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Diffstat (limited to 'tensorflow/docs_src/tutorials/layers.md')
| -rw-r--r-- | tensorflow/docs_src/tutorials/layers.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md index aa8e2cc839..0fdfcf5d2a 100644 --- a/tensorflow/docs_src/tutorials/layers.md +++ b/tensorflow/docs_src/tutorials/layers.md @@ -341,7 +341,7 @@ pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2) ``` Note that convolutional layer #2 takes the output tensor of our first pooling -layer (`pool1`) as input, and produces the tensor `h_conv2` as output. `conv2` +layer (`pool1`) as input, and produces the tensor `conv2` as output. `conv2` has a shape of <code>[<em>batch_size</em>, 14, 14, 64]</code>, the same width and height as `pool1` (due to `padding="same"`), and 64 channels for the 64 filters applied. @@ -585,7 +585,7 @@ hand-drawn digits) and training labels (the corresponding value from 0–9 for each image) as [numpy arrays](https://docs.scipy.org/doc/numpy/reference/generated/numpy.array.html) in `train_data` and `train_labels`, respectively. Similarly, we store the -evalulation feature data (10,000 images) and evaluation labels in `eval_data` +evaluation feature data (10,000 images) and evaluation labels in `eval_data` and `eval_labels`, respectively. ### Create the Estimator {#create-the-estimator} |