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Add documentation for using channels when training with TF (#247)
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src/sagemaker/tensorflow/README.rst

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==========================================
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TensorFlow SageMaker Estimators and Models
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==========================================
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train_instance_count=1, train_instance_type='ml.p2.xlarge')
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tf_estimator.fit('s3://bucket/path/to/training/data')
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Where the s3 url is a path to your training data, within Amazon S3. The
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Where the S3 url is a path to your training data, within Amazon S3. The
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constructor keyword arguments define how SageMaker runs your training
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script and are discussed, in detail, in a later section.
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Required argument
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'''''''''''''''''
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- ``inputs (str)``: A S3 URI, for example ``s3://my-bucket/my-training-data``, which contains
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the dataset that will be used for training. When the training job starts in SageMaker the
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container will download the dataset. Both ``train_input_fn`` and ``eval_input_fn`` functions
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have a parameter called ``training_dir`` which contains the directory inside the container
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where the dataset was saved into. See `Creating train_input_fn and eval_input_fn functions`_.
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- ``inputs``: The S3 location(s) of datasets to be used for training. This can take one of two forms:
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- ``str``: An S3 URI, for example ``s3://my-bucket/my-training-data``, which indicates the dataset's location.
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- ``dict[str, str]``: A dictionary mapping channel names to S3 locations, for example ``{'train': 's3://my-bucket/my-training-data/train', 'test': 's3://my-bucket/my-training-data/test'}``
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When the training job starts in SageMaker the container will download the dataset.
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Both ``train_input_fn`` and ``eval_input_fn`` functions have a parameter called ``training_dir`` which
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contains the directory inside the container where the dataset was saved into.
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See `Creating train_input_fn and eval_input_fn functions`_.
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Optional arguments
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''''''''''''''''''

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