Sparse Symbol API¶
Overview¶
This document lists the routines of the sparse symbolic expression package:
mxnet.symbol.sparse |
Sparse Symbol API of MXNet. |
The Sparse Symbol
API, defined in the symbol.sparse
package, provides
sparse neural network graphs and auto-differentiation.
The storage type of a variable is speficied by the stype
attribute of the variable.
The storage type of a symbolic expression is inferred based on the storage types of the variables and the operators.
>>> a = mx.sym.Variable('a', stype='csr')
>>> b = mx.sym.Variable('b')
>>> c = mx.sym.dot(a, b, transpose_a=True)
>>> type(c)
>>> e = c.bind(mx.cpu(), {'a': mx.nd.array([[1,0,0]]).tostype('csr'), 'b':mx.nd.ones((1,2))})
>>> y = e.forward()
# the result storage type of dot(csr.T, dense) is inferred to be `row_sparse`
>>> y
[]
>>> y[0].asnumpy()
array([ 1., 1.],
[ 0., 0.],
[ 0., 0.]], dtype=float32)
Note
most operators provided in mxnet.symbol.sparse
are similar to those in
mxnet.symbol
although there are few differences:
- Only a subset of operators in
mxnet.symbol
have efficient sparse implementations inmxnet.symbol.sparse
. - If an operator do not occur in the
mxnet.symbol.sparse
namespace, that means the operator does not have an efficient sparse implementation yet. If sparse inputs are passed to such an operator, it will convert inputs to the dense format and fallback to the already available dense implementation. - The storage types (
stype
) of sparse operators’ outputs depend on the storage types of inputs. By default the operators not available inmxnet.symbol.sparse
infer “default” (dense) storage type for outputs. Please refer to the API reference section for further details on specific operators.
In the rest of this document, we list sparse related routines provided by the
symbol.sparse
package.
Symbol creation routines¶
zeros_like |
Return an array of zeros with the same shape, type and storage type as the input array. |
mxnet.symbol.var |
Creates a symbolic variable with specified name. |
Symbol manipulation routines¶
Changing symbol storage type¶
cast_storage |
Casts tensor storage type to the new type. |
Mathematical functions¶
Arithmetic operations¶
elemwise_add |
Adds arguments element-wise. |
elemwise_sub |
Subtracts arguments element-wise. |
elemwise_mul |
Multiplies arguments element-wise. |
broadcast_add |
Returns element-wise sum of the input arrays with broadcasting. |
broadcast_sub |
Returns element-wise difference of the input arrays with broadcasting. |
broadcast_mul |
Returns element-wise product of the input arrays with broadcasting. |
broadcast_div |
Returns element-wise division of the input arrays with broadcasting. |
negative |
Numerical negative of the argument, element-wise. |
dot |
Dot product of two arrays. |
add_n |
Adds all input arguments element-wise. |
Trigonometric functions¶
sin |
Computes the element-wise sine of the input array. |
tan |
Computes the element-wise tangent of the input array. |
arcsin |
Returns element-wise inverse sine of the input array. |
arctan |
Returns element-wise inverse tangent of the input array. |
degrees |
Converts each element of the input array from radians to degrees. |
radians |
Converts each element of the input array from degrees to radians. |
Hyperbolic functions¶
sinh |
Returns the hyperbolic sine of the input array, computed element-wise. |
tanh |
Returns the hyperbolic tangent of the input array, computed element-wise. |
arcsinh |
Returns the element-wise inverse hyperbolic sine of the input array, computed element-wise. |
arctanh |
Returns the element-wise inverse hyperbolic tangent of the input array, computed element-wise. |
Reduce functions¶
sum |
Computes the sum of array elements over given axes. |
mean |
Computes the mean of array elements over given axes. |
Rounding¶
round |
Returns element-wise rounded value to the nearest integer of the input. |
rint |
Returns element-wise rounded value to the nearest integer of the input. |
fix |
Returns element-wise rounded value to the nearest integer towards zero of the input. |
floor |
Returns element-wise floor of the input. |
ceil |
Returns element-wise ceiling of the input. |
trunc |
Return the element-wise truncated value of the input. |
Exponents and logarithms¶
expm1 |
Returns exp(x) - 1 computed element-wise on the input. |
log1p |
Returns element-wise log(1 + x) value of the input. |
Neural network¶
More¶
make_loss |
Make your own loss function in network construction. |
stop_gradient |
Stops gradient computation. |
Embedding |
Maps integer indices to vector representations (embeddings). |
LinearRegressionOutput |
Computes and optimizes for squared loss during backward propagation. |
LogisticRegressionOutput |
Applies a logistic function to the input. |
API Reference¶
Sparse Symbol API of MXNet.
-
mxnet.symbol.sparse.
ElementWiseSum
(*args, **kwargs)¶ Adds all input arguments element-wise.
\[add\_n(a_1, a_2, ..., a_n) = a_1 + a_2 + ... + a_n\]add_n
is potentially more efficient than callingadd
by n times.The storage type of
add_n
output depends on storage types of inputs- add_n(row_sparse, row_sparse, ..) = row_sparse
- add_n(default, csr, default) = default
- add_n(any input combinations longer than 4 (>4) with at least one default type) = default
- otherwise,
add_n
falls all inputs back to default storage and generates default storage
Defined in src/operator/tensor/elemwise_sum.cc:L156 This function support variable length of positional input.
Parameters: - args (Symbol[]) – Positional input arguments
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
Embedding
(data=None, weight=None, input_dim=_Null, output_dim=_Null, dtype=_Null, sparse_grad=_Null, name=None, attr=None, out=None, **kwargs)¶ Maps integer indices to vector representations (embeddings).
This operator maps words to real-valued vectors in a high-dimensional space, called word embeddings. These embeddings can capture semantic and syntactic properties of the words. For example, it has been noted that in the learned embedding spaces, similar words tend to be close to each other and dissimilar words far apart.
For an input array of shape (d1, ..., dK), the shape of an output array is (d1, ..., dK, output_dim). All the input values should be integers in the range [0, input_dim).
If the input_dim is ip0 and output_dim is op0, then shape of the embedding weight matrix must be (ip0, op0).
By default, if any index mentioned is too large, it is replaced by the index that addresses the last vector in an embedding matrix.
Examples:
input_dim = 4 output_dim = 5 // Each row in weight matrix y represents a word. So, y = (w0,w1,w2,w3) y = [[ 0., 1., 2., 3., 4.], [ 5., 6., 7., 8., 9.], [ 10., 11., 12., 13., 14.], [ 15., 16., 17., 18., 19.]] // Input array x represents n-grams(2-gram). So, x = [(w1,w3), (w0,w2)] x = [[ 1., 3.], [ 0., 2.]] // Mapped input x to its vector representation y. Embedding(x, y, 4, 5) = [[[ 5., 6., 7., 8., 9.], [ 15., 16., 17., 18., 19.]], [[ 0., 1., 2., 3., 4.], [ 10., 11., 12., 13., 14.]]]
