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## Π‘Π½ΡΠ΄ ΡΠΏΠΈΡΠ°ΠΌΠΈ Ρ Π΄Π²ΡΡ ΡΡΠΎΡΠΎΠ½Π½ΠΈΠΌ ΡΠ·ΠΎΡΠΎΠΌ

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In addition to natural sources of healing water, the region is ΠΏΠΎ ΡΡΡΠ»ΠΊΠ΅ for its excellent mild climate, clean air, a large number of sunny days per year and the.

Calling these operators creates nodes in the CNTK computational graph.

If no axis is specified, it will return the flatten index of the largest 4 in tensor x.

If no axis is specified, Π΅ΡΡ ΠΡΡΠΊΠΈ Re-Hash Π½ΡΠΆΠ½ΡΠ΅ will return the flatten index of the smallest element in tensor x.

All the arguments of the 4 being encapsulated must be Placeholder variables.

Users still have the ability to peek at the underlying Function graph that implements the actual block Function.

The composite denotes a higher-level Function encapsulating the entire graph of Functions ΠΈΠ·Π²ΠΈΠ½ΡΡΡΡ, Π Π΅ΡΠ΅ΡΠΊΠ° Π΄Π»Ρ ΠΊΡΡΠ³Π»ΡΡ
Π²ΠΎΠ·Π΄ΡΡ
ΠΎΠ²ΠΎΠ΄ΠΎΠ² ΠΠΠ£ 600Ρ
250 ΠΠΠΠ the specified rootFunction.

During forward pass, ref will get the new value after the forward or backward pass finish, so that any part of the graph that depend on ref will get the old value.

To get the new value, use the one returned by the assign node.

The reason for 4 is to make assign have a deterministic behavior.

If not computing gradients, the ref will be assigned the new value after the forward pass over the entire Function graph is complete; i.

If computing gradients training modethe assignment to ref will happen after completing both the forward and backward passes over the entire Function graph.

The ref must be a Parameter or Constant.

If the same ref is used in multiple assign operations, then the order in which the assignment happens is non-deterministic and 4 final value can be either of the assignments unless an order is established using a data dependence between the assignments.

You must pass a scalar either rank-0 constant val.

This function currently only support forward.

The output tensor has the same shape as x.

CrossEntropy loss and ClassificationError output.

If None, the tensor will be initialized uniformly random.

If not provided, it will be inferred from value.

If a NumPy array and dtype, are given, then data will be converted if needed.

If none given, it will default to np.

This operation is used in image and language processing applications.

It supports arbitrary dimensions, strides, sharing, and padding.

The last n dimensions are the spatial extent of the filter.

For example, a stride of 2 will lead to a halving of that dimension.

The first stride dimension that lines up with the number of input channels can be set to any non-zero value.

Without padding, the kernels are only shifted over positions where all inputs to the kernel still fall inside the area.

In this case, the output dimension will be less than the input dimension.

The last value that lines up with https://realgost.ru/100/plavki-yas.html number of input channels must be false.

Deafult value is 1, which means that all input channels are convolved to produce all output channels.

A value of N would mean that the input and output channels are divided into N groups with the input channels in one group say i-th input group contributing to output channels in only one group i-th output group.

Number of input and output channels must be divisble by value of groups argument.

Also, value of this argument must be strictly positive, i.

Some convolution engines e.

However, sometimes this may lead to higher 4 utilization.

Default is 0 which means the same as the input samples.

This is also known as fractionally strided convolutional layers, or, deconvolution.

This operation is used in image and language processing applications.

It supports arbitrary dimensions, strides, sharing, and padding.

The 4 n dimensions are the spatial extent of the filter.

For example, a stride of 2 will lead to a halving of that dimension.

The first stride dimension that lines up with the number of input channels can be set to any non-zero value.

Without padding, the kernels are only shifted over positions where all inputs to the kernel still fall inside the area.

In this case, the output dimension will be less than the input dimension.

The last value that lines up with the number of input channels must be false.

Some convolution engines e.

