diff options
Diffstat (limited to 'tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md')
| -rw-r--r-- | tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md index c8ae21e3dd..5059f02a73 100644 --- a/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md +++ b/tensorflow/g3doc/how_tos/summaries_and_tensorboard/index.md @@ -86,23 +86,23 @@ with tf.name_scope("Wx_b") as scope: y = tf.nn.softmax(tf.matmul(x,W) + b) # Add summary ops to collect data -w_hist = tf.histogram_summary("weights", W) -b_hist = tf.histogram_summary("biases", b) -y_hist = 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") as scope: cross_entropy = -tf.reduce_sum(y_*tf.log(y)) - ce_summ = tf.scalar_summary("cross entropy", cross_entropy) + tf.scalar_summary("cross entropy", cross_entropy) with tf.name_scope("train") as scope: train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) with tf.name_scope("test") as scope: correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) - accuracy_summary = tf.scalar_summary("accuracy", accuracy) + tf.scalar_summary("accuracy", accuracy) # Merge all the summaries and write them out to /tmp/mnist_logs merged = tf.merge_all_summaries() |