The storage type of weight can be either row_sparse or default.
Note
If “sparse_grad” is set to True, the storage type of gradient w.r.t weights will be “row_sparse”. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad and Adam. Note that by default lazy updates is turned on, which may perform differently from standard updates. For more details, please check the Optimization API at: /api/python/optimization/optimization.html
Defined in src/operator/tensor/indexing_op.cc:L267
Parameters: - data (Symbol) – The input array to the embedding operator.
- weight (Symbol) – The embedding weight matrix.
- input_dim (int, required) – Vocabulary size of the input indices.
- output_dim (int, required) – Dimension of the embedding vectors.
- dtype ({'float16', 'float32', 'float64', 'int32', 'int64', 'int8', 'uint8'},optional, default='float32') – Data type of weight.
- sparse_grad (boolean, optional, default=0) – Compute row sparse gradient in the backward calculation. If set to True, the grad’s storage type is row_sparse.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
FullyConnected
(data=None, weight=None, bias=None, num_hidden=_Null, no_bias=_Null, flatten=_Null, name=None, attr=None, out=None, **kwargs)¶ Applies a linear transformation: \(Y = XW^T + b\).
If
flatten
is set to be true, then the shapes are:- data: (batch_size, x1, x2, ..., xn)
- weight: (num_hidden, x1 * x2 * ... * xn)
- bias: (num_hidden,)
- out: (batch_size, num_hidden)
If
flatten
is set to be false, then the shapes are:- data: (x1, x2, ..., xn, input_dim)
- weight: (num_hidden, input_dim)
- bias: (num_hidden,)
- out: (x1, x2, ..., xn, num_hidden)
The learnable parameters include both
weight
andbias
.If
no_bias
is set to be true, then thebias
term is ignored.Note
The sparse support for FullyConnected is limited to forward evaluation with row_sparse weight and bias, where the length of weight.indices and bias.indices must be equal to num_hidden. This could be useful for model inference with row_sparse weights trained with importance sampling or noise contrastive estimation.
To compute linear transformation with ‘csr’ sparse data, sparse.dot is recommended instead of sparse.FullyConnected.
Defined in src/operator/nn/fully_connected.cc:L272
Parameters: - data (Symbol) – Input data.
- weight (Symbol) – Weight matrix.
- bias (Symbol) – Bias parameter.
- num_hidden (int, required) – Number of hidden nodes of the output.
- no_bias (boolean, optional, default=0) – Whether to disable bias parameter.
- flatten (boolean, optional, default=1) – Whether to collapse all but the first axis of the input data tensor.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
LinearRegressionOutput
(data=None, label=None, grad_scale=_Null, name=None, attr=None, out=None, **kwargs)¶ Computes and optimizes for squared loss during backward propagation. Just outputs
data
during forward propagation.If \(\hat{y}_i\) is the predicted value of the i-th sample, and \(y_i\) is the corresponding target value, then the squared loss estimated over \(n\) samples is defined as
\(\text{SquaredLoss}(\textbf{Y}, \hat{\textbf{Y}} ) = \frac{1}{n} \sum_{i=0}^{n-1} \lVert \textbf{y}_i - \hat{\textbf{y}}_i \rVert_2\)
Note
Use the LinearRegressionOutput as the final output layer of a net.
The storage type of
label
can bedefault
orcsr
- LinearRegressionOutput(default, default) = default
- LinearRegressionOutput(default, csr) = default
By default, gradients of this loss function are scaled by factor 1/m, where m is the number of regression outputs of a training example. The parameter grad_scale can be used to change this scale to grad_scale/m.
Defined in src/operator/regression_output.cc:L92
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
LogisticRegressionOutput
(data=None, label=None, grad_scale=_Null, name=None, attr=None, out=None, **kwargs)¶ Applies a logistic function to the input.
The logistic function, also known as the sigmoid function, is computed as \(\frac{1}{1+exp(-\textbf{x})}\).
Commonly, the sigmoid is used to squash the real-valued output of a linear model \(wTx+b\) into the [0,1] range so that it can be interpreted as a probability. It is suitable for binary classification or probability prediction tasks.
Note
Use the LogisticRegressionOutput as the final output layer of a net.
The storage type of
label
can bedefault
orcsr
- LogisticRegressionOutput(default, default) = default
- LogisticRegressionOutput(default, csr) = default
The loss function used is the Binary Cross Entropy Loss:
\(-{(y\log(p) + (1 - y)\log(1 - p))}\)
Where y is the ground truth probability of positive outcome for a given example, and p the probability predicted by the model. By default, gradients of this loss function are scaled by factor 1/m, where m is the number of regression outputs of a training example. The parameter grad_scale can be used to change this scale to grad_scale/m.
Defined in src/operator/regression_output.cc:L152
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
MAERegressionOutput
(data=None, label=None, grad_scale=_Null, name=None, attr=None, out=None, **kwargs)¶ Computes mean absolute error of the input.
MAE is a risk metric corresponding to the expected value of the absolute error.
If \(\hat{y}_i\) is the predicted value of the i-th sample, and \(y_i\) is the corresponding target value, then the mean absolute error (MAE) estimated over \(n\) samples is defined as
\(\text{MAE}(\textbf{Y}, \hat{\textbf{Y}} ) = \frac{1}{n} \sum_{i=0}^{n-1} \lVert \textbf{y}_i - \hat{\textbf{y}}_i \rVert_1\)
Note
Use the MAERegressionOutput as the final output layer of a net.
The storage type of
label
can bedefault
orcsr
- MAERegressionOutput(default, default) = default
- MAERegressionOutput(default, csr) = default
By default, gradients of this loss function are scaled by factor 1/m, where m is the number of regression outputs of a training example. The parameter grad_scale can be used to change this scale to grad_scale/m.
Defined in src/operator/regression_output.cc:L120
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
abs
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise absolute value of the input.
Example:
abs([-2, 0, 3]) = [2, 0, 3]
The storage type of
abs
output depends upon the input storage type:- abs(default) = default
- abs(row_sparse) = row_sparse
- abs(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L668
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
adagrad_update
(weight=None, grad=None, history=None, lr=_Null, epsilon=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, name=None, attr=None, out=None, **kwargs)¶ Update function for AdaGrad optimizer.
Referenced from Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, and available at http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
Updates are applied by:
rescaled_grad = clip(grad * rescale_grad, clip_gradient) history = history + square(rescaled_grad) w = w - learning_rate * rescaled_grad / sqrt(history + epsilon)
Note that non-zero values for the weight decay option are not supported.
