NegativeLogLikelihoodLoss#
NegativeLogLikelihoodLoss - 13#
Version
domain: main
since_version: 13
function: False
support_level: SupportType.COMMON
shape inference: True
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. Default value is
'mean'
.
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
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
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
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
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))
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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))
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')
Differences
0 | 0 | A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. | A NegativeLogLikelihoodLoss operator computes (weighted) negative log likelihood loss. |
1 | 1 | Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. | Its "input" tensor has the shape of (N, C, d1, d2, ..., dk) where k >= 0. |
2 | 2 | The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). | The "input" tensor contains log-probabilities for input[n, :, d_1, d_2,..., d_k] being in a class of [0, C). |
3 | 3 | The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) | The operator's "target" input tensor has the shape of (N, d1, d2, ..., dk). It encodes class labels (one of C classes) |
4 | 4 | or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. | or it may contain a special value (indicated by an attribute ignore_index) for N x d1 x d2 x ... x dk samples. |
5 | 5 | 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: | 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: |
6 |
| ||
6 | 7 | loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. | loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k]. |
8 |
| ||
7 | 9 | When an optional "weight" is provided, the sample loss is calculated as: | When an optional "weight" is provided, the sample loss is calculated as: |
10 |
| ||
8 | 11 | loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. | loss[n][d_1][d_2]...[d_k] = -input[n][c][d_1][d_2]...[d_k] * weight[c]. |
12 |
| ||
9 | 13 | loss is zero for the case when target-value equals ignore_index. | loss is zero for the case when target-value equals ignore_index. |
10 | 14 |
|
|
11 | 15 | loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index | loss[n][d_1][d_2]...[d_k] = 0, when target[n][d_1][d_2]...[d_k] = ignore_index |
16 |
| ||
12 | 17 | 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 "none", the operator's output will be the above loss with shape (N, d1, d2, ..., dk). |
13 | 18 | If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: | If "reduction" attribute is set to "mean" (the default attribute value), the output loss is (weight) averaged: |
19 |
| ||
14 | 20 | mean(loss), if "weight" is not provided, | mean(loss), if "weight" is not provided, |
21 |
| ||
15 | 22 | or if weight is provided, | or if weight is provided, |
23 |
| ||
16 | 24 | sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. | sum(loss) / sum(weight[target[n][d_1][d_2]...[d_k]]]), for all samples. |
25 |
| ||
17 | 26 | If "reduction" attribute is set to "sum", the output is a scalar: | If "reduction" attribute is set to "sum", the output is a scalar: |
18 | 27 | sum(loss). | sum(loss). |
28 |
| ||
19 | 29 | See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. | See also https://pytorch.org/docs/stable/nn.html#torch.nn.NLLLoss. |
30 |
| ||
20 | 31 | Example 1: | Example 1: |
32 |
| ||
21 | 33 | // negative log likelihood loss, "none" reduction | // negative log likelihood loss, "none" reduction |
22 | 34 | N, C, d1 = 2, 3, 2 | N, C, d1 = 2, 3, 2 |
23 | 35 | input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], | input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], |
24 | 36 | [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] | [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] |
25 | 37 | target = [[2, 1], [0, 2]] | target = [[2, 1], [0, 2]] |
38 |
| ||
26 | 39 | loss = np.zeros((N, d1)) | loss = np.zeros((N, d1)) |
27 | 40 | for n in range(N): | for n in range(N): |
28 | 41 | for d_1 in range(d1): | for d_1 in range(d1): |
29 | 42 | c = target[n][d_1] | c = target[n][d_1] |
30 | 43 | loss[n][d_1] = -input[n][c][d_1] | loss[n][d_1] = -input[n][c][d_1] |
44 |
| ||
31 | 45 | // print(loss) | // print(loss) |
32 | 46 | // [[-3. -2.] | // [[-3. -2.] |
33 | 47 | // [-0. -2.]] | // [-0. -2.]] |
48 |
| ||
34 | 49 | Example 2: | Example 2: |
50 |
| ||
35 | 51 | // weighted negative log likelihood loss, sum reduction | // weighted negative log likelihood loss, sum reduction |
36 | 52 | N, C, d1 = 2, 3, 2 | N, C, d1 = 2, 3, 2 |
37 | 53 | input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], | input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], |
38 | 54 | [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] | [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] |
39 | 55 | target = [[2, 1], [0, 2]] | target = [[2, 1], [0, 2]] |
40 | 56 | weight = [0.2, 0.3, 0.1] | weight = [0.2, 0.3, 0.1] |
41 | 57 | loss = np.zeros((N, d1)) | loss = np.zeros((N, d1)) |
42 | 58 | for n in range(N): | for n in range(N): |
43 | 59 | for d_1 in range(d1): | for d_1 in range(d1): |
44 | 60 | c = target[n][d_1] | c = target[n][d_1] |
45 | 61 | loss[n][d_1] = -input[n][c][d_1] * weight[c] | loss[n][d_1] = -input[n][c][d_1] * weight[c] |
62 |
| ||
46 | 63 | loss = np.sum(loss) | loss = np.sum(loss) |
47 | 64 | // print(loss) | // print(loss) |
48 | 65 | // -1.1 | // -1.1 |
66 |
| ||
49 | 67 | Example 3: | Example 3: |
68 |
| ||
50 | 69 | // weighted negative log likelihood loss, mean reduction | // weighted negative log likelihood loss, mean reduction |
51 | 70 | N, C, d1 = 2, 3, 2 | N, C, d1 = 2, 3, 2 |
52 | 71 | input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], | input = [[[1.0, 2.0], [2.0, 2.0], [3.0, 2.0]], |
53 | 72 | [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] | [[0.0, 1.0], [2.0, 2.0], [1.0, 2]]] |
54 | 73 | target = [[2, 1], [0, 2]] | target = [[2, 1], [0, 2]] |
55 | 74 | weight = [0.2, 0.3, 0.1] | weight = [0.2, 0.3, 0.1] |
56 | 75 | loss = np.zeros((N, d1)) | loss = np.zeros((N, d1)) |
57 | 76 | weight_total = 0 | weight_total = 0 |
58 | 77 | for n in range(N): | for n in range(N): |
59 | 78 | for d_1 in range(d1): | for d_1 in range(d1): |
60 | 79 | c = target[n][d_1] | c = target[n][d_1] |
61 | 80 | loss[n][d_1] = -input[n][c][d_1] * weight[c] | loss[n][d_1] = -input[n][c][d_1] * weight[c] |
62 | 81 | weight_total = weight_total + weight[c] | weight_total = weight_total + weight[c] |
82 |
| ||
63 | 83 | loss = np.sum(loss) / weight_total | loss = np.sum(loss) / weight_total |
64 | 84 | // print(loss) | // print(loss) |
65 | 85 | // -1.57 | // -1.57 |
66 | 86 |
|
|
67 | 87 | **Attributes** | **Attributes** |
68 | 88 |
|
|
69 | 89 | * **ignore_index**: | * **ignore_index**: |
70 | 90 | Specifies a target value that is ignored and does not contribute to | Specifies a target value that is ignored and does not contribute to |
71 | 91 | the input gradient. It's an optional value. | the input gradient. It's an optional value. |
72 | 92 | * **reduction**: | * **reduction**: |
73 | 93 | Type of reduction to apply to loss: none, sum, mean (default). | Type of reduction to apply to loss: none, sum, mean (default). |
74 | 94 | 'none': the output is the loss for each sample. 'sum': the output | 'none': the output is the loss for each sample. 'sum': the output |
75 | 95 | will be summed. 'mean': the sum of the output will be divided by the | will be summed. 'mean': the sum of the output will be divided by the |
76 | 96 | sum of applied weights. Default value is 'mean'. | sum of applied weights. Default value is 'mean'. |
77 | 97 |
|
|
78 | 98 | **Inputs** | **Inputs** |
79 | 99 |
|
|
80 | 100 | Between 2 and 3 inputs. | Between 2 and 3 inputs. |
81 | 101 |
|
|
82 | 102 | * **input** (heterogeneous) - **T**: | * **input** (heterogeneous) - **T**: |
83 | 103 | Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk). | Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk). |
84 | 104 | * **target** (heterogeneous) - **Tind**: | * **target** (heterogeneous) - **Tind**: |
85 | 105 | Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element | Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element |
86 | 106 | value shall be in range of [0, C). If ignore_index is specified, it | value shall be in range of [0, C). If ignore_index is specified, it |
87 | 107 | may have a value outside [0, C) and the target values should either | may have a value outside [0, C) and the target values should either |
88 | 108 | be in the range [0, C) or have the value ignore_index. | be in the range [0, C) or have the value ignore_index. |
89 | 109 | * **weight** (optional, heterogeneous) - **T**: | * **weight** (optional, heterogeneous) - **T**: |
90 | 110 | Optional rescaling weight tensor. If given, it has to be a tensor of | Optional rescaling weight tensor. If given, it has to be a tensor of |
91 | 111 | size C. Otherwise, it is treated as if having all ones. | size C. Otherwise, it is treated as if having all ones. |
92 | 112 |
|
|
93 | 113 | **Outputs** | **Outputs** |
94 | 114 |
|
|
95 | 115 | * **loss** (heterogeneous) - **T**: | * **loss** (heterogeneous) - **T**: |
96 | 116 | The negative log likelihood loss | The negative log likelihood loss |
97 | 117 |
|
|
98 | 118 | **Type Constraints** | **Type Constraints** |
99 | 119 |
|
|
100 | 120 | * **T** in ( | * **T** in ( |
101 | 121 | tensor(double), | tensor(double), |
102 | 122 | tensor(float), | tensor(float), |
103 | 123 | tensor(float16) | tensor(float16) |
104 | 124 | ): | ): |
105 | 125 | Constrain input, weight, and output types to floating-point tensors. | Constrain input, weight, and output types to floating-point tensors. |
106 | 126 | * **Tind** in ( | * **Tind** in ( |
107 | 127 | tensor(int32), | tensor(int32), |
108 | 128 | tensor(int64) | tensor(int64) |
109 | 129 | ): | ): |
110 | 130 | Constrain target to integer types | Constrain target to integer types |
NegativeLogLikelihoodLoss - 12#
Version
domain: main
since_version: 12
function: False
support_level: SupportType.COMMON
shape inference: True
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. Default value is
'mean'
.
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


