1 files changed, 33 insertions, 0 deletions
diff --git a/tensorflow/g3doc/tutorials/mnist/mnist_softmax.py b/tensorflow/g3doc/tutorials/mnist/mnist_softmax.py
new file mode 100644
index 0000000000..640ea29dac
--- /dev/null
+++ b/tensorflow/g3doc/tutorials/mnist/mnist_softmax.py
@@ -0,0 +1,33 @@
+"""A very simple MNIST classifer.
+
+See extensive documentation at ??????? (insert public URL)
+"""
+
+# Import data
+import input_data
+mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
+
+import tensorflow as tf
+sess = tf.InteractiveSession()
+
+# Create the model
+x = tf.placeholder("float", [None, 784])
+W = tf.Variable(tf.zeros([784,10]))
+b = tf.Variable(tf.zeros([10]))
+y = tf.nn.softmax(tf.matmul(x,W) + b)
+
+# Define loss and optimizer
+y_ = tf.placeholder("float", [None,10])
+cross_entropy = -tf.reduce_sum(y_*tf.log(y))
+train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
+
+# Train
+tf.initialize_all_variables().run()
+for i in range(1000):
+  batch_xs, batch_ys = mnist.train.next_batch(100)
+  train_step.run({x: batch_xs, y_: batch_ys})
+
+# Test trained model
+correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+print accuracy.eval({x: mnist.test.images, y_: mnist.test.labels})