NegativeLogLikelihoodLoss#

NegativeLogLikelihoodLoss - 13#

Version

This version of the operator has been available since version 13.

Summary

A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its “input” tensor has the shape of (N, C, d1, d2, …, dk) where k >= 0. The “input” tensor contains log-probabilities for input[n, :, d_1, d_2,…, d_k] being in a class of [0, C). The operator’s “target” input tensor has the shape of (N, d1, d2, …, dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x … x dk samples. The loss value for input[n, :, d_1, d_2,…d_k] being classified as class c = target[n][d_1][d_2]…[d_k] is computed as:

loss[n][d_1][d_2]…[d_k] = -input[n][c][d_1][d_2]…[d_k].

When an optional “weight” is provided, the sample loss is calculated as:

loss[n][d_1][d_2]…[d_k] = -input[n][c][d_1][d_2]…[d_k] * weight[c].

loss is zero for the case when target-value equals ignore_index.

loss[n][d_1][d_2]…[d_k] = 0, when target[n][d_1][d_2]…[d_k] = ignore_index

If “reduction” attribute is set to “none”, the operator’s output will be the above loss with shape (N, d1, d2, …, dk). If “reduction” attribute is set to “mean” (the default attribute value), the output loss is (weight) averaged:

mean(loss), if “weight” is not provided,

or if weight is provided,

sum(loss) / sum(weight[target[n][d_1][d_2]…[d_k]]]), for all samples.

If “reduction” attribute is set to “sum”, the output is a scalar:

sum(loss).

See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss.

Example 1:

// negative log likelihood loss, “none” reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],

[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]

target = [[2, 1], [0, 2]]

loss = np.zeros((N, d1)) for n in range(N):

for d_1 in range(d1):

c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1]

// print(loss) // [[-3. -2.] // [-0. -2.]]

Example 2:

// weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],

[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]

target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N):

for d_1 in range(d1):

c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c]

loss = np.sum(loss) // print(loss) // -1.1

Example 3:

// weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],

[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]

target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N):

for d_1 in range(d1):

c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c]

loss = np.sum(loss) / weight_total // print(loss) // -1.57

Attributes

  • ignore_index: Specifies a target value that is ignored and does not contribute to the input gradient. It’s an optional value.

  • reduction: Type of reduction to apply to loss: none, sum, mean (default). ‘none’: the output is the loss for each sample. ‘sum’: the output will be summed. ‘mean’: the sum of the output will be divided by the sum of applied weights.

Inputs

Between 2 and 3 inputs.

  • input (heterogeneous) - T: Input tensor of shape (N, C) or (N, C, d1, d2, …, dk).

  • target (heterogeneous) - Tind: Target tensor of shape (N) or (N, d1, d2, …, dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.

  • weight (optional, heterogeneous) - T: Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.

Outputs

  • loss (heterogeneous) - T: The negative log likelihood loss

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input, weight, and output types to floating-point tensors.

  • Tind in ( tensor(int32), tensor(int64) ): Constrain target to integer types

Examples

_input_shape_is_NC

import numpy as np
import onnx

reduction = "none"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
)

N, C = 3, 5
np.random.seed(0)
input = np.random.rand(N, C).astype(np.float32)
target = np.random.randint(0, high=C, size=(N,)).astype(np.int64)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=None, reduction=reduction
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NC",
)

_input_shape_is_NCd1d2

import numpy as np
import onnx

reduction = "none"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=None, reduction=reduction
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2",
)

_input_shape_is_NCd1d2_reduction_mean

import numpy as np
import onnx

reduction = "mean"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=None, reduction=reduction
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2_reduction_mean",
)

_input_shape_is_NCd1d2_reduction_sum

import numpy as np
import onnx

reduction = "sum"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=None, reduction=reduction
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2_reduction_sum",
)

_input_shape_is_NCd1d2_with_weight

import numpy as np
import onnx

reduction = "none"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2_with_weight",
)

_input_shape_is_NCd1d2_with_weight_reduction_mean

import numpy as np
import onnx

reduction = "mean"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2_with_weight_reduction_mean",
)