{# TODO: Fill in later #}
{% load staticfiles %}
{% include "blocks/includes/css_includes_distrib.html" %}
{% include "blocks/includes/css_includes_project.html" %}
Agiler :: {% block title %}{% endblock %}
{# HTML5 shim and Respond.js IE8 support of HTML5 elements and media queries #}


In the beginning was the Word. Western civilization rests upon those words. And yet there is a lively group of thinkers who believe that in the beginning was the Act. that nothing can precede action—no breath before act, no thought before act, no pervasive love before some kind of act.
Next


User stories:
1) I can pass a URL as a parameter and I will receive a shortened URL in the JSON response.
2) If I pass an invalid URL that doesn't follow the valid http://www.example.com format,
the JSON response will contain an error instead.
3) When I visit that shortened URL, it will redirect me to my original link.
Creation
Example usage:
https://fcc-backend-urlshortener-slkinnison.c9users.io/api/new/https://www.google.com
https://fcc-backend-urlshortener-slkinnison.c9users.io/api/new/http://freecodecamp.com/news
If you want to pass a site that doesn't exist (or an invalid url) for some reason you can do:
https://fcc-backend-urlshortener-slkinnison.c9users.io/api/new/invalid?allow=true
Example output:
{
"original_url": "http://freecodecamp.com/news",
"short_url": "https://fcc-backend-urlshortener-slkinnison.c9users.io/api/7"
}
Redirection
Example usage:
https://fcc-backend-urlshortener-slkinnison.c9users.io/api/7
Will redirect to
http://freecodecamp.com/news


"x" "1" "WANG,CJ, LIN,YL AND LIN,JK, MUTAGENICITY AND CYTOTOXICITY OFNITROPYRROLE COMPOUNDS DERIVED FROM THE REACTION OF 2-ACETYLPYRROLE WITHNITRITE, FOOD CHEM. TOXICOL. 32(9):839-844, 1994"


We extend interfaces!
- architect mission-critical experiences
- strategize dynamic web-readiness
- recontextualize holistic partnerships
- disintermediate e-business initiatives
- brand viral schemas
- strategize proactive platforms
- leverage customized functionalities
- aggregate vertical convergence
- drive innovative ROI
- reinvent global functionalities
- monetize innovative web services
- implement distributed bandwidth
- facilitate best-of-breed solutions
- disintermediate impactful methodologies
- enable magnetic mindshare


EMT: Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Multivariate data
The package provides functions to carry out a
Goodness-of-fit test for discrete multivariate data. It is
tested if a given observation is likely to have occurred under
the assumption of an ab-initio model. A p-value can be
calculated using different distance measures between observed
and expected frequencies. A Monte Carlo method is provided to
make the package capable of solving high-dimensional problems.
| Version: |
1.1 |
| Published: |
2013-01-29 |
| Author: |
Uwe Menzel |
| Maintainer: |
Uwe Menzel <uwemenzel at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: |
no |
| CRAN checks: |
EMT results |
Downloads:
Reverse dependencies:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=EMT
to link to this page.


