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Update mobilenet_v1 docs to include revised tflite quantized models.

Add link to arxiv paper.

Bug fix: change dict.iteritems() to dict.items()

The data_async_generation.py process would print to stderr, but the main process would redirect it's stderr to a pipe. The main process never read from the pipe, so when the pipe was full, data_async_generation.py would stall on a write to stderr. This change makes data_async_generation.py not write to stdout/stderr.

image segmentation post

`iteritems()` was removed from python3. `items()` does the same functionality so changing it will work in both python2 and python3. The only difference as far as I know is `iteritems()` returns a generator where `items` returns a list. But for this this code it will not make any difference where we are just changing the key of the dict to a string.

* Merged commit includes the following changes:
206852642  by Zhichao Lu:

    Build the balanced_positive_negative_sampler in the model builder for FasterRCNN. Also adds an option to use the static implementation of the sampler.

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206803260  by Zhichao Lu:

    Fixes a misplaced argument in resnet fpn feature extractor.

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206682736  by Zhichao Lu:

    This CL modifies the SSD meta architecture to support both Slim-based and Keras-based box predictors, and begins preparation for Keras box predictor support in the other meta architectures.

    Concretely, this CL adds a new `KerasBoxPredictor` base class and makes the meta architectures appropriately call whichever box predictors they are using.

    We can switch the non-ssd meta architectures to fully support Keras box predictors once the Keras Convolutional Box Predictor CL is submitted.

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206669634  by Zhichao Lu:

    Adds an alternate m...

* nst colab

* downloaded py filed

* Removed text. Use gdoc for reviewing text, py for code

* update ipynb

* Removed google3 imports and added images

* nst update images

* final updates

* add github and colab links

* removed py file again

The output of an embeddding layer is already flattened, so the Flatten layers acted as no-ops.

Update overfit_and_underfit.ipynb

Delete 4_Neural_Style_Transfer_with_Eager_Execution.py

* nst colab

* downloaded py filed

* Removed text. Use gdoc for reviewing text, py for code

* update ipynb

* Removed google3 imports and added images

* nst update images

* final updates

* Fix crash when Python interpreter not on PATH.

* Fix lint error.

Update noteboook title. Add link to MLCC text classification guide.

* add indirection file

* remove unused imports

* fix import