_input_shape_is_NCd1d2_with_weight_reduction_sum

import numpy as np
import onnx

reduction = "sum"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2_with_weight_reduction_sum",
)

_input_shape_is_NCd1d2_with_weight_reduction_sum_ii

import numpy as np
import onnx

reduction = "sum"
ignore_index = np.int64(0)
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
    ignore_index=ignore_index,
)

N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
target[0][0][0] = np.int64(0)
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2_with_weight_reduction_sum_ii",
)

_input_shape_is_NCd1d2_no_weight_reduction_mean_ii

import numpy as np
import onnx

reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
    ignore_index=ignore_index,
)

N, C, dim1, dim2 = 3, 5, 6, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2)).astype(np.int64)
target[0][0][0] = np.int64(1)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, reduction=reduction, ignore_index=ignore_index
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2_no_weight_reduction_mean_ii",
)

_input_shape_is_NCd1

import numpy as np
import onnx

reduction = "mean"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=None, reduction=reduction
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1",
)

_input_shape_is_NCd1_weight

import numpy as np
import onnx

reduction = "mean"
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1_weight",
)

_input_shape_is_NCd1_ii

import numpy as np
import onnx

reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
    ignore_index=ignore_index,
)

N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
target[0][0] = np.int64(1)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=None, reduction=reduction, ignore_index=ignore_index
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1_ii",
)

_input_shape_is_NCd1_weight_ii

import numpy as np
import onnx

reduction = "mean"
ignore_index = np.int64(1)
node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
    ignore_index=ignore_index,
)

N, C, d1 = 3, 5, 2
np.random.seed(0)
input = np.random.rand(N, C, d1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, d1)).astype(np.int64)
target[0][0] = np.int64(1)
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1_weight_ii",
)

_input_shape_is_NCd1d2d3d4d5_mean_weight

import numpy as np
import onnx

reduction = "mean"

node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
target = np.random.randint(
    0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2d3d4d5_mean_weight",
)

_input_shape_is_NCd1d2d3d4d5_none_no_weight

import numpy as np
import onnx

reduction = "none"

node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
)

N, C, dim1, dim2, dim3, dim4, dim5 = 3, 5, 6, 6, 5, 3, 4
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3, dim4, dim5).astype(np.float32)
target = np.random.randint(
    0, high=C, size=(N, dim1, dim2, dim3, dim4, dim5)
).astype(np.int64)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, reduction=reduction
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2d3d4d5_none_no_weight",
)

_input_shape_is_NCd1_mean_weight_negative_ii

import numpy as np
import onnx

reduction = "mean"
ignore_index = np.int64(-1)

node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
    ignore_index=ignore_index,
)

N, C, dim1 = 3, 5, 6
np.random.seed(0)
input = np.random.rand(N, C, dim1).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1)).astype(np.int64)
target[0][0] = -1
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1_mean_weight_negative_ii",
)

_input_shape_is_NCd1d2d3_none_no_weight_negative_ii

import numpy as np
import onnx

reduction = "none"
ignore_index = np.int64(-5)

node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target"],
    outputs=["loss"],
    reduction=reduction,
    ignore_index=ignore_index,
)

N, C, dim1, dim2, dim3 = 3, 5, 6, 6, 5
np.random.seed(0)
input = np.random.rand(N, C, dim1, dim2, dim3).astype(np.float32)
target = np.random.randint(0, high=C, size=(N, dim1, dim2, dim3)).astype(
    np.int64
)
target[0][0][0][0] = -5

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, reduction=reduction, ignore_index=ignore_index
)

expect(
    node,
    inputs=[input, target],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2d3_none_no_weight_negative_ii",
)

_input_shape_is_NCd1d2d3_sum_weight_high_ii

import numpy as np
import onnx

reduction = "sum"
ignore_index = np.int64(10)

node = onnx.helper.make_node(
    "NegativeLogLikelihoodLoss",
    inputs=["input", "target", "weight"],
    outputs=["loss"],
    reduction=reduction,
    ignore_index=ignore_index,
)

N, C = 3, 5
np.random.seed(0)
input = np.random.rand(N, C).astype(np.float32)
target = np.random.randint(0, high=C, size=(N)).astype(np.int64)
target[0] = 10
weight = np.random.rand(C).astype(np.float32)

negative_log_likelihood_loss = compute_negative_log_likelihood_loss(
    input, target, weight=weight, reduction=reduction, ignore_index=ignore_index
)

expect(
    node,
    inputs=[input, target, weight],
    outputs=[negative_log_likelihood_loss],
    name="test_nllloss_NCd1d2d3_sum_weight_high_ii",
)

NegativeLogLikelihoodLoss - 12#

Version

This version of the operator has been available since version 12.

