Add methods to determine input size

[AlexDBlack]

Output/layer sizes are (now) easy to determine:

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java

Lines 3453 to 3474 in c24376c

	/**
	* Return the layer size (number of units) for the specified layer.
	* Note that the meaning of the "layer size" can depend on the type of layer. For example:<br>
	* - DenseLayer, OutputLayer, recurrent layers: number of units (nOut configuration option)<br>
	* - ConvolutionLayer: the depth (number of channels)<br>
	* - Subsampling layers, global pooling layers, etc: size of 0 is always returned<br>
	*
	* @param layer Index of the layer to get the size of. Must be in range 0 to nLayers-1 inclusive
	* @return Size of the layer
	*/
	public int layerSize(int layer) {
	if (layer < 0 || layer > layers.length) {
	throw new IllegalArgumentException("Invalid layer index: " + layer + ". Layer index must be between 0 and "
	+ (layers.length - 1) + " inclusive");
	}
	org.deeplearning4j.nn.conf.layers.Layer conf = layers[layer].conf().getLayer();
	if (conf == null || !(conf instanceof FeedForwardLayer)) {
	return 0;
	}
	FeedForwardLayer ffl = (FeedForwardLayer) conf;
	return ffl.getNOut();
	}

But input size/type to a network is not as easy to infer.

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