{% extends "material_en/base-layout.html" %} {% load static %} {% block head %} {% endblock %} {% block ofbar %} {% endblock %} {% block content %}
Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
Find more...Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
Find more...Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
Find more...The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
Divide details about your product or agency work into parts. Write a few lines about each one. A paragraph describing a feature will be enough.
The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
The moment you use Material Kit, you know you’ve never felt anything like it. With a single use, this powerfull UI Kit lets you do more than ever before.
Like so many organizations these days, Autodesk is a company in transition. It was until recently a traditional boxed software company selling licenses. Today, it’s moving to a subscription model. Yet its own business model disruption is only part of the story — and… Read More
Like so many organizations these days, Autodesk is a company in transition. It was until recently a traditional boxed software company selling licenses. Today, it’s moving to a subscription model. Yet its own business model disruption is only part of the story — and… Read More
Like so many organizations these days, Autodesk is a company in transition. It was until recently a traditional boxed software company selling licenses. Read More
Like so many organizations these days, Autodesk is a company in transition. It was until recently a traditional boxed software company selling licenses. Read More
Like so many organizations these days, Autodesk is a company in transition. It was until recently a traditional boxed software company selling licenses. Read More
Like so many organizations these days, Autodesk is a company in transition. It was until recently a traditional boxed software company selling licenses. Yet its own business model disruption is only part of the story — and… Read More
Like so many organizations these days, Autodesk is a company in transition. It was until recently a traditional boxed software company selling licenses. Yet its own business model disruption is only part of the story — and… Read More
Like so many organizations these days, Autodesk is a company in transition. It was until recently a traditional boxed software company selling licenses. Yet its own business model disruption is only part of the story — and… Read More
Don't be scared of the truth because we need to restart the human foundation in truth And I love you like Kanye loves Kanye I love Rick Owens’ bed design but the back is...
content_copy View AppDon't be scared of the truth because we need to restart the human foundation in truth And I love you like Kanye loves Kanye I love Rick Owens’ bed design but the back is...
build View ProjectDon't be scared of the truth because we need to restart the human foundation in truth And I love you like Kanye loves Kanye I love Rick Owens’ bed design but the back is...
subject Case StudyMaterial Kit is a Free Bootstrap UI Kit with a fresh, new design inspired by Google's material design. It's a great pleasure to introduce to you the material concepts in an easy to use and beautiful set of components.
Light Bootstrap Dashboard PRO is a Bootstrap Admin Theme designed to look simple and beautiful. Forget about boring dashboards and grab yourself a copy to kickstart new project!
Get Shit Done Kit Pro it's a Bootstrap Kit that comes with a huge number of customisable components. They are pixel perfect, light and easy to use and combine with other elements.
Don't be scared of the truth because we need to restart the human foundation in truth.
Don't be scared of the truth because we need to restart the human foundation in truth.
Don't be scared of the truth because we need to restart the human foundation in truth.
Don't be scared of the truth because we need to restart the human foundation in truth.
Don't be scared of the truth because we need to restart the human foundation in truth.
We've created the design of the website using modern Technologies like Sketch. It was a very interesting collaboration.
We've developed the website with HTML5 and CSS3. The client has access to the code using GitHub.
There is also a Fully Customizable CMS Admin Dashboard for this product.
We've created the marketing campaign of the website. It was a very interesting collaboration.
We've developed the website with HTML5 and CSS3. The client has access to the code using GitHub.
There is also a Fully Customizable CMS Admin Dashboard for this product.
Don't be scared of the truth because we need to restart the human foundation in truth.


public class MinesweeperBoard
extends java.lang.Object
implements java.io.Serializable
| Modifier and Type | Field and Description |
|---|---|
static int |
DNE
Locations that do not exist (DNE) will have a value = -3 in visibleBoard.
|
static int |
FLAG
Locations with flags will have a value = -2 in visibleBoard.
|
static int |
INVISIBLE
Invisible locations will have value = 0 in visibleBoard.
|
static int |
MINE
Locations with mines will have a value = -1 in valBoard.
|
static int |
VISIBLE
Visible locations will have value = 1 in visibleBoard.
|
| Constructor and Description |
|---|
MinesweeperBoard(int N,
int M,
java.lang.String level,
java.lang.String shape)
Constructs a new instance of MinsweeperBoard.
|
| Modifier and Type | Method and Description |
|---|---|
void |
addFlag()
Increases the value representing the number of flags placed.
|
void |
addMine(OrderedPair pair)
Method that adds a new mine to the board.
|
java.util.ArrayList<OrderedPair> |
getAdjacencies(OrderedPair op)
Method that return an ArrayList containing OrderedPair locations for all the valid
grid spots bordering the given OrderedPair in this instance of MinesweeperBoard
|
java.lang.String |
getDifficulty()
Returns the difficulty associated with the current board.
|
java.util.HashSet<OrderedPair> |
getDNEs()
Method that return a HashSet containing OrderedPair locations for all the DNE
locations in this instance of MinesweeperBoard
|
boolean |
getInit()
Returns true if the board has been initialized, false otherwise.
|
java.util.HashSet<OrderedPair> |
getMineLocs()
Method that return a HashSet containing OrderedPair locations for all the mines
in this instance of MinesweeperBoard
|
int |
getNumFlags()
Return the number of flags count.
|
int |
getScore()
Returns the current score associated with the board.
|
java.lang.String |
getShape()
Returns the current shape of the board.
|
int |
getVal(int row,
int col)
Method that returns the value stored at the specified location in the
MinesweeperBoard
|
int |
getVisible(int row,
int col)
Method that gets the current visibility status at the passed in row and col
location
|
int |
numCols()
Method that returns the number of columns in the rows that this instance of MinesweeperBoard has
This number will correspond with the passed in M parameter when creating a new instance
of the MinesweeperBoard class using the two argument constructor
|
int |
numRows()
Method that returns the number of rows that this instance of MinesweeperBoard has
This number will correspond with the passed in N parameter when creating a new instance
of the MinesweeperBoard class using the two argument constructor
|
int |
numVisible()
Method that returns the number of squares that are visible to the user
|
void |
putVal(int row,
int col,
int val)
Method that puts the given value into the instance of the MinesweeperBoard
|
void |
putVisible(int row,
int col,
int vis)
Method that puts or updates the visibility status at the (row, col) position of the
visible board
Method also creates and adds a new OrderedPair object into the visibles ArrayList
which stores all the locations on the board that has been revealed to the player
|
void |
setInit(boolean init)
Sets the initialized field to the given boolean value.
|
void |
setNumFlags(int num)
Set the number of flags to the specified number.
|
void |
setScore(int currScore)
Sets the player's current game runtime as the score.
|
void |
subFlag()
Decreases the value representing the number of flags placed.
|
public static final int VISIBLE
public static final int INVISIBLE
public static final int MINE
public static final int FLAG
public static final int DNE
public MinesweeperBoard(int N, int M, java.lang.String level, java.lang.String shape)
N
- The number of rows in the board.M
- The number of columns in the board.level
- The difficulty of the board.shape
- The shape of the board to build.public void setScore(int currScore)
currScore
- Player's current game runtime to save.public int getScore()
public void subFlag()
public void addFlag()
public int getNumFlags()
public void setNumFlags(int num)
num
- The number of flags value to save.public java.lang.String getDifficulty()
public java.lang.String getShape()
public boolean getInit()
public void setInit(boolean init)
init
- boolean value to set initialized to.public int getVal(int row, int col)
row
- int row of position being consideredcol
- int column of position being consideredpublic void putVal(int row, int col, int val)
row
- int row of position being consideredcol
- int column of position being consideredval
- int value to be placed in instance of Boardpublic int getVisible(int row, int col)
row
- int row of position being consideredcol
- int column of position being consideredpublic void putVisible(int row, int col, int vis)
row
- int row of position being consideredcol
- int column of position being consideredvis
- int new visibility status for the (row, col) position on the boardpublic int numVisible()
public int numRows()
public int numCols()
public void addMine(OrderedPair pair)
pair
- OrderedPair position of the new mine to be addedpublic java.util.HashSet<OrderedPair> getMineLocs()
public java.util.HashSet<OrderedPair> getDNEs()
public java.util.ArrayList<OrderedPair> getAdjacencies(OrderedPair op)
op
- OrderedPair representing the position whose adjacency list is being returned


public class CloneNotSupportedException extends Exception
Object.clone()
is called on an object
which does not implement the Cloneable
interface. For example:void m() throws CloneNotSupportedException { clone(); }
Notice that calling clone()
on an array will never produce
this exception, as the VM will always succeed in copying the array, or
cause an OutOfMemoryError first. For example:
void m(int[] array) { int[] copy = (int[]) array.clone(); }
Cloneable
,
Object.clone()
| Constructor and Description |
|---|
CloneNotSupportedException()
Create an exception without a message.
|
CloneNotSupportedException(String s)
Create an exception with a message.
|
addSuppressed, getCause, getMessage, getSuppressed, initCause, printStackTrace, printStackTrace, toString
public CloneNotSupportedException()
public CloneNotSupportedException(String s)
s
- the error message