Summary

A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. Its “input” tensor has the shape of (N, C, d1, d2, …, dk) where k >= 0. The “input” tensor contains log-probabilities for input[n, :, d_1, d_2,…, d_k] being in a class of [0, C). The operator’s “target” input tensor has the shape of (N, d1, d2, …, dk). It encodes class labels (one of C classes) or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x … x dk samples. The loss value for input[n, :, d_1, d_2,…d_k] being classified as class c = target[n][d_1][d_2]…[d_k] is computed as:

loss[n][d_1][d_2]…[d_k] = -input[n][c][d_1][d_2]…[d_k].

When an optional “weight” is provided, the sample loss is calculated as:

loss[n][d_1][d_2]…[d_k] = -input[n][c][d_1][d_2]…[d_k] * weight[c].

loss is zero for the case when target-value equals ignore_index.

loss[n][d_1][d_2]…[d_k] = 0, when target[n][d_1][d_2]…[d_k] = ignore_index

If “reduction” attribute is set to “none”, the operator’s output will be the above loss with shape (N, d1, d2, …, dk). If “reduction” attribute is set to “mean” (the default attribute value), the output loss is (weight) averaged:

mean(loss), if “weight” is not provided,

or if weight is provided,

sum(loss) / sum(weight[target[n][d_1][d_2]…[d_k]]]), for all samples.

If “reduction” attribute is set to “sum”, the output is a scalar:

sum(loss).

See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. Example 1:

// negative log likelihood loss, “none” reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],

[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]

target = [[2, 1], [0, 2]] loss = np.zeros((N, d1)) for n in range(N):

for d_1 in range(d1):

c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1]

// print(loss) // [[-3. -2.] // [-0. -2.]]

Example 2:

// weighted negative log likelihood loss, sum reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],

[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]

target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) for n in range(N):

for d_1 in range(d1):

c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c]

loss = np.sum(loss) // print(loss) // -1.1

Example 3:

// weighted negative log likelihood loss, mean reduction N, C, d1 = 2, 3, 2 input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]],

[[0.0, 1.0], [2.0, 2.0], [1.0, 2]]]

target = [[2, 1], [0, 2]] weight = [0.2, 0.3, 0.1] loss = np.zeros((N, d1)) weight_total = 0 for n in range(N):

for d_1 in range(d1):

c = target[n][d_1] loss[n][d_1] = -input[n][c][d_1] * weight[c] weight_total = weight_total + weight[c]

loss = np.sum(loss) / weight_total // print(loss) // -1.57

Attributes

  • ignore_index: Specifies a target value that is ignored and does not contribute to the input gradient. It’s an optional value.

  • reduction: Type of reduction to apply to loss: none, sum, mean (default). ‘none’: the output is the loss for each sample. ‘sum’: the output will be summed. ‘mean’: the sum of the output will be divided by the sum of applied weights.

Inputs

Between 2 and 3 inputs.

  • input (heterogeneous) - T: Input tensor of shape (N, C) or (N, C, d1, d2, …, dk).

  • target (heterogeneous) - Tind: Target tensor of shape (N) or (N, d1, d2, …, dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index.

  • weight (optional, heterogeneous) - T: Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones.

Outputs

  • loss (heterogeneous) - T: The negative log likelihood loss

Type Constraints

  • T in ( tensor(double), tensor(float), tensor(float16) ): Constrain input, weight, and output types to floating-point tensors.

  • Tind in ( tensor(int32), tensor(int64) ): Constrain target to integer types