* Add a unit test for CuDNNGRU conversion with TimeDistributed.

* Extract duplicated function convert_model() to _convert_model_weights().

* #10356 Convert weights of CuDNN/plain RNN nested in TimeDistributed.

Same case as for Bidirectional, except that in TimeDistributed there's only
one nested layer instead of two.

* Fix issue when working in conjunction with TimeSeriesGenerator

The TimeSeriesGenerator class uses xrange through six which caused an IndexError

* Add test

* Add corresponding test

* Fix for python3

* Simplified code

* Fix indent

* Fix test

* Embed layer-outputs rather than layer-weights in TensorBoard callback

* Update docstring and allow multiple inputs

* Fix tests

* Renaming

* Set learning phase

* Compute embeddings in batches

* Pass embedding data explicitly

* Actually process embeddings in batches

* Allow multiple inputs and validate input data

* Add example

* Delete utils.py

* Revert uncorrectly resolved merge-conflict

* Minor renaming

* Add comment clarifying the design choice

The max_value argument can not be used in a layer, except
custom layer or Lambda. Hence, similarly to LeakyReLU or
for example Softmax, this PR adds a layer for ReLU,
enabling also a capped ReLU to be used.

* Reduce tests for applications

* Make selection over all models random

* Add depth as third dimension in docstring of
  Conv3DTranspose in convolutional.py in keras.layers

* Split `applications` and `preprocessing` modules.

* Fix dependencies.

* equip TimeDistributed with mask and unspecified input length

* fix bugs in theano. add test on timedistributed + masking

* skip tests on cntk with multiple unspecified time lengths.

* move static shape inference to theano_backend, add docstring, etc.

* fix format

Fixed doc

* Add pooling option

* Add pooling test

* Fix naming convention

* Add missing doc

#10219

Non training Batch Norm operator has bad performance for it running into tensorflow's non fused batch norm API (#10207)

* When use tensorflow as backend, let batch norm run into fused batch norm as much as possible, which has better performance.

fix issue: http://github.com/keras-team/keras/issues/10058

* In Tensorflow backend, let batch norm call to FusedBatchNorm only NHWC format, also gamma and beta are not None.

Test result:
test env: with Tensorflow(commit a543d9471047ca3f6881c87105fcbe2cdff9207d Date:   Thu May 10 17:43:30 2018, local build), python3.4, centos7.4
test cases:
  "pytest  ./tests/keras/layers/normalization_test.py"  <all passed>
  "pytest  ./tests"      <keep same result as without this commit's modification on BN>

* fix code sytle.

* 1. Add axis parameter in backend's batch_normalization functions.
2. Refine the batch_normalization function in tensorflow backend, Let's it call to fused batch norm as much as possible.

Thanks the coments from fchollet.

* Trigger

* 1. add default value -1 for parameter axis in batch_normalization function in backend.
2. fix some code style.
Thanks the comments from fchollet.

* Alphabetizes and adds to layers doc.

* Responding to @cais comments

* fix spacing.  Remove in(out)bound_nodes

Fix for #10265

* Fixes automatic doc generation problem with indented lists. Adds a new test

* Some style fixes on doc automatic generation files

* Fixes a bad space in convolutional_recurrent.py

* Changes the test_doc_auto_generation in order to include a doc string taken from the codebase. Allows text lines following nested lists

Doc expects a list containing 2 tensors.

Softplus docstring missing a parenthesis.

* Create get_random_transform and refactor

* Fix style and add tests

* Add more tests

* Fix documentation error

* Fix documentation style issue

* add apply_affine_transform

* document transformation dictionary

* Doc style fix

* Remove Sequential.model deprecation warning

* Remove dead line of code

New Benchmark by in-place split:

>> keras.application.Resnet50 224x224x3 (NCWH; NVidia Tesla P100 x 4)
 input_shape = 3x224x224, batch_size =  96 x 4: 392(images/sec) => 417(images/sec)
 input_shape = 3x299x299, batch_size =  64 x 4: 229(images/sec) => 244(images/sec)
 input_shape = 3x224x224, batch_size =   8 x 4: 148(images/sec) => 163(images/sec)

>> keras.application.InceptionV3 (NCWH; NVidia Tesla P100 x 4)
 input_shape = 3x224x224, batch_size = 128 x 4: 488(images/sec) => 526(images/sec)
 input_shape = 3x299x299, batch_size =  96 x 4: 270(images/sec) => 294(images/sec)
 input_shape = 3x224x224, batch_size =   8 x 4: 146(images/sec) => 158(images/sec)
Signed-off-by: NCUI Wei <ghostplant@qq.com>

* Fix NASNet

* Update weight files

Signed-off-by: NCUI Wei <ghostplant@qq.com>