Defined in src/operator/optimizer_op.cc:L665
Parameters: - weight (Symbol) – Weight
- grad (Symbol) – Gradient
- history (Symbol) – History
- lr (float, required) – Learning rate
- epsilon (float, optional, default=1e-07) – epsilon
- wd (float, optional, default=0) – weight decay
- rescale_grad (float, optional, default=1) – Rescale gradient to grad = rescale_grad*grad.
- clip_gradient (float, optional, default=-1) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
adam_update
(weight=None, grad=None, mean=None, var=None, lr=_Null, beta1=_Null, beta2=_Null, epsilon=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, lazy_update=_Null, name=None, attr=None, out=None, **kwargs)¶ Update function for Adam optimizer. Adam is seen as a generalization of AdaGrad.
Adam update consists of the following steps, where g represents gradient and m, v are 1st and 2nd order moment estimates (mean and variance).
\[\begin{split}g_t = \nabla J(W_{t-1})\\ m_t = \beta_1 m_{t-1} + (1 - \beta_1) g_t\\ v_t = \beta_2 v_{t-1} + (1 - \beta_2) g_t^2\\ W_t = W_{t-1} - \alpha \frac{ m_t }{ \sqrt{ v_t } + \epsilon }\end{split}\]It updates the weights using:
m = beta1*m + (1-beta1)*grad v = beta2*v + (1-beta2)*(grad**2) w += - learning_rate * m / (sqrt(v) + epsilon)
However, if grad’s storage type is
row_sparse
,lazy_update
is True and the storage type of weight is the same as those of m and v, only the row slices whose indices appear in grad.indices are updated (for w, m and v):for row in grad.indices: m[row] = beta1*m[row] + (1-beta1)*grad[row] v[row] = beta2*v[row] + (1-beta2)*(grad[row]**2) w[row] += - learning_rate * m[row] / (sqrt(v[row]) + epsilon)
Defined in src/operator/optimizer_op.cc:L495
Parameters: - weight (Symbol) – Weight
- grad (Symbol) – Gradient
- mean (Symbol) – Moving mean
- var (Symbol) – Moving variance
- lr (float, required) – Learning rate
- beta1 (float, optional, default=0.9) – The decay rate for the 1st moment estimates.
- beta2 (float, optional, default=0.999) – The decay rate for the 2nd moment estimates.
- epsilon (float, optional, default=1e-08) – A small constant for numerical stability.
- wd (float, optional, default=0) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.
- rescale_grad (float, optional, default=1) – Rescale gradient to grad = rescale_grad*grad.
- clip_gradient (float, optional, default=-1) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).
- lazy_update (boolean, optional, default=1) – If true, lazy updates are applied if gradient’s stype is row_sparse and all of w, m and v have the same stype
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
add_n
(*args, **kwargs)¶ Adds all input arguments element-wise.
\[add\_n(a_1, a_2, ..., a_n) = a_1 + a_2 + ... + a_n\]add_n
is potentially more efficient than callingadd
by n times.The storage type of
add_n
output depends on storage types of inputs- add_n(row_sparse, row_sparse, ..) = row_sparse
- add_n(default, csr, default) = default
- add_n(any input combinations longer than 4 (>4) with at least one default type) = default
- otherwise,
add_n
falls all inputs back to default storage and generates default storage
Defined in src/operator/tensor/elemwise_sum.cc:L156 This function support variable length of positional input.
Parameters: - args (Symbol[]) – Positional input arguments
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
arccos
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise inverse cosine of the input array.
The input should be in range [-1, 1]. The output is in the closed interval \([0, \pi]\)
\[arccos([-1, -.707, 0, .707, 1]) = [\pi, 3\pi/4, \pi/2, \pi/4, 0]\]The storage type of
arccos
output is always denseDefined in src/operator/tensor/elemwise_unary_op_trig.cc:L123
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
arccosh
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns the element-wise inverse hyperbolic cosine of the input array, computed element-wise.
The storage type of
arccosh
output is always denseDefined in src/operator/tensor/elemwise_unary_op_trig.cc:L264
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
arcsin
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise inverse sine of the input array.
The input should be in the range [-1, 1]. The output is in the closed interval of [\(-\pi/2\), \(\pi/2\)].
\[arcsin([-1, -.707, 0, .707, 1]) = [-\pi/2, -\pi/4, 0, \pi/4, \pi/2]\]The storage type of
arcsin
output depends upon the input storage type:- arcsin(default) = default
- arcsin(row_sparse) = row_sparse
- arcsin(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L104
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
arcsinh
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns the element-wise inverse hyperbolic sine of the input array, computed element-wise.
The storage type of
arcsinh
output depends upon the input storage type:- arcsinh(default) = default
- arcsinh(row_sparse) = row_sparse
- arcsinh(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L250
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
arctan
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise inverse tangent of the input array.
The output is in the closed interval \([-\pi/2, \pi/2]\)
\[arctan([-1, 0, 1]) = [-\pi/4, 0, \pi/4]\]The storage type of
arctan
output depends upon the input storage type:- arctan(default) = default
- arctan(row_sparse) = row_sparse
- arctan(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L144
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
arctanh
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns the element-wise inverse hyperbolic tangent of the input array, computed element-wise.
The storage type of
arctanh
output depends upon the input storage type:- arctanh(default) = default
- arctanh(row_sparse) = row_sparse
- arctanh(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L281
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
broadcast_add
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise sum of the input arrays with broadcasting.
broadcast_plus is an alias to the function broadcast_add.
Example:
x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_add(x, y) = [[ 1., 1., 1.], [ 2., 2., 2.]] broadcast_plus(x, y) = [[ 1., 1., 1.], [ 2., 2., 2.]]
Supported sparse operations:
broadcast_add(csr, dense(1D)) = dense broadcast_add(dense(1D), csr) = denseDefined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L58
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
broadcast_div
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise division of the input arrays with broadcasting.
Example:
x = [[ 6., 6., 6.], [ 6., 6., 6.]] y = [[ 2.], [ 3.]] broadcast_div(x, y) = [[ 3., 3., 3.], [ 2., 2., 2.]]
Supported sparse operations:
broadcast_div(csr, dense(1D)) = csrDefined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L187
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
broadcast_minus
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise difference of the input arrays with broadcasting.
broadcast_minus is an alias to the function broadcast_sub.
Example:
x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_sub(x, y) = [[ 1., 1., 1.], [ 0., 0., 0.]] broadcast_minus(x, y) = [[ 1., 1., 1.], [ 0., 0., 0.]]
Supported sparse operations:
broadcast_sub/minus(csr, dense(1D)) = dense broadcast_sub/minus(dense(1D), csr) = denseDefined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L106
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
broadcast_mul
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise product of the input arrays with broadcasting.
