None means this dimension is variable.
The first dimension in a keras model is always the batch size. You don't need fixed batch sizes, unless in very specific cases (for instance, when working with stateful=True LSTM layers).
That's why this dimension is often ignored when you define your model. For instance, when you define input_shape=(100,200), actually you're ignoring the batch size and defining the shape of "each sample". Internally the shape will be (None, 100, 200), allowing a variable batch size, each sample in the batch having the shape (100,200).
The batch size will be then automatically defined in the fit or predict methods.
Other None dimensions:
Not only the batch dimension can be None, but many others as well.
For instance, in a 2D convolutional network, where the expected input is (batchSize, height, width, channels), you can have shapes like (None, None, None, 3), allowing variable image sizes.
In recurrent networks and in 1D convolutions, you can also make the length/timesteps dimension variable, with shapes like (None, None, featuresOrChannels)
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