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#t 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.
"""
This example demonstrates one way to access the weights of a custom skflow
model. It is otherwise identical to the standard MNIST convolutional code.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from sklearn import metrics
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib import learn
### Download and load MNIST data.
mnist = learn.datasets.load_dataset('mnist')
### Linear classifier.
classifier = learn.TensorFlowLinearClassifier(
n_classes=10, batch_size=100, steps=1000, learning_rate=0.01)
classifier.fit(mnist.train.images, mnist.train.labels)
score = metrics.accuracy_score(mnist.test.labels, classifier.predict(mnist.test.images))
print('Accuracy: {0:f}'.format(score))
### Convolutional network
def max_pool_2x2(tensor_in):
return tf.nn.max_pool(tensor_in, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
padding='SAME')
def conv_model(X, y):
# reshape X to 4d tensor with 2nd and 3rd dimensions being image width and height
# final dimension being the number of color channels
X = tf.reshape(X, [-1, 28, 28, 1])
# first conv layer will compute 32 features for each 5x5 patch
with tf.variable_scope('conv_layer1'):
h_conv1 = learn.ops.conv2d(X, n_filters=32, filter_shape=[5, 5],
bias=True, activation=tf.nn.relu)
h_pool1 = max_pool_2x2(h_conv1)
# second conv layer will compute 64 features for each 5x5 patch
with tf.variable_scope('conv_layer2'):
h_conv2 = learn.ops.conv2d(h_pool1, n_filters=64, filter_shape=[5, 5],
bias=True, activation=tf.nn.relu)
h_pool2 = max_pool_2x2(h_conv2)
# reshape tensor into a batch of vectors
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
# densely connected layer with 1024 neurons
h_fc1 = learn.ops.dnn(h_pool2_flat, [1024], activation=tf.nn.relu, dropout=0.5)
return learn.models.logistic_regression(h_fc1, y)
# Training and predicting
classifier = learn.TensorFlowEstimator(
model_fn=conv_model, n_classes=10, batch_size=100, steps=20000,
learning_rate=0.001)
classifier.fit(mnist.train.images, mnist.train.labels)
score = metrics.accuracy_score(mnist.test.labels, classifier.predict(mnist.test.images))
print('Accuracy: {0:f}'.format(score))
# Examining fitted weights
## General usage is classifier.get_tensor_value('foo')
## 'foo' must be the variable scope of the desired tensor followed by the
## graph path.
## To understand the mechanism and figure out the right scope and path, you can do logging.
## Then use TensorBoard or a text editor on the log file to look at available strings.
## First Convolutional Layer
print('1st Convolutional Layer weights and Bias')
print(classifier.get_tensor_value('conv_layer1/convolution/filters:0'))
print(classifier.get_tensor_value('conv_layer1/convolution/bias:0'))
## Second Convolutional Layer
print('2nd Convolutional Layer weights and Bias')
print(classifier.get_tensor_value('conv_layer2/convolution/filters:0'))
print(classifier.get_tensor_value('conv_layer2/convolution/bias:0'))
## Densely Connected Layer
print('Densely Connected Layer weights')
print(classifier.get_tensor_value('dnn/layer0/Linear/Matrix:0'))
## Logistic Regression weights
print('Logistic Regression weights')
print(classifier.get_tensor_value('logistic_regression/weights:0'))
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