Welcome Pizza Lovers!
Hours: Monday - Friday 10am-4pm
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Contact
(602) 275-5181
326 N. 48th Street Phoenix, AZ 85008
<EMAIL_ADDRESS>2016 Jerome
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Abstract
In R we can do a lot with data tables. There are plenty of ways to import tabular data and then transform it for our needs. Lately, there’s been an explosion of new R packages that allow us to take these data tables and make them presentable in publication contexts. A package Rich Iannone has been working on, called gt, is great for taking a data frame and making it look good (and more like a table you’d find on the web or in print). Rich will show us some examples of how the gt package works, where it works well, and where other packages might do better for specific needs. This introduction will help prepare you to try out gt and some of the other table-making packages in the R ecosystem.


by Andrew Hand
Posted on December 14, 2018 at 9:00 PM
All projects begin with an idea. It's something we initially can't put into words, but the concept exists. To bring it to life is a process; one we can all follow. The following is my personal experience for when I have an idea that I need to bring to life.
When I have an idea words are no good, but a picture says everything I need and more. Documenting my thoughts as sketches or dynamic visuals allows myself to ponder the meaning of my idea and solidify my concept of the goal. Putting the element of an idea on the page allows me to visually sort the aspects of my idea and conceptualize the complex components that will form from combining each element. Once I begin to understand my own idea, then there is direction and I can begin designing with a goal.
Taking a page of scribbles and sketches and giving them functionality is the next logical step. Just like any working theory, there is also practice, and often practice leads to results. It's one thing to think of an idea in your head and begin to understand it, but it's another to see it, touch it, and truly experience it firsthand. However, creating your final product should not be rushed. To be sure of your design before it's even been created is nothing short of overconfidence. Instead, bring the idea to reality should be the inspiration used to make something even better.
When your idea has drawn its first breath put it to the test. Test it yourself and see how it feels, but let others be the judge of your work because we are blind to our own faults. Listen to their feedback and address their concerns to strengthen your design.
Reflect on what you've learned. There is always room for improvement, something I learned after I started asking: what do you think? Once you begin to design you soon learn the process never truly ends. Eventually, you reach a point where your product is "finished" by context, but not by definition. Once we begin to create, we relate, and the message we spread is fluid throughout our all designs. It is my belief people create to express, whether it be their idealism, emotion, or hopes there is a story that they tell that won't end until they can't create any more.
"Have no fear of perfection — you’ll never reach it."
A special thank you to all my peers who helped me design these projects along the way. I hope you learned as much as me.


Wishful Thinking * * (out of * * * * )
Directed by Ray Parks. Cast: James LeGros, Jennifer Beals, Drew Barrymore, Jon Stewart, Eric Thal. 1999 - 89 minutes. Rated R (for profanity and sexual situations). Capsule Review - July 19, 1999.
One of several movies Drew Barrymore made in 1997 that dodged the big screen and are only now reaching home video, Adam Park's "Wishful Thinking" is at least a step above "Best Men," which is also currently available on video, but that isn't saying much. The film is clearly an indie and not a big-budget studio picture, which may have been another reason it bypassed theaters, as it recounts the troubles that a close couple are having from three different points-of-view: Max (James LeGros), the jealous boyfriend; Elizabeth (Jennifer Beals), the misunderstood girlfriend; and Lena (Drew Barrymore), Max's co-worker at a movie theater who pines for him from afar. All three leads are charismatic (particularly Beals, who deserves more film roles), and the unorthodox structure is similar to "Pulp Fiction" and "Go," but the screenplay, including the dialogue, too often wanders into unnecessary silliness, and the ending leaves something to be desired. Jon Stewart, as a good-natured man Elizabeth falls for, is a standout. Too bad the movie itself isn't as entertaining as he is.
- Copyright 1999 by Dustin Putman Http://hometown.aol.com/FilmFan16/index.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


My First Post
section name: posts
This is the my first post.
- section
sub section 1
- list
- nested list
- list
- number list
- number list
- code block
package main
import "fmt"
func main () {
fmt.Println("hello world")
}
| title | desc |
|---|---|
| this is titile | this is description |


---
layout: portal
category: reject
tags: reject tooclose monument
id: 8
imgurl: https://lh5.ggpht.com/JbdM4WC6IBdb1w87V8gSsIgvGd7F_K5AYYdKkSld09beR_Rd4eKMIG3FoHV5EHHXKnHBHcg66Qh78KRX8LUP
portalname: 藤田翁遺徳碑
sentdate: Tue Dec 30 20:09:01 JST 2014
title: 8 - 藤田翁遺徳碑
location: "35.5886509,139.4675428"
---
From: Ingress Operations<EMAIL_ADDRESS>Date: 2014-12-12 22:44 GMT+09:00
Subject: Ingress Portal Rejected: 藤田翁遺徳碑
Thank you for your Ingress Portal candidate. Unfortunately, this Portal is too close to another existing Portal to be safely hacked and cannot be enabled at this time.
For additional information, please visit the Ingress Intel Map or our Help Center.


