Last active
January 13, 2017 09:16
-
-
Save junichiro/91943b2545ed2bb450a771426d80b910 to your computer and use it in GitHub Desktop.
TensorFlow 入門 〜 MNIST for beginner のプチ応用 〜 ref: http://qiita.com/junichiro/items/6f0de9688e12535f93e8
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
h_\theta(x) = \frac{ 1 }{ 1 + e^{ -\theta^T x } } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
J(\theta) = -\frac{ 1 }{ m }\sum_{ i = 1 }^{ m }\bigl( y^{ (i) } log h_\theta(x^{ (i) }) + (1 - y^{ (i) }) log (1 - h_\theta(x^{ (i) })) \bigr) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
diff -u mnist_softmax.py mnist_sigmoid.py | |
--- mnist_softmax.py 2017-01-12 10:12:21.000000000 +0900 | |
+++ mnist_sigmoid.py 2017-01-12 10:50:52.000000000 +0900 | |
@@ -56,9 +56,9 @@ | |
# | |
# So here we use tf.nn.softmax_cross_entropy_with_logits on the raw | |
# outputs of 'y', and then average across the batch. | |
- cross_entropy = tf.reduce_mean( | |
- tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y)) | |
- train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) | |
+ hx = tf.sigmoid(y) | |
+ loss = -tf.reduce_mean((y_ * tf.log(hx) + (1 - y_) * tf.log(1 - hx)), reduction_indices=[0]) | |
+ train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss) | |
sess = tf.InteractiveSession() | |
tf.global_variables_initializer().run() | |
Diff finished. Thu Jan 12 10:51:33 2017 |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# Train | |
for _ in range(1000): | |
batch_xs, batch_ys = mnist.train.next_batch(100) | |
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
diff -u mnist_sigmoid.py mnist_sigmoid_all.py | |
--- mnist_sigmoid.py 2017-01-12 12:59:36.000000000 +0900 | |
+++ mnist_sigmoid_all.py 2017-01-12 12:59:09.000000000 +0900 | |
@@ -64,8 +64,7 @@ | |
tf.global_variables_initializer().run() | |
# Train | |
for _ in range(1000): | |
- batch_xs, batch_ys = mnist.train.next_batch(100) | |
- sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) | |
+ sess.run(train_step, feed_dict={x: mnist.train.images, y_: mnist.train.labels}) | |
# Test trained model | |
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) | |
Diff finished. Thu Jan 12 13:54:43 2017 |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment