tensorflow.contrib.learn import monitors
# Load dataset
digits = datasets.load_digits()
X = digits.images
y = digits.target
# Split it into train / test subsets
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y,
test_size=0.2,
random_state=42)
# Split X_train again to create validation data
X_train, X_val, y_train, y_val = cross_validation.train_test_split(X_train,
y_train,
test_size=0.2,
random_state=42)
# TensorFlow model using Scikit Flow ops
def conv_model(X, y):
X = tf.expand_dims(X, 3)
features = tf.reduce_max(learn.ops.conv2d(X, 12, [3, 3]), [1, 2])
features = tf.reshape(features, [-1, 12])
return learn.models.logistic_regression(features, y)
val_monitor = monitors.ValidationMonitor(X_val, y_val, every_n_steps=50)
# Create a classifier, train and predict.
classifier = learn.TensorFlowEstimator(model_fn=conv_model, n_classes=10,
steps=1000, learning_rate=0.05,
batch_size=128)
classifier.fit(X_train, y_train, val_monitor)
score = metrics.accuracy_score(y_test, classifier.predict(X_test))
print('Test Accuracy: {0:f}'.format(score))
|