| PERLHACKTUT(1) | Perl Programmers Reference Guide | PERLHACKTUT(1) |
If you haven't read perlhack yet, go do that first! You might also want to read through perlsource too.
Once you're done here, check out perlhacktips next.
Here's something Larry suggested: if a "U" is the first active format during a "pack", (for example, "pack "U3C8", @stuff") then the resulting string should be treated as UTF-8 encoded.
If you are working with a git clone of the Perl repository, you will want to create a branch for your changes. This will make creating a proper patch much simpler. See the perlgit for details on how to do this.
[Well, it was in pp.c when this tutorial was written. It has now been split off with "pp_unpack" to its own file, pp_pack.c]
Now let's look over "pp_pack": we take a pattern into "pat", and then loop over the pattern, taking each format character in turn into "datum_type". Then for each possible format character, we swallow up the other arguments in the pattern (a field width, an asterisk, and so on) and convert the next chunk input into the specified format, adding it onto the output SV "cat".
How do we know if the "U" is the first format in the "pat"? Well, if we have a pointer to the start of "pat" then, if we see a "U" we can test whether we're still at the start of the string. So, here's where "pat" is set up:
STRLEN fromlen; char *pat = SvPVx(*++MARK, fromlen); char *patend = pat + fromlen; I32 len; I32 datumtype; SV *fromstr;
We'll have another string pointer in there:
STRLEN fromlen; char *pat = SvPVx(*++MARK, fromlen); char *patend = pat + fromlen; + char *patcopy; I32 len; I32 datumtype; SV *fromstr;
And just before we start the loop, we'll set "patcopy" to be the start of "pat":
items = SP - MARK; MARK++; SvPVCLEAR(cat); + patcopy = pat; while (pat < patend) {
Now if we see a "U" which was at the start of the string, we turn on the "UTF8" flag for the output SV, "cat":
+ if (datumtype == 'U' && pat==patcopy+1) + SvUTF8_on(cat); if (datumtype == '#') { while (pat < patend && *pat != '\n') pat++;
Remember that it has to be "patcopy+1" because the first character of the string is the "U" which has been swallowed into "datumtype!"
Oops, we forgot one thing: what if there are spaces at the start of the pattern? "pack(" U*", @stuff)" will have "U" as the first active character, even though it's not the first thing in the pattern. In this case, we have to advance "patcopy" along with "pat" when we see spaces:
if (isSPACE(datumtype)) continue;
needs to become
if (isSPACE(datumtype)) { patcopy++; continue; }
OK. That's the C part done. Now we must do two additional things before this patch is ready to go: we've changed the behaviour of Perl, and so we must document that change. We must also provide some more regression tests to make sure our patch works and doesn't create a bug somewhere else along the line.
t/op/pack.t has a sensible ok() function, but if it didn't we could use the one from t/test.pl.
require './test.pl'; plan( tests => 159 );
so instead of this:
print 'not ' unless "1.20.300.4000" eq sprintf "%vd", pack("U*",1,20,300,4000); print "ok $test\n"; $test++;
we can write the more sensible (see Test::More for a full explanation of is() and other testing functions).
is( "1.20.300.4000", sprintf "%vd", pack("U*",1,20,300,4000), "U* produces Unicode" );
Now we'll test that we got that space-at-the-beginning business right:
is( "1.20.300.4000", sprintf "%vd", pack(" U*",1,20,300,4000), " with spaces at the beginning" );
And finally we'll test that we don't make Unicode strings if "U" is not the first active format:
isnt( v1.20.300.4000, sprintf "%vd", pack("C0U*",1,20,300,4000), "U* not first isn't Unicode" );
Mustn't forget to change the number of tests which appears at the top, or else the automated tester will get confused. This will either look like this:
print "1..156\n";
or this:
plan( tests => 156 );
We now compile up Perl, and run it through the test suite. Our new tests pass, hooray!
=item * If the pattern begins with a C<U>, the resulting string will be treated as UTF-8-encoded Unicode. You can force UTF-8 encoding on in a string with an initial C<U0>, and the bytes that follow will be interpreted as Unicode characters. If you don't want this to happen, you can begin your pattern with C<C0> (or anything else) to force Perl not to UTF-8 encode your string, and then follow this with a C<U*> somewhere in your pattern.
| 2019-10-21 | perl v5.30.3 |


Pascal
`Prev `__
P
`Next `__
--------------
**Pascal**: n.
An Algol-descended language designed by Niklaus Wirth on the CDC 6600
around 1967--68 as an instructional tool for elementary programming.
This language, designed primarily to keep students from shooting
themselves in the foot and thus extremely restrictive from a
general-purpose-programming point of view, was later promoted as a
general-purpose tool and, in fact, became the ancestor of a large family
of languages including Modula-2 and Ada (see also
`*bondage-and-discipline
language* <../B/bondage-and-discipline-language.html>`__). The hackish
point of view on Pascal was probably best summed up by a devastating
(and, in its deadpan way, screamingly funny) 1981 paper by Brian
Kernighan (of `*K&R* <../K/K-ampersand-R.html>`__ fame) entitled *Why
Pascal is Not My Favorite Programming Language*, which was turned down
by the technical journals but circulated widely via photocopies. It was
eventually published in *Comparing and Assessing Programming Languages*,
edited by Alan Feuer and Narain Gehani (Prentice-Hall, 1984). Part of
his discussion is worth repeating here, because its criticisms are still
apposite to Pascal itself after many years of improvement and could also
stand as an indictment of many other bondage-and-discipline languages.
(The entire essay is available at
`http://www.lysator.liu.se/c/bwk-on-pascal.html `__.)
At the end of a summary of the case against Pascal, Kernighan wrote:
9. There is no escape
This last point is perhaps the most important. The language is
inadequate but circumscribed, because there is no way to escape its
limitations. There are no casts to disable the type-checking when
necessary. There is no way to replace the defective run-time
environment with a sensible one, unless one controls the compiler
that defines the “standard procedures”. The language is closed.
People who use Pascal for serious programming fall into a fatal
trap. Because the language is impotent, it must be extended. But
each group extends Pascal in its own direction, to make it look like
whatever language they really want. Extensions for separate
compilation, FORTRAN-like COMMON, string data types, internal static
variables, initialization, octal numbers, bit operators, etc., all
add to the utility of the language for one group but destroy its
portability to others.
I feel that it is a mistake to use Pascal for anything much beyond
its original target. In its pure form, Pascal is a toy language,
suitable for teaching but not for real programming.
Pascal has since been entirely displaced (mainly by
`*C* <../C/C.html>`__) from the niches it had acquired in serious
applications and systems programming, and from its role as a teaching
language by Java.
--------------
+--------------------------+----------------------------+---------------------------------+
| `Prev `__ | `Up <../P.html>`__ | `Next `__ |
+--------------------------+----------------------------+---------------------------------+
| parse | `Home <../index.html>`__ | PascalCasing |
+--------------------------+----------------------------+---------------------------------+


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Hello and welcome to LeiosLabs, the hub for all projects lead by James Schloss (Leios)! I primarily work on computational science research while also trying to engage the public through youtube, twitch, twitter, or any other website I can find.
All ongoing and completed projects can be found on the sidebar. If you are interested in hiring me, please contact me through my e-mail at jrs.schloss@gmail.com.