Example:
x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_mul(x, y) = [[ 0., 0., 0.], [ 1., 1., 1.]]
Supported sparse operations:
broadcast_mul(csr, dense(1D)) = csrDefined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L146
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
broadcast_plus
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise sum of the input arrays with broadcasting.
broadcast_plus is an alias to the function broadcast_add.
Example:
x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_add(x, y) = [[ 1., 1., 1.], [ 2., 2., 2.]] broadcast_plus(x, y) = [[ 1., 1., 1.], [ 2., 2., 2.]]
Supported sparse operations:
broadcast_add(csr, dense(1D)) = dense broadcast_add(dense(1D), csr) = denseDefined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L58
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
broadcast_sub
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise difference of the input arrays with broadcasting.
broadcast_minus is an alias to the function broadcast_sub.
Example:
x = [[ 1., 1., 1.], [ 1., 1., 1.]] y = [[ 0.], [ 1.]] broadcast_sub(x, y) = [[ 1., 1., 1.], [ 0., 0., 0.]] broadcast_minus(x, y) = [[ 1., 1., 1.], [ 0., 0., 0.]]
Supported sparse operations:
broadcast_sub/minus(csr, dense(1D)) = dense broadcast_sub/minus(dense(1D), csr) = denseDefined in src/operator/tensor/elemwise_binary_broadcast_op_basic.cc:L106
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
cast_storage
(data=None, stype=_Null, name=None, attr=None, out=None, **kwargs)¶ Casts tensor storage type to the new type.
When an NDArray with default storage type is cast to csr or row_sparse storage, the result is compact, which means:
- for csr, zero values will not be retained
- for row_sparse, row slices of all zeros will not be retained
The storage type of
cast_storage
output depends on stype parameter:- cast_storage(csr, ‘default’) = default
- cast_storage(row_sparse, ‘default’) = default
- cast_storage(default, ‘csr’) = csr
- cast_storage(default, ‘row_sparse’) = row_sparse
- cast_storage(csr, ‘csr’) = csr
- cast_storage(row_sparse, ‘row_sparse’) = row_sparse
Example:
dense = [[ 0., 1., 0.], [ 2., 0., 3.], [ 0., 0., 0.], [ 0., 0., 0.]] # cast to row_sparse storage type rsp = cast_storage(dense, 'row_sparse') rsp.indices = [0, 1] rsp.values = [[ 0., 1., 0.], [ 2., 0., 3.]] # cast to csr storage type csr = cast_storage(dense, 'csr') csr.indices = [1, 0, 2] csr.values = [ 1., 2., 3.] csr.indptr = [0, 1, 3, 3, 3]
Defined in src/operator/tensor/cast_storage.cc:L71
Parameters: - data (Symbol) – The input.
- stype ({'csr', 'default', 'row_sparse'}, required) – Output storage type.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
cbrt
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise cube-root value of the input.
\[cbrt(x) = \sqrt[3]{x}\]Example:
cbrt([1, 8, -125]) = [1, 2, -5]
The storage type of
cbrt
output depends upon the input storage type:- cbrt(default) = default
- cbrt(row_sparse) = row_sparse
- cbrt(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L889
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
ceil
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise ceiling of the input.
The ceil of the scalar x is the smallest integer i, such that i >= x.
Example:
ceil([-2.1, -1.9, 1.5, 1.9, 2.1]) = [-2., -1., 2., 2., 3.]
The storage type of
ceil
output depends upon the input storage type:- ceil(default) = default
- ceil(row_sparse) = row_sparse
- ceil(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L746
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
clip
(data=None, a_min=_Null, a_max=_Null, name=None, attr=None, out=None, **kwargs)¶ Clips (limits) the values in an array.
Given an interval, values outside the interval are clipped to the interval edges. Clipping
x
between a_min and a_x would be:clip(x, a_min, a_max) = max(min(x, a_max), a_min))
Example:
x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] clip(x,1,8) = [ 1., 1., 2., 3., 4., 5., 6., 7., 8., 8.]
The storage type of
clip
output depends on storage types of inputs and the a_min, a_max parameter values:- clip(default) = default
- clip(row_sparse, a_min <= 0, a_max >= 0) = row_sparse
- clip(csr, a_min <= 0, a_max >= 0) = csr
- clip(row_sparse, a_min < 0, a_max < 0) = default
- clip(row_sparse, a_min > 0, a_max > 0) = default
- clip(csr, a_min < 0, a_max < 0) = csr
- clip(csr, a_min > 0, a_max > 0) = csr
Defined in src/operator/tensor/matrix_op.cc:L618
Parameters: - data (Symbol) – Input array.
- a_min (float, required) – Minimum value
- a_max (float, required) – Maximum value
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
concat
(*data, **kwargs)¶ Joins input arrays along a given axis.
Note
Concat is deprecated. Use concat instead.
The dimensions of the input arrays should be the same except the axis along which they will be concatenated. The dimension of the output array along the concatenated axis will be equal to the sum of the corresponding dimensions of the input arrays.
The storage type of
concat
output depends on storage types of inputs- concat(csr, csr, ..., csr, dim=0) = csr
- otherwise,
concat
generates output with default storage
Example:
x = [[1,1],[2,2]] y = [[3,3],[4,4],[5,5]] z = [[6,6], [7,7],[8,8]] concat(x,y,z,dim=0) = [[ 1., 1.], [ 2., 2.], [ 3., 3.], [ 4., 4.], [ 5., 5.], [ 6., 6.], [ 7., 7.], [ 8., 8.]] Note that you cannot concat x,y,z along dimension 1 since dimension 0 is not the same for all the input arrays. concat(y,z,dim=1) = [[ 3., 3., 6., 6.], [ 4., 4., 7., 7.], [ 5., 5., 8., 8.]]
Defined in src/operator/nn/concat.cc:L368 This function support variable length of positional input.
Parameters: - data (Symbol[]) – List of arrays to concatenate
- dim (int, optional, default='1') – the dimension to be concated.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
cos
(data=None, name=None, attr=None, out=None, **kwargs)¶ Computes the element-wise cosine of the input array.
