Merge pull request tensorflow#111 from dvbuntu/custom_weights_example

Add example accessing of weights

examples/mnist_weights.py

@@ -0,0 +1,96 @@
+#t Copyright 2015-present 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.
+
+"""
+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 sklearn import metrics
+
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+
+import skflow
+
+### Download and load MNIST data.
+
+mnist = input_data.read_data_sets('MNIST_data')
+
+### Linear classifier.
+
+classifier = skflow.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 = skflow.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 = skflow.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 = skflow.ops.dnn(h_pool2_flat, [1024], activation=tf.nn.relu, keep_prob=0.5)
+    return skflow.models.logistic_regression(h_fc1, y)
+
+# Training and predicting
+classifier = skflow.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 figur 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'))