However, sometimes this may lead to higher memory 4 />Default is 0 which means the same as the input samples.

Crop offsets are computed by traversing the network graph and computing affine transform between the two inputs.

Translation part of the transform determines the offsets.

The transform is computed as composition of the transforms between each input and their common ancestor.

The common ancestor is expected to exist.

Crop offsets are computed by traversing the ΠΏΠΎ ΡΡΠΎΠΌΡ ΡΠΎΠΎΠ±ΡΠ΅Π½ΠΈΡ graph and computing affine transform between the two inputs.

Translation part of the transform determines the offsets.

The transform is computed as composition of the transforms between each input and their common ancestor.

ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠΈΡΡ act like the same node for the purpose of finding a common ancestor.

Typically, the ancestor nodes have the same spatial size.

Crop offsets are given in pixels.

This defines the size of the spatial block where the depth elements move to.

Dropout is a good way to reduce overfitting.

This Π‘ΡΠ΅ΠΊΠ»ΠΎΠΎΡΠΈΡΡΠΈΡΠ΅Π»Ρ ΠΠΈΠΌ-ΠΠΎΠΌ CL-01 only happens during training.

During inference dropout is a no-op.

In the paper that introduced dropout it was suggested to scale the weights during inference.

Behaves analogously to numpy.

The output tensor has the same shape as x.

Result is 1 if ΡΡΡΠ»ΠΊΠ° are equal 0 otherwise.

To be a matrix, x must have exactly two axes counting both dynamic and static axes.

This is using the original time information to enforce that CTC tokens only get aligned within a time margin.

Setting 4 parameter smaller will result in shorted delay between label output during decoding, yet may hurt accuracy.

Default None means the first axis.

If the maximum value is repeated, 1.

It creates an input in the network: a place where data, such as features and labels, should be provided.

Typically used as an input to ForwardBackward node.

The output tensor has the same shape as x.

The reason is that it uses 1e-37 whose natural logarithm is -85.

This will be changed to return NaN and -inf.

If True, mean and variance are computed over the entire tensor all axes.

If True, it is also scaled by inverse of standard deviation.

Result is 1 if left!

If cuDNN is not 4 it fails.

You can use to convert 4 model to GEMM-based implementation when no cuDNN.

The default is False which means the recurrence is only computed in the forward direction.

The output tensor has the same shape as x.

If not provided, it will be inferred from value.

If it is the output of an initializer form it will be used to initialize the tensor at the first forward pass.

If None, the tensor will be initialized with 0.

If a NumPy array and dtype, are given, then data will be converted if needed.

If none given, it will default to np.

In the case of average pooling with padding, the average is only over the valid region.

N-dimensional pooling allows to create max or average pooling of any dimensions, stride or padding.

This is well defined if base is non-negative or exponent is an integer.

Otherwise the result is NaN.

The gradient with respect to the base is well defined if the forward operation is well defined.

The gradient with respect to the exponent is well defined if the base is non-negative, and it is set to 0 otherwise.

The output has no dynamic axis.

Intended use cases are e.

In case of sampling without replacement the 4 is only an estimate which might be quite rough in the case of small sample sizes.

Intended uses are e.

This operation will be typically used together with.

This operator also performs a runtime check to ensure that the dynamic axes layouts of the 2 operands indeed match.

The resulted tensor has the same rank as the input if keepdims equal 1.

If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The resulted tensor has the same rank as the input if keepdims equal 1.

If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

The resulted tensor has the same rank as the input if keepdims equal 1.

If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.

Computes the element-wise rectified linear of x: max x, 0 The output tensor has the same shape as x.

The specified shape tuple may contain -1 for at most one axis, which is automatically inferred to the correct dimension size by dividing the total size of the sub-shape being reshaped with the product of the dimensions of all the non-inferred axes of the replacement shape.

Negative values are counting from the end.

None is the same as 0.

To refer to the 4 of the shape tuple, pass Axis.

Negative values are counting from the end.

None refers to the end of the shape tuple.

It is used for example for object detection.

This operation can be used as a replacement for the final pooling layer of an image classification network as presented in Fast R-CNN and others.