The input should be in radians (\(2\pi\) rad equals 360 degrees).
\[cos([0, \pi/4, \pi/2]) = [1, 0.707, 0]\]The storage type of
cos
output is always denseDefined in src/operator/tensor/elemwise_unary_op_trig.cc:L63
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
cosh
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns the hyperbolic cosine of the input array, computed element-wise.
\[cosh(x) = 0.5\times(exp(x) + exp(-x))\]The storage type of
cosh
output is always denseDefined in src/operator/tensor/elemwise_unary_op_trig.cc:L216
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
degrees
(data=None, name=None, attr=None, out=None, **kwargs)¶ Converts each element of the input array from radians to degrees.
\[degrees([0, \pi/2, \pi, 3\pi/2, 2\pi]) = [0, 90, 180, 270, 360]\]The storage type of
degrees
output depends upon the input storage type:- degrees(default) = default
- degrees(row_sparse) = row_sparse
- degrees(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L163
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
dot
(lhs=None, rhs=None, transpose_a=_Null, transpose_b=_Null, forward_stype=_Null, name=None, attr=None, out=None, **kwargs)¶ Dot product of two arrays.
dot
‘s behavior depends on the input array dimensions:1-D arrays: inner product of vectors
2-D arrays: matrix multiplication
N-D arrays: a sum product over the last axis of the first input and the first axis of the second input
For example, given 3-D
x
with shape (n,m,k) andy
with shape (k,r,s), the result array will have shape (n,m,r,s). It is computed by:dot(x,y)[i,j,a,b] = sum(x[i,j,:]*y[:,a,b])
Example:
x = reshape([0,1,2,3,4,5,6,7], shape=(2,2,2)) y = reshape([7,6,5,4,3,2,1,0], shape=(2,2,2)) dot(x,y)[0,0,1,1] = 0 sum(x[0,0,:]*y[:,1,1]) = 0
The storage type of
dot
output depends on storage types of inputs, transpose option and forward_stype option for output storage type. Implemented sparse operations include:- dot(default, default, transpose_a=True/False, transpose_b=True/False) = default
- dot(csr, default, transpose_a=True) = default
- dot(csr, default, transpose_a=True) = row_sparse
- dot(csr, default) = default
- dot(csr, row_sparse) = default
- dot(default, csr) = csr (CPU only)
- dot(default, csr, forward_stype=’default’) = default
- dot(default, csr, transpose_b=True, forward_stype=’default’) = default
If the combination of input storage types and forward_stype does not match any of the above patterns,
dot
will fallback and generate output with default storage.Note
If the storage type of the lhs is “csr”, the storage type of gradient w.r.t rhs will be “row_sparse”. Only a subset of optimizers support sparse gradients, including SGD, AdaGrad and Adam. Note that by default lazy updates is turned on, which may perform differently from standard updates. For more details, please check the Optimization API at: /api/python/optimization/optimization.html
Defined in src/operator/tensor/dot.cc:L77
Parameters: - lhs (Symbol) – The first input
- rhs (Symbol) – The second input
- transpose_a (boolean, optional, default=0) – If true then transpose the first input before dot.
- transpose_b (boolean, optional, default=0) – If true then transpose the second input before dot.
- forward_stype ({None, 'csr', 'default', 'row_sparse'},optional, default='None') – The desired storage type of the forward output given by user, if thecombination of input storage types and this hint does not matchany implemented ones, the dot operator will perform fallback operationand still produce an output of the desired storage type.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
elemwise_add
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Adds arguments element-wise.
The storage type of
elemwise_add
output depends on storage types of inputs- elemwise_add(row_sparse, row_sparse) = row_sparse
- elemwise_add(csr, csr) = csr
- elemwise_add(default, csr) = default
- elemwise_add(csr, default) = default
- elemwise_add(default, rsp) = default
- elemwise_add(rsp, default) = default
- otherwise,
elemwise_add
generates output with default storage
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
elemwise_div
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Divides arguments element-wise.
The storage type of
elemwise_div
output is always denseParameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
elemwise_mul
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Multiplies arguments element-wise.
The storage type of
elemwise_mul
output depends on storage types of inputs- elemwise_mul(default, default) = default
- elemwise_mul(row_sparse, row_sparse) = row_sparse
- elemwise_mul(default, row_sparse) = row_sparse
- elemwise_mul(row_sparse, default) = row_sparse
- elemwise_mul(csr, csr) = csr
- otherwise,
elemwise_mul
generates output with default storage
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
elemwise_sub
(lhs=None, rhs=None, name=None, attr=None, out=None, **kwargs)¶ Subtracts arguments element-wise.
The storage type of
elemwise_sub
output depends on storage types of inputs- elemwise_sub(row_sparse, row_sparse) = row_sparse
- elemwise_sub(csr, csr) = csr
- elemwise_sub(default, csr) = default
- elemwise_sub(csr, default) = default
- elemwise_sub(default, rsp) = default
- elemwise_sub(rsp, default) = default
- otherwise,
elemwise_sub
generates output with default storage
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
exp
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise exponential value of the input.
\[exp(x) = e^x \approx 2.718^x\]Example:
exp([0, 1, 2]) = [1., 2.71828175, 7.38905621]
The storage type of
exp
output is always denseDefined in src/operator/tensor/elemwise_unary_op_basic.cc:L929
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
expm1
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns
exp(x) - 1
computed element-wise on the input.This function provides greater precision than
exp(x) - 1
for small values ofx
.The storage type of
expm1
output depends upon the input storage type:- expm1(default) = default
- expm1(row_sparse) = row_sparse
- expm1(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L1008
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
fix
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise rounded value to the nearest integer towards zero of the input.
Example:
fix([-2.1, -1.9, 1.9, 2.1]) = [-2., -1., 1., 2.]
The storage type of
fix
output depends upon the input storage type:- fix(default) = default
- fix(row_sparse) = row_sparse
- fix(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L803
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
floor
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise floor of the input.
The floor of the scalar x is the largest integer i, such that i <= x.
Example:
floor([-2.1, -1.9, 1.5, 1.9, 2.1]) = [-3., -2., 1., 1., 2.]
The storage type of
floor
output depends upon the input storage type:- floor(default) = default
- floor(row_sparse) = row_sparse
- floor(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L765
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
ftrl_update
(weight=None, grad=None, z=None, n=None, lr=_Null, lamda1=_Null, beta=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, name=None, attr=None, out=None, **kwargs)¶ Update function for Ftrl optimizer. Referenced from Ad Click Prediction: a View from the Trenches, available at http://dl.acm.org/citation.cfm?id=2488200.
It updates the weights using:
rescaled_grad = clip(grad * rescale_grad, clip_gradient) z += rescaled_grad - (sqrt(n + rescaled_grad**2) - sqrt(n)) * weight / learning_rate n += rescaled_grad**2 w = (sign(z) * lamda1 - z) / ((beta + sqrt(n)) / learning_rate + wd) * (abs(z) > lamda1)
If w, z and n are all of
row_sparse
storage type, only the row slices whose indices appear in grad.indices are updated (for w, z and n):for row in grad.indices: rescaled_grad[row] = clip(grad[row] * rescale_grad, clip_gradient) z[row] += rescaled_grad[row] - (sqrt(n[row] + rescaled_grad[row]**2) - sqrt(n[row])) * weight[row] / learning_rate n[row] += rescaled_grad[row]**2 w[row] = (sign(z[row]) * lamda1 - z[row]) / ((beta + sqrt(n[row])) / learning_rate + wd) * (abs(z[row]) > lamda1)
Defined in src/operator/optimizer_op.cc:L632
Parameters: - weight (Symbol) – Weight
- grad (Symbol) – Gradient
- z (Symbol) – z
- n (Symbol) – Square of grad
- lr (float, required) – Learning rate
- lamda1 (float, optional, default=0.01) – The L1 regularization coefficient.
