def categorical_crossentropy(target, output, from_logits=False):
if from_logits:
output = softmax(output)
elif output.opname != 'softmax':
output /= sum(output, axis=(-1,), keepdims=True)
output = clip(output, epsilon(), 1.0 - epsilon())
T = target.tensor
O = output.tensor
ndims = O.shape.ndims
fixed_dims = edsl.TensorDims(ndims - 1)
fixed_idxs = edsl.TensorIndexes(ndims - 1)
Y = edsl.TensorDim()
y = edsl.TensorIndex()
input_dims = fixed_dims + [Y]
O.bind_dims(*input_dims)
T.bind_dims(*input_dims)
LO = edsl.log(O)
TR = edsl.TensorOutput(*fixed_dims)
TR[fixed_idxs] += T[fixed_idxs + [y]] * LO[fixed_idxs + [y]]
R = -TR
return _KerasNode('categorical_crossentropy', tensor=R)
def log(x):
logger.debug('log(x: {})'.format(x))
return _KerasNode('log', tensor=edsl.log(x.tensor))
def log(x):
return _KerasNode('log', tensor=edsl.log(x.tensor))
