message: add batching in readme example
diff: @@ -23,25 +23,35 @@ the data processing aspect replaced by tensorflow-io:
  import tensorflow as tf
  import tensorflow_io as tfio
  -# Read MNIST into Dataset
  +# Read the MNIST data into the IODataset.
  d_train = tfio.IODataset.from_mnist(
  'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
  - 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz').batch(1)
  + 'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz')
  -# By default image data is uint8 so convert to float32.
  +# Shuffle the elements of the dataset.
  +d_train = d_train.shuffle(buffer_size=1024)
  +
  +# By default image data is uint8, so convert to float32 using map().
  d_train = d_train.map(lambda x, y: (tf.image.convert_image_dtype(x, tf.float32), y))
  +# prepare batches the data just like any other tf.data.Dataset
  +d_train = d_train.batch(32)
  +
  +# Build the model.
  model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(512, activation=tf.nn.relu),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation=tf.nn.softmax)
  ])
  +
  +# Compile the model.
  model.compile(optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])
  -model.fit(d_train, epochs=5, steps_per_epoch=10000)
  +# Fit the model.
  +model.fit(d_train, epochs=5, steps_per_epoch=200)
  ```
  In the above [MNIST](http://yann.lecun.com/exdb/mnist/) example, the URL's
  