- beta (float, optional, default=1) – Per-Coordinate Learning Rate beta.
- wd (float, optional, default=0) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.
- rescale_grad (float, optional, default=1) – Rescale gradient to grad = rescale_grad*grad.
- clip_gradient (float, optional, default=-1) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
gamma
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns the gamma function (extension of the factorial function to the reals), computed element-wise on the input array.
The storage type of
gamma
output is always denseParameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
gammaln
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise log of the absolute value of the gamma function of the input.
The storage type of
gammaln
output is always denseParameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
log
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise Natural logarithmic value of the input.
The natural logarithm is logarithm in base e, so that
log(exp(x)) = x
The storage type of
log
output is always denseDefined in src/operator/tensor/elemwise_unary_op_basic.cc:L941
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
log10
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise Base-10 logarithmic value of the input.
10**log10(x) = x
The storage type of
log10
output is always denseDefined in src/operator/tensor/elemwise_unary_op_basic.cc:L953
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
log1p
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise
log(1 + x)
value of the input.This function is more accurate than
log(1 + x)
for smallx
so that \(1+x\approx 1\)The storage type of
log1p
output depends upon the input storage type:- log1p(default) = default
- log1p(row_sparse) = row_sparse
- log1p(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L990
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
log2
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise Base-2 logarithmic value of the input.
2**log2(x) = x
The storage type of
log2
output is always denseDefined in src/operator/tensor/elemwise_unary_op_basic.cc:L965
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
make_loss
(data=None, name=None, attr=None, out=None, **kwargs)¶ Make your own loss function in network construction.
This operator accepts a customized loss function symbol as a terminal loss and the symbol should be an operator with no backward dependency. The output of this function is the gradient of loss with respect to the input data.
For example, if you are a making a cross entropy loss function. Assume
out
is the predicted output andlabel
is the true label, then the cross entropy can be defined as:cross_entropy = label * log(out) + (1 - label) * log(1 - out) loss = make_loss(cross_entropy)
We will need to use
make_loss
when we are creating our own loss function or we want to combine multiple loss functions. Also we may want to stop some variables’ gradients from backpropagation. See more detail inBlockGrad
orstop_gradient
.The storage type of
make_loss
output depends upon the input storage type:- make_loss(default) = default
- make_loss(row_sparse) = row_sparse
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L298
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
mean
(data=None, axis=_Null, keepdims=_Null, exclude=_Null, name=None, attr=None, out=None, **kwargs)¶ Computes the mean of array elements over given axes.
Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L131
Parameters: - data (Symbol) – The input
- axis (Shape or None, optional, default=None) –
The axis or axes along which to perform the reduction.
The default, axis=(), will compute over all elements into a scalar array with shape (1,).If axis is int, a reduction is performed on a particular axis.
If axis is a tuple of ints, a reduction is performed on all the axes specified in the tuple.
If exclude is true, reduction will be performed on the axes that are NOT in axis instead.
Negative values means indexing from right to left.
- keepdims (boolean, optional, default=0) – If this is set to True, the reduced axes are left in the result as dimension with size one.
- exclude (boolean, optional, default=0) – Whether to perform reduction on axis that are NOT in axis instead.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
negative
(data=None, name=None, attr=None, out=None, **kwargs)¶ Numerical negative of the argument, element-wise.
The storage type of
negative
output depends upon the input storage type:- negative(default) = default
- negative(row_sparse) = row_sparse
- negative(csr) = csr
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
norm
(data=None, ord=_Null, axis=_Null, keepdims=_Null, name=None, attr=None, out=None, **kwargs)¶ Computes the norm on an NDArray.
This operator computes the norm on an NDArray with the specified axis, depending on the value of the ord parameter. By default, it computes the L2 norm on the entire array. Currently only ord=2 supports sparse ndarrays.
Examples:
x = [[[1, 2], [3, 4]], [[2, 2], [5, 6]]] norm(x, ord=2, axis=1) = [[3.1622777 4.472136 ] [5.3851647 6.3245554]] norm(x, ord=1, axis=1) = [[4., 6.], [7., 8.]] rsp = x.cast_storage('row_sparse') norm(rsp) = [5.47722578] csr = x.cast_storage('csr') norm(csr) = [5.47722578]
Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L345
Parameters: - data (Symbol) – The input
- ord (int, optional, default='2') – Order of the norm. Currently ord=1 and ord=2 is supported.
- axis (Shape or None, optional, default=None) –
- The axis or axes along which to perform the reduction.
- The default, axis=(), will compute over all elements into a scalar array with shape (1,). If axis is int, a reduction is performed on a particular axis. If axis is a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of these matrices are computed.
- keepdims (boolean, optional, default=0) – If this is set to True, the reduced axis is left in the result as dimension with size one.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
radians
(data=None, name=None, attr=None, out=None, **kwargs)¶ Converts each element of the input array from degrees to radians.
\[radians([0, 90, 180, 270, 360]) = [0, \pi/2, \pi, 3\pi/2, 2\pi]\]The storage type of
radians
output depends upon the input storage type:- radians(default) = default
- radians(row_sparse) = row_sparse
- radians(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L182
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
relu
(data=None, name=None, attr=None, out=None, **kwargs)¶ Computes rectified linear.
\[max(features, 0)\]The storage type of
relu
output depends upon the input storage type:- relu(default) = default
- relu(row_sparse) = row_sparse
- relu(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L85
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
retain
(data=None, indices=None, name=None, attr=None, out=None, **kwargs)¶ pick rows specified by user input index array from a row sparse matrix and save them in the output sparse matrix.
Example:
data = [[1, 2], [3, 4], [5, 6]] indices = [0, 1, 3] shape = (4, 2) rsp_in = row_sparse(data, indices) to_retain = [0, 3] rsp_out = retain(rsp_in, to_retain) rsp_out.values = [[1, 2], [5, 6]] rsp_out.indices = [0, 3]
The storage type of
retain
output depends on storage types of inputs- retain(row_sparse, default) = row_sparse
- otherwise,
retain
is not supported
Defined in src/operator/tensor/sparse_retain.cc:L53
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
rint
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise rounded value to the nearest integer of the input.
Note
- For input
n.5
rint
returnsn
whileround
returnsn+1
. - For input
-n.5
bothrint
andround
returns-n-1
.