THE GOOD SON A film review by James Berardinelli Copyright 1993 James Berardinelli
Rating (Linear 0 to 10): 4.0
Date Released: 9/24/93 Running Length: 1:27 Rated: R (Violence, language)
Starring: Macaulay Culkin, Elijah Wood, Wendy Crewson, David Morse, Daniel Hugh Kelly, Quinn Culkin Director: Joseph Ruben Producers: Mary Anne Page and Joseph Ruben Screenplay: Ian McEwan Music: Elmer Bernstein Released by Twentieth Century Fox
The death of his mother has been hard on Mark (Elijah Wood). In fact, as she lay on her deathbed, he promised her that he wouldn't let her die--a promise that he couldn't keep. Now, only a short time after the tragedy, Mark's father (David Morse) has to jet off to Tokyo to close a business deal, and Mark must spend his winter break with the family of his uncle Wallace (Daniel Hugh Kelly). While at first the prospect of two weeks in Maine is less than enticing, Mark loses much of his reticence once he meets the people he'll be staying with, taking an instant liking to his aunt Susan (Wendy Crewson), cousin Connie (Quinn Culkin), and especially cousin Henry (Macaulay Culkin). However, after an enjoyable first few days spent mostly in the company of Henry, Mark comes to learn that his cousin has a dark and violent streak that is becoming increasingly more twisted, and that it's only a matter of time before someone dies--if they haven't already.
The producers took a big chance in casting the youthful star of HOME ALONE as the demonic child in THE GOOD SON. Would Macaulay Culkin fill the bill? Could he fulfill the promise of his multi-million dollar contract and turn in a sinister performance as a truly evil twelve-year old masquerading as an angel? The answer, in a word, is no.
Casting actors against type is always a risky proposition. When it works, the dividends are often huge, but when it fails, the movie usually goes under. Such is the case with THE GOOD SON. Few are less likely to play a conscienceless, nasty-minded boy than the lively Culkin, and when he tries, he does a horrendous job.
Elijah Wood, who did a solid job in Disney's newest ADVENTURES OF HUCK FINN, is better than Culkin--but not by much. There's something not right about Mark. He comes across more like a written character than a real child, and Wood is unable to bridge the gap. A few of Mark's scenes ring true, but at least as many are overly-melodramatic and acted with a distinct lack of inspiration.
The plot itself is rather unimpressive, telling the story of a psychotic pre-teen. There are a number of fascinating psychological issues that could have been exploited, but were left untouched in favor of presenting a straightforward thriller. For example, we get precious little motivation for Henry's actions. There are a couple of throw-away lines that explain one incident, but say little about everything else.
The movie makes a few futile attempts to use Mark's guilt about his mother's death as a plot device, but it keeps getting in the way of the overall story. Part of the problem may be that the guilt and grief are never well-presented, and it's difficult to understand why Mark comes to believe that his aunt is somehow the reincarnation of his mother, whose dying words to him were that she would be with him forever. If anything, this element makes THE GOOD SON even less believable than it already is.
The ending of the movie is actually clever, which is a surprise given the routine conclusions to so many thrillers, but those few good minutes do not save the movie.
 Ruben, whose 1987 sleeper THE STEPFATHER was one of the decade's best thrillers, has, in THE GOOD SON, ignored everything that made that earlier film work: a tight script, excellent atmosphere, and solid characterization.
From the red-clay backdrops of Nevada to the frosty winterscapes of New England, THE GOOD SON is beautifully photographed (by cinematographer John Lindley), but that's one of its few positive points. Not many people enjoy going to the movies just to see pretty nature scenes, but that's just about the only reason for taking a trip to the theaters for THE GOOD SON. Those expecting a good scare would do better to look elsewhere. At best, this is strictly cable fare.
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


--- layout: post title: Separation of immiscible fluids in a cylinder thumb: cahnhilliard ---
Solution of the 3D Cahn-Hilliard equation (without convection) in a stationary cylinder, simulating the process of phase separation of immiscible fluids through diffusion under the influence of surface tension. The blue and yellow manifolds tightly enclose the regions of uniform mixing and can be thought of as the boundaries of the two phases, which are not otherwise visualized.


UKCloud Limited (“UKC”) and Virtual Infrastructure Group Limited (“VIG”) (together “the Companies”) – in Compulsory Liquidation
On 25 October 2022, the Companies were placed into Liquidation with the Official Receiver appointed as Liquidator and J Robinson and A M Hudson simultaneously appointed as Special Managers to manage the liquidation process on behalf of the Official Receiver.
Further information regarding the Liquidations can be found here: https://www.gov.uk/government/news/virtual-infrastructure-group-limited-and-ukcloud-limited-information-for-creditors-and-interested-parties
Contact details:
For any general queries relating to the Liquidations please email<EMAIL_ADDRESS>For customer related queries please email<EMAIL_ADDRESS>For supplier related queries please email<EMAIL_ADDRESS>How to scale an OpenShift v4 cluster
Overview
In OpenShift v4 clusters, worker nodes are managed by "MachineSets". These are objects that define the configuration of worker nodes, and can be scaled to set the number of workers of that type in the cluster.
Scaling an OpenShift v4 cluster
To view available MachineSets and their current scale, check the Compute ... MachineSets section of the OpenShift web console, or run the following oc command:
oc get MachineSets -n openshift-machine-api
To scale a MachineSet up or down, set the scale against the MachineSet in the OpenShift web console, or run:
oc scale MachineSet <machinesetname> -n openshift-machine-api --replicas=<scale>
Where <machinesetname>
is the name of the MachineSet to scale, and <scale>
is the number of workers desired from that MachineSet.
After approximately three minutes, the new nodes should be visible in the OpenShift web console or in the output of:
oc get nodes
Note
When you scale down a MachineSet, some workers will be drained and deleted. Workloads will automatically restart on remaining nodes if sufficient capacity remains. If all worker MachineSets are scaled to "0", workloads may stop completely.
Default MachineSets available
UKCloud offers three charging models for OpenShift workers:
Hourly - This is charged by the hour, with a one hour minimum charge.
Monthly - This is charged by the month, with a one month minimum charge. However, when used for the entire month, the cost is lower than running a hourly worker for the entire month.
Annual - This is charged by the year, with a one year minimum charge. However, when used for the entire year, the cost is lower than running a monthly worker for the entire year. If you'd like to add annual MachineSets to your cluster, raise a Service Request via the My Calls section of the UKCloud Portal and provide us with the size and network connectivity of the MachineSets you'd like to add.
Hourly MachineSets are particularly useful for short-term testing or for bursts of demand.
By default, UKCloud OpenShift clusters have the following MachineSets available:
<clusterid>-hourly-m1-medium
- 4 CPU, 16GB RAM, charged by the hour<clusterid>-monthly-m1-medium
- 4 CPU, 16GB RAM, charged by the month<clusterid>-hourly-r1-small
- 8 CPU, 16GB RAM, charged by the hour<clusterid>-monthly-r1-small
- 8 CPU, 16GB RAM, charged by the month<clusterid>-hourly-m1-large
- 4 CPU, 32B RAM, charged by the hour<clusterid>-monthly-m1-large
- 4 CPU, 32GB RAM, charged by the month<clusterid>-hourly-r1-medium
- 8 CPU, 32GB RAM, charged by the hour<clusterid>-monthly-r1-medium
- 8 CPU, 32GB RAM, charged by the month
For information about costs for each flavour and charging model, see section 2.1.3 of the UKCloud Pricing Guide.
Note
UKCloud can add MachineSets for larger node flavours. If this is something you require, raise a Service Request via the My Calls section of the UKCloud Portal and provide us with the size and network connectivity of the MachineSets you'd like to add.
Further information
For more information, see the Red Hat OpenShift Documentation: https://docs.openshift.com/container-platform/4.8/machine_management/manually-scaling-machineset.html
Feedback
If you find a problem with this article, click Improve this Doc to make the change yourself or raise an issue in GitHub. If you have an idea for how we could improve any of our services, send an email to feedback@ukcloud.com.


{% extends "base.html" %} {% block metatags %} {% endblock %} {% block breadcrumb_nav %}
Due to the extreme sizes of some of the zip archives (often well in excess of 4GB), we recommend using a dedicated zip archive application, such as 7-zip, to open and extract these datasets.
For example, the built-in zip archive handling in Microsoft Windows (ie Right-click to 'Extract Here') is not designed to handle these sizes and will throw errors, even suggesting (falsely) that the archive is corrupt.