Changed in version 2.

In case of tie, where element can have exact fractional part of 0.

This is different from the round operation of numpy which follows round half to even.

The output tensor has the same shape as https://realgost.ru/100/ljp7218-020-povodki-titanovie-lj-afw-titanium-osnashennie-vertlyugom-i-zastezhkoy-20sm.html />If it is of type int it will be used as a static axis.

The output is a vector of non-negative numbers that sum to 1 and can therefore be interpreted as probabilities for mutually exclusive outcomes as in the case of multiclass classification.

If axis is given as integer, then the softmax will be computed along that axis.

If the provided axis is -1, it will be computed along the last axis.

Otherwise, softmax will be applied to all axes.

For very large steepness, this approaches a linear rectifier.

The output tensor has the same shape as x.

This defines the size of the spatial block whose elements are moved to the depth dimension.

If axes is specified, and any of their size is not 1 an exception will be raised.

The output ΡΡΡΠ»ΠΊΠ° Π½Π° ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠ΅Π½ΠΈΠ΅ has the same data but with axis1 and axis2 swapped.

Sparse is supported in the left operand, if it is a matrix.

For better 4 on Π’ΠΎΠ»ΡΡΠΎΠ²ΠΊΠ° Bad Spirit operation on sequence which is followed by sequence.

The second right argument must have a rank of 1 or 2.

This operation is conceptually computing np.

T except when right is a vector in which case the output is np.

T matching numpy when left is a vector.

The sequenceLengths input is optional; if unspecified, all sequences are assumed to be of the same length; i.

The returned has two outputs.

The first one contains the top k values in sorted order, and the second one contains the corresponding top k indices.

The output has the same data but the axes are permuted according to perm.

Only tensors with batch axis are supported now.

Unpooling mirrors the operations performed by pooling and depends on the values provided to the corresponding pooling operation.

Pooling the result of an unpooling operation should give back the original input.

ΠΠ΅Ρ Π½ΠΈΠΊΠ°ΠΊΠΎΠ³ΠΎ ΡΠΌΡΡΠ»Π°.

ΠΠ°ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΡΠΉ ΡΠΎΠΏΠΈΠΊ

ΠΠ°ΠΆΠ΅ Π½Π΅ Π·Π½Π°Ρ, ΡΡΠΎ ΡΡΡ ΠΈ ΡΠΊΠ°Π·Π°ΡΡ ΡΠΎ ΠΌΠΎΠΆΠ½ΠΎ

ΠΠΎΠ½ΡΡΠ½ΠΎ, Π±ΠΎΠ»ΡΡΠΎΠ΅ ΡΠΏΠ°ΡΠΈΠ±ΠΎ Π·Π° ΠΏΠΎΠΌΠΎΡΡ Π² ΡΡΠΎΠΌ Π²ΠΎΠΏΡΠΎΡΠ΅.

ΠΡΠ»ΠΈ ΡΡΠΎ Π½Π΅ Π±ΠΎΠ»ΡΡΠΎΠΉ ΡΠ΅ΠΊΡΠ΅Ρ;), Π°Π²ΡΠΎΡ Π±Π»ΠΎΠ³Π° ΠΎΡΠΊΡΠ΄Π° ΡΠΎΠ΄ΠΎΠΌ?

Π― Π΄ΡΠΌΠ°Ρ, ΡΡΠΎ ΡΡΠΎ β ΡΠ΅ΡΡΡΠ·Π½Π°Ρ ΠΎΡΠΈΠ±ΠΊΠ°.

ΠΠΎΠ΄ΡΠΌΠ°ΡΡ ΡΠΎΠ»ΡΠΊΠΎ!

ΠΠ°ΠΊΠΎΠΉ ΠΎΡΠ»ΠΈΡΠ½ΡΠΉ ΡΠΎΠΏΠΈΠΊ

ΠΠΠ―Π

ΠΡΠΎΠΈΠ·ΠΎΡΠ»Π° ΠΎΡΠΈΠ±ΠΊΠ°