Update generated Op docs.

Change: 111631697

tensorflow/g3doc/api_docs/python/array_ops.md

@@ -727,6 +727,7 @@ output[3, 2:, :, ...] = input[3, 2:, :, ...]
 ```
 
 In contrast, if:
+
 ```prettyprint
 # Given this:
 batch_dim = 2

tensorflow/g3doc/api_docs/python/image.md

@@ -207,7 +207,7 @@ resized_image = tf.image.resize_images(image, 299, 299)
 
 - - -
 
-### `tf.image.resize_images(images, new_height, new_width, method=0)` {#resize_images}
+### `tf.image.resize_images(images, new_height, new_width, method=0, align_corners=False)` {#resize_images}
 
 Resize `images` to `new_width`, `new_height` using the specified `method`.
 
@@ -233,6 +233,9 @@ the same as `new_width`, `new_height`.  To avoid distortions see
 *  <b>`new_height`</b>: integer.
 *  <b>`new_width`</b>: integer.
 *  <b>`method`</b>: ResizeMethod.  Defaults to `ResizeMethod.BILINEAR`.
+*  <b>`align_corners`</b>: bool. If true, exactly align all 4 cornets of the input and
+                 output. Defaults to `false`. Only implemented for bilinear
+                 interpolation method so far.
 
 ##### Raises:
 
@@ -298,7 +301,7 @@ Input images can be of different types but output images are always float.
 
 - - -
 
-### `tf.image.resize_bilinear(images, size, name=None)` {#resize_bilinear}
+### `tf.image.resize_bilinear(images, size, align_corners=None, name=None)` {#resize_bilinear}
 
 Resize `images` to `size` using bilinear interpolation.
 
@@ -311,6 +314,10 @@ Input images can be of different types but output images are always float.
     4-D with shape `[batch, height, width, channels]`.
 *  <b>`size`</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`.  The
     new size for the images.
+*  <b>`align_corners`</b>: An optional `bool`. Defaults to `False`.
+    If true, rescale input by (new_height - 1) / (height - 1), which
+    exactly aligns the 4 corners of images and resized images. If false, rescale
+    by new_height / height. Treat similarly the width dimension.
 *  <b>`name`</b>: A name for the operation (optional).
 
 ##### Returns:
@@ -685,7 +692,7 @@ Example:
 # Decode an image and convert it to HSV.
 rgb_image = tf.decode_png(...,  channels=3)
 rgb_image_float = tf.convert_image_dtype(rgb_image, tf.float32)
-hsv_image = tf.hsv_to_rgb(rgb_image)
+hsv_image = tf.rgb_to_hsv(rgb_image)
 ```
 
 - - -
@@ -788,7 +795,7 @@ Convert `image` to `dtype`, scaling its values if needed.
 
 Images that are represented using floating point values are expected to have
 values in the range [0,1). Image data stored in integer data types are
-expected to have values in the range `[0,MAX]`, wbere `MAX` is the largest
+expected to have values in the range `[0,MAX]`, where `MAX` is the largest
 positive representable number for the data type.
 
 This op converts between data types, scaling the values appropriately before
@@ -921,7 +928,7 @@ channel and then adjusts each component `x` of each pixel to
 
 Adjust the contrast of an image by a random factor.
 
-Equivalent to `adjust_constrast()` but uses a `contrast_factor` randomly
+Equivalent to `adjust_contrast()` but uses a `contrast_factor` randomly
 picked in the interval `[lower, upper]`.
 
 ##### Args:
@@ -1010,7 +1017,7 @@ picked in the interval `[-max_delta, max_delta]`.
 
 ### `tf.image.adjust_saturation(image, saturation_factor, name=None)` {#adjust_saturation}
 
-Adjust staturation of an RGB image.
+Adjust saturation of an RGB image.
 
 This is a convenience method that converts an RGB image to float
 representation, converts it to HSV, add an offset to the saturation channel,
@@ -1073,7 +1080,7 @@ Linearly scales `image` to have zero mean and unit norm.
 
 This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average
 of all values in image, and
-`adjusted_stddev = max(stddev, 1.0/srqt(image.NumElements()))`.
+`adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements()))`.
 
 `stddev` is the standard deviation of all values in `image`. It is capped
 away from zero to protect against division by 0 when handling uniform images.

tensorflow/g3doc/api_docs/python/math_ops.md

@@ -408,7 +408,7 @@ Computes exponential of x element-wise.  \\(y = e^x\\).
 
 ### `tf.log(x, name=None)` {#log}
 
-Computes natural logrithm of x element-wise.
+Computes natural logarithm of x element-wise.
 
 I.e., \\(y = \log_e x\\).
 
@@ -2018,7 +2018,7 @@ Computes the inverse permutation of a tensor.
 
 This operation computes the inverse of an index permutation. It takes a 1-D
 integer tensor `x`, which represents the indices of a zero-based array, and
-swaps each value with its index position. In other words, for an ouput tensor
+swaps each value with its index position. In other words, for an output tensor
 `y` and an input tensor `x`, this operation computes the following:
 
 `y[x[i]] = i for i in [0, 1, ..., len(x) - 1]`

tensorflow/g3doc/api_docs/python/train.md

@@ -298,6 +298,8 @@ Construct a new gradient descent optimizer.
 
 Optimizer that implements the Adagrad algorithm.
 
+See http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
+
 - - -
 
 #### `tf.train.AdagradOptimizer.__init__(learning_rate, initial_accumulator_value=0.1, use_locking=False, name='Adagrad')` {#AdagradOptimizer.__init__}
@@ -350,14 +352,14 @@ Construct a new Momentum optimizer.
 
 Optimizer that implements the Adam algorithm.
 
+See http://arxiv.org/pdf/1412.6980v7.pdf.
+
 - - -
 
 #### `tf.train.AdamOptimizer.__init__(learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False, name='Adam')` {#AdamOptimizer.__init__}
 
 Construct a new Adam optimizer.
 
-Implementation is based on: http://arxiv.org/pdf/1412.6980v7.pdf
-
 Initialization:
 
 ```
@@ -461,6 +463,8 @@ using this function.
 
 Optimizer that implements the RMSProp algorithm.
 
+See http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf.
+
 - - -
 
 #### `tf.train.RMSPropOptimizer.__init__(learning_rate, decay=0.9, momentum=0.0, epsilon=1e-10, use_locking=False, name='RMSProp')` {#RMSPropOptimizer.__init__}
@@ -893,7 +897,7 @@ ema = tf.train.ExponentialMovingAverage(decay=0.9999)
 maintain_averages_op = ema.apply([var0, var1])
 
 # Create an op that will update the moving averages after each training
-# step.  This is what we will use in place of the usuall trainig op.
+# step.  This is what we will use in place of the usual training op.
 with tf.control_dependencies([opt_op]):
     training_op = tf.group(maintain_averages_op)