def __init__(self):
super(log_sum_exp, self).__init__()
self.maxi = P.ReduceMax()
self.maxi_dim = P.ReduceMax(keep_dims=True)
self.log = P.Log()
self.sums = P.ReduceSum()
self.exp = P.Exp()
def __init__(self):
super(log_softmax, self).__init__()
self.maxi = P.ReduceMax()
self.log = P.Log()
self.sums = P.ReduceSum()
self.exp = P.Exp()
self.axis = -1
self.concat = P.Concat(-1)
self.expanddims = P.ExpandDims()
def construct(self, inputs, inputs_back, targets, targets_back):
weights = self.weights
loss = self.network(inputs, inputs_back, targets, targets_back)
grads = self.grad(self.network, weights)(inputs, inputs_back, targets,
targets_back)
# grad reducer on grads
grads = self.grad_reducer(grads)
#grads = clip.average_gradients([grads])
#grads = clip.clip_grads(grads)
grads = P.clip_by_global_norm(grads)
train_perplexity = P.Exp()(loss)
succ = self.optimizer(grads)
return F.depend(train_perplexity, succ)
def __init__(self, sparse=False):
super(SoftmaxCrossEntropyExpand, self).__init__()
self.exp = ops.Exp()
self.sum = ops.ReduceSum(keep_dims=True)
self.onehot = ops.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.div = ops.RealDiv()
self.log = ops.Log()
self.sum_cross_entropy = ops.ReduceSum(keep_dims=False)
self.mul = ops.Mul()
self.mul2 = ops.Mul()
self.mean = ops.ReduceMean(keep_dims=False)
self.sparse = sparse
self.max = ops.ReduceMax(keep_dims=True)
self.sub = ops.Sub()
def __init__(self, log_scale_min=-7.0, reduce=True):
super(mix_gaussian_loss, self).__init__()
self.log_scale_min = log_scale_min
self.reduce = reduce
self.transpose_op = P.Transpose()
self.maximum = P.Maximum()
self.tile = P.Tile()
self.exp = P.Exp()
self.logsoftmax = P.LogSoftmax(-1)
self.expand_dims = P.ExpandDims()
self.sums = P.ReduceSum()
self.lse = log_sum_exp()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.const = P.ScalarToArray()
self.log = P.Log()
def __init__(self, num_classes=256, log_scale_min=-7.0, reduce=True):
super(discretized_mix_logistic_loss, self).__init__()
self.num_classes = num_classes
self.log_scale_min = log_scale_min
self.reduce = reduce
self.transpose_op = P.Transpose()
self.exp = P.Exp()
self.sigmoid = P.Sigmoid()
self.softplus = Stable_softplus()
self.log = P.Log()
self.cast = P.Cast()
self.logsoftmax = P.LogSoftmax(-1)
self.expand_dims = P.ExpandDims()
self.tile = P.Tile()
self.maximum = P.Maximum()
self.sums = P.ReduceSum()
self.lse = log_sum_exp()
self.reshape = P.Reshape()
self.factor = self.log(Tensor((self.num_classes - 1) / 2, ms.float32))
def __init__(self, log_scale_min=-7.0, reduce=True):
super(mix_gaussian_loss, self).__init__()
self.log_scale_min = log_scale_min
self.reduce = reduce
self.transpose_op = P.Transpose()
self.maximum = P.Maximum()
self.tile = P.Tile()
self.exp = P.Exp()
self.expand_dims = P.ExpandDims()
self.sums = P.ReduceSum()
self.lse = log_sum_exp()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.const = P.ScalarToArray()
self.log = P.Log()
self.tensor_one = Tensor(1., ms.float32)
if context.get_context("device_target") == "CPU":
self.logsoftmax = log_softmax()
else:
self.logsoftmax = P.LogSoftmax(-1)
def __init__(self):
super(Stable_softplus, self).__init__()
self.log_op = P.Log()
self.abs_op = P.Abs()
self.relu_op = P.ReLU()
self.exp_op = P.Exp()
def __init__(self):
super().__init__()
self.exp = ops.Exp()
self.reduce_sum = ops.ReduceSum(keep_dims=True)
def __init__(self, alpha=1.0):
super().__init__()
self.alpha = alpha
self.exp = ops.Exp()
self.max = ops.Maximum()
self, min = ops.Minimum()