Example:
rint([-1.5, 1.5, -1.9, 1.9, 2.1]) = [-2., 1., -2., 2., 2.]
The storage type of
rint
output depends upon the input storage type:- rint(default) = default
- rint(row_sparse) = row_sparse
- rint(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L727
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type: - For input
-
mxnet.symbol.sparse.
round
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise rounded value to the nearest integer of the input.
Example:
round([-1.5, 1.5, -1.9, 1.9, 2.1]) = [-2., 2., -2., 2., 2.]
The storage type of
round
output depends upon the input storage type:- round(default) = default
- round(row_sparse) = row_sparse
- round(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L706
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
rsqrt
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise inverse square-root value of the input.
\[rsqrt(x) = 1/\sqrt{x}\]Example:
rsqrt([4,9,16]) = [0.5, 0.33333334, 0.25]
The storage type of
rsqrt
output is always denseDefined in src/operator/tensor/elemwise_unary_op_basic.cc:L866
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
sgd_mom_update
(weight=None, grad=None, mom=None, lr=_Null, momentum=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, lazy_update=_Null, name=None, attr=None, out=None, **kwargs)¶ Momentum update function for Stochastic Gradient Descent (SGD) optimizer.
Momentum update has better convergence rates on neural networks. Mathematically it looks like below:
\[\begin{split}v_1 = \alpha * \nabla J(W_0)\\ v_t = \gamma v_{t-1} - \alpha * \nabla J(W_{t-1})\\ W_t = W_{t-1} + v_t\end{split}\]It updates the weights using:
v = momentum * v - learning_rate * gradient weight += v
Where the parameter
momentum
is the decay rate of momentum estimates at each epoch.However, if grad’s storage type is
row_sparse
,lazy_update
is True and weight’s storage type is the same as momentum’s storage type, only the row slices whose indices appear in grad.indices are updated (for both weight and momentum):for row in gradient.indices: v[row] = momentum[row] * v[row] - learning_rate * gradient[row] weight[row] += v[row]
Defined in src/operator/optimizer_op.cc:L372
Parameters: - weight (Symbol) – Weight
- grad (Symbol) – Gradient
- mom (Symbol) – Momentum
- lr (float, required) – Learning rate
- momentum (float, optional, default=0) – The decay rate of momentum estimates at each epoch.
- wd (float, optional, default=0) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.
- rescale_grad (float, optional, default=1) – Rescale gradient to grad = rescale_grad*grad.
- clip_gradient (float, optional, default=-1) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).
- lazy_update (boolean, optional, default=1) – If true, lazy updates are applied if gradient’s stype is row_sparse and both weight and momentum have the same stype
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
sgd_update
(weight=None, grad=None, lr=_Null, wd=_Null, rescale_grad=_Null, clip_gradient=_Null, lazy_update=_Null, name=None, attr=None, out=None, **kwargs)¶ Update function for Stochastic Gradient Descent (SDG) optimizer.
It updates the weights using:
weight = weight - learning_rate * (gradient + wd * weight)
However, if gradient is of
row_sparse
storage type andlazy_update
is True, only the row slices whose indices appear in grad.indices are updated:for row in gradient.indices: weight[row] = weight[row] - learning_rate * (gradient[row] + wd * weight[row])
Defined in src/operator/optimizer_op.cc:L331
Parameters: - weight (Symbol) – Weight
- grad (Symbol) – Gradient
- lr (float, required) – Learning rate
- wd (float, optional, default=0) – Weight decay augments the objective function with a regularization term that penalizes large weights. The penalty scales with the square of the magnitude of each weight.
- rescale_grad (float, optional, default=1) – Rescale gradient to grad = rescale_grad*grad.
- clip_gradient (float, optional, default=-1) – Clip gradient to the range of [-clip_gradient, clip_gradient] If clip_gradient <= 0, gradient clipping is turned off. grad = max(min(grad, clip_gradient), -clip_gradient).
- lazy_update (boolean, optional, default=1) – If true, lazy updates are applied if gradient’s stype is row_sparse.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
sigmoid
(data=None, name=None, attr=None, out=None, **kwargs)¶ Computes sigmoid of x element-wise.
\[y = 1 / (1 + exp(-x))\]The storage type of
sigmoid
output is always denseDefined in src/operator/tensor/elemwise_unary_op_basic.cc:L101
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
sign
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise sign of the input.
Example:
sign([-2, 0, 3]) = [-1, 0, 1]
The storage type of
sign
output depends upon the input storage type:- sign(default) = default
- sign(row_sparse) = row_sparse
- sign(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L687
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
sin
(data=None, name=None, attr=None, out=None, **kwargs)¶ Computes the element-wise sine of the input array.
The input should be in radians (\(2\pi\) rad equals 360 degrees).
\[sin([0, \pi/4, \pi/2]) = [0, 0.707, 1]\]The storage type of
sin
output depends upon the input storage type:- sin(default) = default
- sin(row_sparse) = row_sparse
- sin(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L46
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
sinh
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns the hyperbolic sine of the input array, computed element-wise.
\[sinh(x) = 0.5\times(exp(x) - exp(-x))\]The storage type of
sinh
output depends upon the input storage type:- sinh(default) = default
- sinh(row_sparse) = row_sparse
- sinh(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L201
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
slice
(data=None, begin=_Null, end=_Null, step=_Null, name=None, attr=None, out=None, **kwargs)¶ Slices a region of the array.
Note
crop
is deprecated. Useslice
instead.This function returns a sliced array between the indices given by begin and end with the corresponding step.
For an input array of
shape=(d_0, d_1, ..., d_n-1)
, slice operation withbegin=(b_0, b_1...b_m-1)
,end=(e_0, e_1, ..., e_m-1)
, andstep=(s_0, s_1, ..., s_m-1)
, where m <= n, results in an array with the shape(|e_0-b_0|/|s_0|, ..., |e_m-1-b_m-1|/|s_m-1|, d_m, ..., d_n-1)
.The resulting array’s k-th dimension contains elements from the k-th dimension of the input array starting from index
b_k
(inclusive) with steps_k
until reachinge_k
(exclusive).If the k-th elements are None in the sequence of begin, end, and step, the following rule will be used to set default values. If s_k is None, set s_k=1. If s_k > 0, set b_k=0, e_k=d_k; else, set b_k=d_k-1, e_k=-1.
The storage type of
slice
output depends on storage types of inputs- slice(csr) = csr
- otherwise,
slice
generates output with default storage
Note
When input data storage type is csr, it only supports
step=(), or step=(None,), or step=(1,) to generate a csr output. For other step parameter values, it falls back to slicing a dense tensor.
