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Keras Dense layer needs an input_dim or input_shape to be specified. What value do I put in there?
My input is a matrix of 1,000,000 rows and only 3 columns. My output is 1,600 classes.
What do I put there?
dimensionality of the inputs (1000000, 1600)
2 because it's a 2D matrix
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input_dim is the number of dimensions of the features, in your case that is just 3. The equivalent notation for input_shape , which is an actual dimensional shape, is (3,)
It's depend more on number of classes. For 20 classes 2 layers 512 should be more then enough. If you want to experiment you can try also 2 x 256 and 2 x 1024. Less then 256 may work too, but you may underutilize power of previous conv layers.
From the definition of Keras documentation the Sequential model is a linear stack of layers.You can create a Sequential model by passing a list of layer instances to the constructor: from keras.models import Sequential from keras.layers import Dense, Activation model = Sequential([ Dense(32, input_shape=(784,)), ...
input_dim is the number of dimensions of the features, in your case that is just 3. The equivalent notation for input_shape, which is an actual dimensional shape, is (3,)
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