def softmax(x, axis=None, name=None):
I = x.tensor
if name is None:
name = 'softmax'
if axis is None:
axis = I.shape.ndims - 1
y = plaidml_op.softmax(I, axis=axis)
return _KerasNode('softmax', name=name, tensor=y)
def softmax(x, axis=None, name=None):
if name is None:
name = 'softmax'
I = x.tensor
ndims = I.shape.ndims
I_dims = edsl.TensorDims(ndims)
I.bind_dims(*I_dims)
if axis is None:
axis = ndims - 1
axis = _normalize_axis(axis=axis, ndims=ndims, name=name + ' (softmax)')
if ndims == 2 and axis == 1:
return _KerasNode(name, tensor=plaidml_op.softmax(I, axis=1))
if axis == 0:
group = 1
else:
group = functools.reduce(lambda x, y: x * y, I_dims[:axis])
values = functools.reduce(lambda x, y: x * y, I_dims[axis:])
flat_x = reshape(x, (group, values))
result = _KerasNode(name, tensor=plaidml_op.softmax(flat_x.tensor, axis=1))
return reshape(result, I_dims)
def softmax(x):
logger.debug('softmax(x: {})'.format(x))
y = plaidml_op.softmax(x.tensor, axis=x.tensor.shape.ndims - 1)
return _KerasNode('softmax', tensor=y)
def softmax(x):
y = plaidml_op.softmax(x.tensor, axis=x.tensor.shape.ndims - 1)
return _KerasNode('softmax', tensor=y)