Pure CSS3 check boxes, there is no javascript in this example and it should work in IE9 and above. There are many great examples of CSS3 checkboxes out there 1 and this is just another one.
To style the switch we are using :before
, :after
pseudo-elements and +
adjacent sibling selector. Then using the :checked
pseudo selector we can update the styles depending on the checkbox state.
input[type="checkbox"]:checked + label {
}
This example also uses data-attributes to provide labels (on/off) to the switches via css. In the html we are using data-switch-on="yes" data-switch-off="no"
and the rendering the content via css using content: attr(data-switch-on);
and content: attr(data-switch-off);
.
You can simply make the switch wider to allow for longer labels by just increasing the width in the css, in this example .toggle-switch-wide
contains the css for the wider version of the switch.
Check out the pen with more examples and the raw .less
See the Pen tHyGc by ben (@devben) on CodePen.
-
Nice flat design CSS3 toggle switchs - http://callmenick.com/2014/05/13/css-toggle-switch-examples/
Tutorial on basic principle from webdesign.tutsplus.com - http://webdesign.tutsplus.com/articles/quick-tip-easy-css3-checkboxes-and-radio-buttons–webdesign-8953 ↩


CoD4: Modern Warfare
A 5v5 team based game playing competetively to win the grand prize of cash money upto 20kGame-mode: SnD (Search and Destroy)Mod used: Promod (pml220)
Counter Strike: Global Offensive
A 5v5 team based game playing competetively to win the grand prize of cash money upto 20kGame-mode: Competetive
Dota 2
Mortal Combat X
Fifa 17
Blur


Prototype 1 for the drag and drop thing I want to make
Blah blah I'll make this look good with CSS later
This is the demo text. Replace me!
Update


This website is basically for the college students who are willing to participate in the event organized by Lalit Chandra Bharali College, Guwahati. Through this website, we encourage every student to participate and showcase their credibility during the event. Our team works together so that the participants get the best platform to enhance their skills and learn new topics about any subject fields.
More than 1K members are involved in our community to organize the events, take care of participants and provide full support. Mostly the members are from our college itself.
Last updated 3 mins ago
For more information, the participants can contact us via the email address provided below. As we are available for 24X7 to help the students or the participants.
Last updated 3 mins ago
We have organized more than 50+ events on our college campus and during the events more than 4K students from different institutes have participated and won maximum prizes. Every year the participants are increasing and we make your day good ahead.
Last updated 3 mins ago


--- layout: post title: Welcome to my new readers ---
Just wanted to say hi to the few people who subscribed to this blog from RSS Bandit, and from the few mac addicts that say hello through Safari. Gecko is also there. Hi everybody, if you come by London, leave me a message!


--- layout: default tab-title: Students title: Students ---
I am currently accepting DeFi PhD students (theoretical and applied) for my lab.
Please stay tuned for further details.


--- title: Prints Given Tensors Every N Local Steps, Every N Seconds, or at End type: docs repo: https://github.com/rstudio/tfestimators menu: main: name: Prints Given Tensors Every N Local Steps, Every N Seconds, or at End - tfestimators parent: tfestimators-reference aliases: - /reference/tfestimators/hook_logging_tensor.html - /tfestimators/reference/hook_logging_tensor.html - /guide/tfestimators/reference/hook_logging_tensor.html - /tools/tools/tfestimators/reference/hook_logging_tensor.html - /installation/tfestimators/reference/hook_logging_tensor.html - /tutorials/tfestimators/reference/hook_logging_tensor.html - /guide/tools/tfestimators/reference/hook_logging_tensor.html - /deploy/tfestimators/reference/hook_logging_tensor.html - /tools/tfestimators/reference/hook_logging_tensor.html - /tutorials/tools/tfestimators/reference/hook_logging_tensor.html ---
The tensors will be printed to the log, with INFO
severity.
hook_logging_tensor(tensors, every_n_iter = NULL, every_n_secs = NULL,
formatter = NULL, at_end = FALSE)
| tensors | A list that maps string-valued tags to tensors/tensor names. |
| every_n_iter | An integer value, indicating the values of |
| every_n_secs | An integer or float value, indicating the values of |
| formatter | A function that takes |
| at_end | A boolean value specifying whether to print the values of |
Note that if at_end
is TRUE
, tensors
should not include any tensor
whose evaluation produces a side effect such as consuming additional inputs.
Other session_run_hook wrappers: hook_checkpoint_saver
,
hook_global_step_waiter
,
hook_history_saver
,
hook_nan_tensor
,
hook_progress_bar
,
hook_step_counter
,
hook_stop_at_step
,
hook_summary_saver
,
session_run_hook


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Agenda:
What is Node?
Brief History
Pros/Cons
A few resources
Agenda:
What is Node?
Brief History
Pros/Cons
A few resources
Agenda:
What is Node?
Brief History
Pros/Cons
A few resources
Agenda:
What is Node?
Brief History
Pros/Cons
A few resources
Just what is Node?
What isn't Node:
- a language
- a platform or service
- a framework
Ok.. what is Node?
Node is...
- a Javascript environment
- Event-driven/Non-blocking
- Pure Awesome
Origin
- Created by Ryan Dahl
- "Scalable network programs"
- Maintained by Isaac Schlueter
Pros
- Javascript
- Non-blocking
- Reuse JS code
- Strong community and modules!


To enable us to display the progress of a long-running process, the process can be run in the background, which is what the backgroundProcess tag allows us to do.
Running background processes will be listed in the database table O2_BACKGROUND_PROCESS. The background process is responsible for updating the counter field of this
table, which is done by continuously calling setCounter on an instance of O2::Script::Detached, f ex:
my $detached = $context->getSingleton('O2::Script::Detached');
my $pid = $detached->getPid();
my $i = 0;
while (...) {
$i++;
$detached->setCounter($pid, $i) if $pid;
...
}
To know when the counter has reached 100%, the "max" attribute should be set on the "progressBar" tag.
id
exclusive [0] - Can we have only one instance of this process running at the same time?
command - raw command that will run in the background.
url - may be used instead of the "command" attribute; the created command will fetch the given URL with wget.
outputDocument - if given, the generated output will be sent here.
urlMod parameters can be used instead of the url attribute.
max - when the counter gets to this number, the process is done, and the progress bar is at 100%.
width - the width of the progress bar. If the designation is missing, it is assumed that the width is given in pixels.
checkIntervalSeconds - number of seconds between each ajax call to check the progress.
estimateProgressBetweenChecks [0] - if the progress bar should move smoothly between each progress check ajax call.
checkTimeoutSeconds - the number of seconds we wait for the ajax call (that checks progress) to return. After this number of seconds, the ajax call is aborted.
text - button text.
onStart - Javascript code to be executed when the button is clicked.
onEnd - Javascript code to be executed when the progress bar reaches 100%.


