From 4abec1a58e5576a45101d4507219b348290e5e76 Mon Sep 17 00:00:00 2001 From: Yifei Date: Sun, 5 Nov 2017 08:50:26 -0800 Subject: [PATCH] Fix nits --- examples/03_logistic_regression_mnist_sol.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/03_logistic_regression_mnist_sol.py b/examples/03_logistic_regression_mnist_sol.py index 33a74d68..0e5d6628 100644 --- a/examples/03_logistic_regression_mnist_sol.py +++ b/examples/03_logistic_regression_mnist_sol.py @@ -20,7 +20,7 @@ # Step 1: Read in data # using TF Learn's built in function to load MNIST data to the folder data/mnist -mnist = input_data.read_data_sets('/data/mnist', one_hot=True) +mnist = input_data.read_data_sets('./data/mnist', one_hot=True) # Step 2: create placeholders for features and labels # each image in the MNIST data is of shape 28*28 = 784 @@ -83,7 +83,7 @@ for i in range(n_batches): X_batch, Y_batch = mnist.test.next_batch(batch_size) - accuracy_batch = sess.run([accuracy], feed_dict={X: X_batch, Y:Y_batch}) + accuracy_batch = sess.run(accuracy, feed_dict={X: X_batch, Y:Y_batch}) total_correct_preds += accuracy_batch print('Accuracy {0}'.format(total_correct_preds/mnist.test.num_examples))