def generalized_outer_product(mat):
if len(mat.shape) == 1:
return np.outer(mat, mat)
elif len(mat.shape) == 2:
return np.einsum('ij,ik->ijk', mat, mat)
else:
raise ArithmeticError
def predictions(weights, inputs):
"""weights is shape (num_weight_samples x num_weights)
inputs is shape (num_datapoints x D)"""
inputs = np.expand_dims(inputs, 0)
for W, b in unpack_layers(weights):
outputs = np.einsum('mnd,mdo->mno', inputs, W) + b
inputs = nonlinearity(outputs)
return outputs