'3BlackChicks Review...'
WEST SIDE STORY (1961) Not Rated; running time 151 minutes Genre: Musical IMDB site: http://us.imdb.com/Details?0055614 Written by: Ernest Lehman (based on the play by Arthur Laurents and "Romeo and Juliet" by Shakespeare) Directed by: Robert Wise, Jerome Robbins Music by: Leonard Bernstein, Stephen Sondheim Choreography by: Jerome Robbins Cast: Natalie Wood, Richard Beymer, Russ Tamblyn, Rita Moreno, George Chakiris, Tucker Smith, Tony Mordente, David Winters, Eliot Feld, Susan Oakes, Simon Oakland, Jose De Vega, Tony Mordente, Ned Glass DVD standard features (region 1): widescreen; theatrical trailer; dual layer; Dolby Digital 5.1 soundtrack; 8-page booklet; scene access. Languages & Subtitles: English, French
Review Copyright Rose Cooper, 2001 Review URL: http://www.3blackchicks.com/bamswestside.html
It's no secret that I love musicals. But that love has been tempered by the fact that musicals, by their very nature, were basically idealized flights of fancy; worlds in which Beautiful People would break out in song and dance at the drop of a hat. Not that there was anything wrong with that, necessarily; in fact, I think my love for musicals developed because I needed to escape to the fantasy life they portrayed.
That is, until I watched WEST SIDE STORY, and saw, for the first time, a musical that addressed the realities of life in the ghetto. Uh, to the extent that dancing and singing 'bangers can be said to be real.
The Story (WARNING: **spoilers contained below**): The place: New York. The time: the 1950s. Whites hung out on one side of the 'hood, Puerto Ricans, on the other; and never the twain should meet. But someone forgot to tell that to Tony (Richard Beymer) and Maria (Natalie Wood).
Tony, a good White boy, and Maria, a good Puerto Rican girl, defy tradition by falling in love, much to the chagrin of their families, their friends - and their respective homies in the rival White Jets and Puerto Rican Sharks gangs. The Jets, led by Riff (Russ Tamblyn), and the Sharks, led by Maria's brother Bernardo (George Chakiris), try to talk "sense" to Tony and Maria, to no avail. All hell breaks loose when Bernardo's girlfriend Anita (Rita Moreno, as good as it gets) tries to deliver a message from Maria to Tony via the hard-headed and hard-hearted Sharks; and dire consequences follow.
The Upshot: Forget that the late Natalie Wood was no more Puerto Rican than I am, that she wouldn't know a Puerto Rican accent if it bit her on the butt, and that Wood, Rita Moreno, and Richard Beymer actually had singing doubles (Marni Nixon, Betty Wand, and Jimmy Bryant, respectively). Forget that of all the males cast for WSS, Richard Beymer seemed the most out-of-place [who might've been better as Tony, at the time? Dunno...maybe John Derek, the cutie from THE TEN COMMANDMENTS?]; if anyone had the potential to drag WEST SIDE STORY down a notch or two, Beymer would've certainly been its weakest link. And forget that one doesn't usually perceive singing and dancing gangbangers as a menace [not in these days of drive-by shooting, anyway. I prefer the cop rock-types myself...but I digress].
Forget about all that, because it simply does not matter. The strength of the movie WEST SIDE STORY lies in its earnestness and in the way it conveyed the immediacy of its tale without compromising on its obvious roots as a stage play. In spite of their shortcomings, I believed in Wood's Maria, and in Beymer's Tony. And because of the powerful performances of the supporting cast (chief among them the firey Moreno, George Chakiris and Russ Tamblin as fated gangleaders, Simon Oakland as the soul-dead Lieutenant Schrank, and my favorite Chick of 'em all, Susan Oakes as the ruffneck Anybodys), WEST SIDE STORY will always be much more than Just Another Musical for me.
Not to discount the musical portion of the program; not in the least. Even without its Message, WSS could still stand on its musical merits alone. Composers Leonard Bernstein and Stephen Sondheim whipped up a masterful score; choreographer Jerome Robbins created memorable dance sequences (who could ever forget "America"? or "Cool"? or...); and though Ernest Lehman's script seems a bit dated by today's standards, director Robert Wise beautifully put it all together in a film that truly deserves to be hailed by a term that's so often abused these days: a classic.
And did I mention it had singing and dancing 'bangers?
The Numbers: To make a long review even longer...
"Overture" and "Jet Ballet" WEST SIDE STORY kicks off with five minutes of little on-screen to entertain the viewer except an unidentifiable (at first) illustration that changes colors; so just close your eyes and enjoy the swelling score until the illustration segues to an overhead view of Manhattan and leads to..."Jet Ballet", which begins with WSS's trademark signaling whistle, and what appears at first to be a bunch of Misunderstood Yooth havin' fun - at first. I can't think of better way to get the feel for the rest of the movie, than these first few minutes.
"Jet Song" and "Something's Coming" It's clear: Tony's trying to climb out of the barrel - and the Jets are the crabs.
"The Dance At The Gym" and "Promenade/Mambo" Everytime I watch the Jiggaboo/Wannabe dance scene in Spike Lee's SCHOOL DAZE, I see Lee's influence in WSS's "Mambo" (though WSS of course has SD beat by a country mile). And besides the amazing dancing, the Gym scene is notable for a grin-invoking bit with John Astin as Glad Hand.
"Maria" I prefer the "Tony and Maria are hit with a bolt of lightning" instrumental/dancing version of "Maria" to the version that Tony sings.
"America" By far my favorite number in a movie full of great song/dance numbers. An interesting contrast of old country/new country viewpoints, there is so much depth included here, both stylistically (Rita Moreno and George Chakiris burned the roof off) and in substance (the lyric "Life is alright in America/If you're all White in America!"...man oh man).
"Officer Krupke" The Problem With Juvenile Delinquents, in a nutshell. All they'd need to add for 90s/00s sensibilities are references to video games and violent movies.
"One Hand, One Heart" If I had been thinking, I would've looked for a recording of this song when I got married.
"The Rumble/Tonight" Great intermix of sweetness (Tony & Maria), sexiness (Anita) and tragedy (the gangs).
"Somewhere" Forget Streisand; this song has never been done better than in WSS. Here, it conveys a poignant mix of despair and desire.
"Cool" Ice (Tucker Smith) was sho'nuff named right; he and Action (Tony Mordente) make this number zoom.
Don't tell anybody, but the time the (very cleverly done) closing credits roll around, I'm always a soggy mess.
DVD Thingies: Nothing outstanding here, as far as DVD-specific features go. I am happy, though, for the updated soundtrack (for which I turned the knob up to 11) and for the 8-page booklet; these alone make purchasing this DVD a steal at any cost.
Bammer's Bottom Line: I could note, I reckon, that WEST SIDE STORY tore up the Academy Awards in 1961, with 10 total Oscars including Best Picture, Best Supporting actor (George Chakiris), and Best Supporting actress (Rita Moreno). But it hardly matters. Y'all already know that WSS was tha bomb, right? If ya don't know, now ya know.
WEST SIDE STORY: (rating: greenlight): They just don't make classics like this anymore. More's the pity.
Rose "Bams" Cooper Webchick and Editor, 3BlackChicks Review Entertainment Reviews With Flava! Copyright Rose Cooper, 2001 EMAIL<EMAIL_ADDRESS>http://www.3blackchicks.com/
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