Example:
x = [[ 1., 2., 3., 4.], [ 5., 6., 7., 8.], [ 9., 10., 11., 12.]] slice(x, begin=(0,1), end=(2,4)) = [[ 2., 3., 4.], [ 6., 7., 8.]] slice(x, begin=(None, 0), end=(None, 3), step=(-1, 2)) = [[9., 11.], [5., 7.], [1., 3.]]
Defined in src/operator/tensor/matrix_op.cc:L413
Parameters: - data (Symbol) – Source input
- begin (Shape(tuple), required) – starting indices for the slice operation, supports negative indices.
- end (Shape(tuple), required) – ending indices for the slice operation, supports negative indices.
- step (Shape(tuple), optional, default=[]) – step for the slice operation, supports negative values.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
sqrt
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise square-root value of the input.
\[\textrm{sqrt}(x) = \sqrt{x}\]Example:
sqrt([4, 9, 16]) = [2, 3, 4]
The storage type of
sqrt
output depends upon the input storage type:- sqrt(default) = default
- sqrt(row_sparse) = row_sparse
- sqrt(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L846
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
square
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns element-wise squared value of the input.
\[square(x) = x^2\]Example:
square([2, 3, 4]) = [4, 9, 16]
The storage type of
square
output depends upon the input storage type:- square(default) = default
- square(row_sparse) = row_sparse
- square(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L823
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
stop_gradient
(data=None, name=None, attr=None, out=None, **kwargs)¶ Stops gradient computation.
Stops the accumulated gradient of the inputs from flowing through this operator in the backward direction. In other words, this operator prevents the contribution of its inputs to be taken into account for computing gradients.
Example:
v1 = [1, 2] v2 = [0, 1] a = Variable('a') b = Variable('b') b_stop_grad = stop_gradient(3 * b) loss = MakeLoss(b_stop_grad + a) executor = loss.simple_bind(ctx=cpu(), a=(1,2), b=(1,2)) executor.forward(is_train=True, a=v1, b=v2) executor.outputs [ 1. 5.] executor.backward() executor.grad_arrays [ 0. 0.] [ 1. 1.]
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L265
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
sum
(data=None, axis=_Null, keepdims=_Null, exclude=_Null, name=None, attr=None, out=None, **kwargs)¶ Computes the sum of array elements over given axes.
Note
sum and sum_axis are equivalent. For ndarray of csr storage type summation along axis 0 and axis 1 is supported. Setting keepdims or exclude to True will cause a fallback to dense operator.
Example:
data = [[[1, 2], [2, 3], [1, 3]], [[1, 4], [4, 3], [5, 2]], [[7, 1], [7, 2], [7, 3]]] sum(data, axis=1) [[ 4. 8.] [ 10. 9.] [ 21. 6.]] sum(data, axis=[1,2]) [ 12. 19. 27.] data = [[1, 2, 0], [3, 0, 1], [4, 1, 0]] csr = cast_storage(data, 'csr') sum(csr, axis=0) [ 8. 3. 1.] sum(csr, axis=1) [ 3. 4. 5.]
Defined in src/operator/tensor/broadcast_reduce_op_value.cc:L115
Parameters: - data (Symbol) – The input
- axis (Shape or None, optional, default=None) –
The axis or axes along which to perform the reduction.
The default, axis=(), will compute over all elements into a scalar array with shape (1,).If axis is int, a reduction is performed on a particular axis.
If axis is a tuple of ints, a reduction is performed on all the axes specified in the tuple.
If exclude is true, reduction will be performed on the axes that are NOT in axis instead.
Negative values means indexing from right to left.
- keepdims (boolean, optional, default=0) – If this is set to True, the reduced axes are left in the result as dimension with size one.
- exclude (boolean, optional, default=0) – Whether to perform reduction on axis that are NOT in axis instead.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
tan
(data=None, name=None, attr=None, out=None, **kwargs)¶ Computes the element-wise tangent of the input array.
The input should be in radians (\(2\pi\) rad equals 360 degrees).
\[tan([0, \pi/4, \pi/2]) = [0, 1, -inf]\]The storage type of
tan
output depends upon the input storage type:- tan(default) = default
- tan(row_sparse) = row_sparse
- tan(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L83
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
tanh
(data=None, name=None, attr=None, out=None, **kwargs)¶ Returns the hyperbolic tangent of the input array, computed element-wise.
\[tanh(x) = sinh(x) / cosh(x)\]The storage type of
tanh
output depends upon the input storage type:- tanh(default) = default
- tanh(row_sparse) = row_sparse
- tanh(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_trig.cc:L234
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
trunc
(data=None, name=None, attr=None, out=None, **kwargs)¶ Return the element-wise truncated value of the input.
The truncated value of the scalar x is the nearest integer i which is closer to zero than x is. In short, the fractional part of the signed number x is discarded.
Example:
trunc([-2.1, -1.9, 1.5, 1.9, 2.1]) = [-2., -1., 1., 1., 2.]
The storage type of
trunc
output depends upon the input storage type:- trunc(default) = default
- trunc(row_sparse) = row_sparse
- trunc(csr) = csr
Defined in src/operator/tensor/elemwise_unary_op_basic.cc:L785
Parameters: - data (Symbol) – The input array.
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
where
(condition=None, x=None, y=None, name=None, attr=None, out=None, **kwargs)¶ Return the elements, either from x or y, depending on the condition.
Given three ndarrays, condition, x, and y, return an ndarray with the elements from x or y, depending on the elements from condition are true or false. x and y must have the same shape. If condition has the same shape as x, each element in the output array is from x if the corresponding element in the condition is true, and from y if false.
If condition does not have the same shape as x, it must be a 1D array whose size is the same as x’s first dimension size. Each row of the output array is from x’s row if the corresponding element from condition is true, and from y’s row if false.
Note that all non-zero values are interpreted as
True
in condition.Examples:
x = [[1, 2], [3, 4]] y = [[5, 6], [7, 8]] cond = [[0, 1], [-1, 0]] where(cond, x, y) = [[5, 2], [3, 8]] csr_cond = cast_storage(cond, 'csr') where(csr_cond, x, y) = [[5, 2], [3, 8]]
Defined in src/operator/tensor/control_flow_op.cc:L57
Parameters: Returns: The result symbol.
Return type:
-
mxnet.symbol.sparse.
zeros_like
(data=None, name=None, attr=None, out=None, **kwargs)¶ Return an array of zeros with the same shape, type and storage type as the input array.
The storage type of
zeros_like
output depends on the storage type of the input- zeros_like(row_sparse) = row_sparse
- zeros_like(csr) = csr
- zeros_like(default) = default
Examples:
x = [[ 1., 1., 1.], [ 1., 1., 1.]] zeros_like(x) = [[ 0., 0., 0.], [ 0., 0., 0.]]
Parameters: - data (Symbol) – The input
- name (string, optional.) – Name of the resulting symbol.
Returns: The result symbol.
Return type: