diff options
Diffstat (limited to 'tensorflow/g3doc/how_tos/reading_data/index.md')
| -rw-r--r-- | tensorflow/g3doc/how_tos/reading_data/index.md | 10 |
1 files changed, 5 insertions, 5 deletions
diff --git a/tensorflow/g3doc/how_tos/reading_data/index.md b/tensorflow/g3doc/how_tos/reading_data/index.md index e59f8d6577..b8df1d88aa 100644 --- a/tensorflow/g3doc/how_tos/reading_data/index.md +++ b/tensorflow/g3doc/how_tos/reading_data/index.md @@ -10,7 +10,7 @@ There are three main methods of getting data into a TensorFlow program: [TOC] -## Feeding {#Feeding} +## Feeding TensorFlow's feed mechanism lets you inject data into any Tensor in a computation graph. A python computation can thus feed data directly into the @@ -253,7 +253,7 @@ summary to the graph that indicates how full the example queue is. If you have enough reading threads, that summary will stay above zero. You can [view your summaries as training progresses using TensorBoard](../../how_tos/summaries_and_tensorboard/index.md). -### Creating threads to prefetch using `QueueRunner` objects {#QueueRunner} +### Creating threads to prefetch using `QueueRunner` objects The short version: many of the `tf.train` functions listed above add [`QueueRunner`](../../api_docs/python/train.md#QueueRunner) objects to your @@ -264,7 +264,7 @@ will start threads that run the input pipeline, filling the example queue so that the dequeue to get the examples will succeed. This is best combined with a [`tf.train.Coordinator`](../../api_docs/python/train.md#Coordinator) to cleanly shut down these threads when there are errors. If you set a limit on the number -of epochs, that will use an epoch counter that will need to be intialized. The +of epochs, that will use an epoch counter that will need to be initialized. The recommended code pattern combining these is: ```python @@ -431,8 +431,8 @@ with tf.Session() as sess: shape=training_data.shape) label_initializer = tf.placeholder(dtype=training_labels.dtype, shape=training_labels.shape) - input_data = tf.Variable(data_initalizer, trainable=False, collections=[]) - input_labels = tf.Variable(label_initalizer, trainable=False, collections=[]) + input_data = tf.Variable(data_initializer, trainable=False, collections=[]) + input_labels = tf.Variable(label_initializer, trainable=False, collections=[]) ... sess.run(input_data.initializer, feed_dict={data_initializer: training_data}) |