src/arraymancer/private/sequninit
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newSeqUninit
newSeqUninit[T](len: Natural): seq[T]
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Procs
func
newSeqUninit
[
T
]
(
len
:
Natural
)
:
seq
[
T
]
{.
inline
.}
Creates an uninitialzed seq. Contrary to newSequnitialized in system.nim this works for any subtype T
Source
Edit
Arraymancer
Technical reference
Core tensor API
accessors
accessors_macros_read
accessors_macros_syntax
accessors_macros_write
aggregate
algorithms
blas_l3_gemm
complex
cublas
cuda
cuda_global_state
data_structure
display
display_cuda
einsum
exporting
filling_data
higher_order_applymap
higher_order_foldreduce
incl_accessors_cuda
incl_higher_order_cuda
incl_kernels_cuda
init_copy_cpu
init_copy_cuda
init_cpu
init_cuda
init_opencl
lapack
math_functions
memory_optimization_hints
naive_l2_gemv
opencl_backend
opencl_global_state
openmp
operators_blas_l1
operators_blas_l1_cuda
operators_blas_l1_opencl
operators_blas_l2l3
operators_blas_l2l3_cuda
operators_blas_l2l3_opencl
operators_broadcasted
operators_broadcasted_cuda
operators_broadcasted_opencl
operators_comparison
operators_logical
optim_ops_fusion
p_accessors
p_accessors_macros_desugar
p_accessors_macros_read
p_accessors_macros_write
p_checks
p_complex
p_display
p_empty_tensors
p_init_cuda
p_init_opencl
p_kernels_interface_cuda
p_kernels_interface_opencl
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Neural network API
Layers: Convolution 2D
Loss: Cross-Entropy losses
Layers: Embedding
flatten
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Layers: GRU (Gated Linear Unit)
Layers: Initializations
Layers: Linear/Dense
Layers: Maxpool 2D
Loss: Mean Square Error
Neural network: Declaration
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Activation: Relu (Rectified linear Unit)
Activation: Sigmoid
Softmax
Activation: Tanh
Linear algebra, stats, ML
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algebra
auxiliary_blas
auxiliary_lapack
Common errors, MAE and MSE (L1, L2 loss)
dbscan
Eigenvalue decomposition
decomposition_lapack
Randomized Truncated SVD
distributions
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Least squares solver
least_squares_lapack
Linear systems solver
overload
Principal Component Analysis (PCA)
solve_lapack
Special linear algebra matrices
Statistics
triangular
IO & Datasets
IMDB
CSV reading and writing
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Images reading and writing
Numpy files reading and writing
io_stream_readers
MNIST
util
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Basic operations
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Hadamard product (elementwise matrix multiply)
Reduction operations
Concatenation, stacking, splitting, chunking operations
Linear algebra operations
Neuralnet primitives
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cudnn_conv_interface
Activations
Convolution 2D - CuDNN
Convolution 2D
Embeddings
Gated Recurrent Unit (GRU)
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Maxpooling
Numerical gradient
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nnpack
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p_activation
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Other docs
align_unroller
ast_utils
compiler_optim_hints
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datatypes
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foreach
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functional
gemm
gemm_packing
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gemm_tiling
gemm_ukernel_avx
gemm_ukernel_avx2
gemm_ukernel_avx512
gemm_ukernel_avx_fma
gemm_ukernel_dispatch
gemm_ukernel_generator
gemm_ukernel_generic
gemm_ukernel_sse
gemm_ukernel_sse2
gemm_ukernel_sse4_1
gemm_utils
global_config
initialization
math_ops_fusion
memory
nested_containers
openmp
sequninit
simd
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Tutorial
First steps
Taking a slice of a tensor
Matrix & vectors operations
Broadcasted operations
Transposing, Reshaping, Permuting, Concatenating
Map & Reduce
Basic iterators
Spellbook (How-To's)
How to convert a Tensor type?
How to create a new universal function?
How to create a multilayer perceptron?
Under the hood
How Arraymancer achieves its speed?
Why does `=` share data by default aka reference semantics?
Working with OpenCL and Cuda in Nim