diff options
Diffstat (limited to 'tensorflow/examples/tutorials/mnist/mnist_with_summaries.py')
| -rw-r--r-- | tensorflow/examples/tutorials/mnist/mnist_with_summaries.py | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py index 9a485e63bc..33dc13c813 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py +++ b/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py @@ -54,23 +54,23 @@ def main(_): # Create the model x = tf.placeholder(tf.float32, [None, 784], name='x-input') W = tf.Variable(tf.zeros([784, 10]), name='weights') - b = tf.Variable(tf.zeros([10], name='bias')) + b = tf.Variable(tf.zeros([10]), name='bias') # Use a name scope to organize nodes in the graph visualizer with tf.name_scope('Wx_b'): y = tf.nn.softmax(tf.matmul(x, W) + b) # Add summary ops to collect data - _ = tf.histogram_summary('weights', W) - _ = tf.histogram_summary('biases', b) - _ = tf.histogram_summary('y', y) + tf.histogram_summary('weights', W) + tf.histogram_summary('biases', b) + tf.histogram_summary('y', y) # Define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10], name='y-input') # More name scopes will clean up the graph representation with tf.name_scope('xent'): cross_entropy = -tf.reduce_sum(y_ * tf.log(y)) - _ = tf.scalar_summary('cross entropy', cross_entropy) + tf.scalar_summary('cross entropy', cross_entropy) with tf.name_scope('train'): train_step = tf.train.GradientDescentOptimizer( FLAGS.learning_rate).minimize(cross_entropy) @@ -78,7 +78,7 @@ def main(_): with tf.name_scope('test'): correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - _ = tf.scalar_summary('accuracy', accuracy) + tf.scalar_summary('accuracy', accuracy) # Merge all the summaries and write them out to /tmp/mnist_logs (by default) merged = tf.merge_all_summaries() |