Implement the Flatten layer

[cesarsouza]

Note: Implementing the Flatten layer does not actually involve implement it from scratch.

What needs to be done is to navigate to https://github.com/fchollet/keras/tree/f65a56fb65062c8d14d215c9f4b1015b97cc5bf3/keras, find where the Flatten layer is implemented (in this case, core.py, line 452), and port its arguably simple code:

class Flatten(Layer):
    """Flattens the input. Does not affect the batch size.
    # Example
    ```python
        model = Sequential()
        model.add(Convolution2D(64, 3, 3,
                                border_mode='same',
                                input_shape=(3, 32, 32)))
        # now: model.output_shape == (None, 64, 32, 32)
        model.add(Flatten())
        # now: model.output_shape == (None, 65536)
    ```
    """

    def __init__(self, **kwargs):
        super(Flatten, self).__init__(**kwargs)
        self.input_spec = InputSpec(min_ndim=3)

    def compute_output_shape(self, input_shape):
        if not all(input_shape[1:]):
            raise ValueError('The shape of the input to "Flatten" '
                             'is not fully defined '
                             '(got ' + str(input_shape[1:]) + '. '
                             'Make sure to pass a complete "input_shape" '
                             'or "batch_input_shape" argument to the first '
                             'layer in your model.')
        return (input_shape[0], np.prod(input_shape[1:]))

    def call(self, inputs):
        return K.batch_flatten(inputs)

into C# using the same K backend already present in Keras Sharp.