- fn add_gate vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe
datasets #
VTL Datasets
This module exposes some functionalities to download and use the VTL datasets. For this we created some batch based iterators to load the datasets.We expose the following datasets:
- Mnist: A dataset of handwritten digits. - Imdb: A dataset of IMDB reviews for sentiment analysis.
VTL Datasets
This module exposes some functionalities to download and use the VTL datasets. For this we created some batch based iterators to load the datasets. We expose the following datasets:
- Mnist: A dataset of handwritten digits.
- Imdb: A dataset of IMDB reviews for sentiment analysis.
VTL Datasets
This module exposes some functionalities to download and
-
+const imdb_folder_name = 'aclImdb'const mnist_test_labels_file = 't10k-labels-idx1-ubyte.gz'-
+const imdb_file_name ='${imdb_folder_name}_v1.tar.gz'const mnist_test_images_file = 't10k-images-idx3-ubyte.gz'-
+const imdb_base_url = 'http://ai.stanford.edu/~amaas/data/sentiment/'const mnist_train_labels_file = 'train-labels-idx1-ubyte.gz'-
+const mnist_base_url = 'http://yann.lecun.com/exdb/mnist/'const mnist_train_images_file = 'train-images-idx3-ubyte.gz'-
+const mnist_train_images_file = 'train-images-idx3-ubyte.gz'const mnist_base_url = 'http://yann.lecun.com/exdb/mnist/'-
+const mnist_train_labels_file = 'train-labels-idx1-ubyte.gz'const imdb_base_url = 'http://ai.stanford.edu/~amaas/data/sentiment/'-
+const mnist_test_images_file = 't10k-images-idx3-ubyte.gz'const imdb_file_name ='${imdb_folder_name}_v1.tar.gz'-
+const mnist_test_labels_file = 't10k-labels-idx1-ubyte.gz'const imdb_folder_name = 'aclImdb'VTL Datasets
This module exposes some functionalities to download and -
+- README
- Constants diff --git a/index.html b/index.html index e794c764..216f8de4 100644 --- a/index.html +++ b/index.html @@ -36,7 +36,7 @@
- An n-dimensional
Tensordata structure - Sophisticated reduction, elementwise, and accumulation operations
- Data Structures that can easily be passed to C libraries
- Powerful linear algebra routines backed by VSL.
- An n-dimensional
Tensordata structure - Sophisticated reduction, elementwise, and accumulation operations
- Data Structures that can easily be passed to C libraries
- Powerful linear algebra routines backed by VSL.
- fn det vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- fn absolute_error vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- fn elu_gate vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- fn dropout_gate vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- fn mse_gate vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- fn compute_fans vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- fn dropout_layer vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- fn loss_loss vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe-
- +fn (Sequential[T]) input #
+ -fn (SequentialInfo[T]) input #
-
+fn (mut nn Sequential[T]) input(shape []int)fn (mut ls SequentialInfo[T]) input(shape []int)input adds a new input layer to the network with the given shape.
- +fn (Sequential[T]) linear #
+ -fn (SequentialInfo[T]) linear #
-
+fn (mut nn Sequential[T]) linear(output_size int)fn (mut ls SequentialInfo[T]) linear(output_size int)linear adds a new linear layer to the network with the given output size
- +fn (Sequential[T]) maxpool2d #
+ -fn (SequentialInfo[T]) maxpool2d #
-
+fn (mut nn Sequential[T]) maxpool2d(kernel []int, padding []int, stride []int)fn (mut ls SequentialInfo[T]) maxpool2d(kernel []int, padding []int, stride []int)maxpool2d adds a new maxpool2d layer to the network with the given kernel size and stride.
- +fn (Sequential[T]) mse_loss #
+ -fn (SequentialInfo[T]) mse_loss #
-
+fn (mut nn Sequential[T]) mse_loss()fn (mut ls SequentialInfo[T]) mse_loss()mse_loss sets the loss function to the mean squared error loss.
- +fn (Sequential[T]) sigmoid_cross_entropy_loss #
+ -fn (SequentialInfo[T]) sigmoid_cross_entropy_loss #
-
+fn (mut nn Sequential[T]) sigmoid_cross_entropy_loss()fn (mut ls SequentialInfo[T]) sigmoid_cross_entropy_loss()sigmoid_cross_entropy_loss sets the loss function to the sigmoid cross entropy loss.
- +fn (Sequential[T]) softmax_cross_entropy_loss #
+ -fn (SequentialInfo[T]) softmax_cross_entropy_loss #
-
+fn (mut nn Sequential[T]) softmax_cross_entropy_loss()fn (mut ls SequentialInfo[T]) softmax_cross_entropy_loss()softmax_cross_entropy_loss sets the loss function to the softmax cross entropy loss.
- +fn (Sequential[T]) flatten #
+ -fn (SequentialInfo[T]) flatten #
-
+fn (mut nn Sequential[T]) flatten()fn (mut ls SequentialInfo[T]) flatten()flatten adds a new flatten layer to the network.
- +fn (Sequential[T]) relu #
+ -fn (SequentialInfo[T]) relu #
-
+fn (mut nn Sequential[T]) relu()fn (mut ls SequentialInfo[T]) relu()relu adds a new relu layer to the network.
- +fn (Sequential[T]) leaky_relu #
+ -fn (SequentialInfo[T]) leaky_relu #
-
+fn (mut nn Sequential[T]) leaky_relu()fn (mut ls SequentialInfo[T]) leaky_relu()leaky_relu adds a new leaky_relu layer to the network.
- +fn (Sequential[T]) elu #
+ -fn (SequentialInfo[T]) elu #
-
+fn (mut nn Sequential[T]) elu()fn (mut ls SequentialInfo[T]) elu()elu adds a new elu layer to the network.
- +fn (Sequential[T]) sigmod #
+ -fn (SequentialInfo[T]) sigmod #
-
+fn (mut nn Sequential[T]) sigmod()fn (mut ls SequentialInfo[T]) sigmod()sigmod adds a new sigmod layer to the network.
- - --fn (Sequential[T]) forward #
-
- - -fn (mut nn Sequential[T]) forward(mut train autograd.Variable[T]) !&autograd.Variable[T]- - --fn (Sequential[T]) loss #
-
- - -fn (mut nn Sequential[T]) loss(output &autograd.Variable[T], target &vtl.Tensor[T]) !&autograd.Variable[T]- +fn (SequentialInfo[T]) input #
+ -fn (Sequential[T]) input #
-
+fn (mut ls SequentialInfo[T]) input(shape []int)fn (mut nn Sequential[T]) input(shape []int)input adds a new input layer to the network with the given shape.
- +fn (SequentialInfo[T]) linear #
+ -fn (Sequential[T]) linear #
-
+fn (mut ls SequentialInfo[T]) linear(output_size int)fn (mut nn Sequential[T]) linear(output_size int)linear adds a new linear layer to the network with the given output size
- +fn (SequentialInfo[T]) maxpool2d #
+ -fn (Sequential[T]) maxpool2d #
-
+fn (mut ls SequentialInfo[T]) maxpool2d(kernel []int, padding []int, stride []int)fn (mut nn Sequential[T]) maxpool2d(kernel []int, padding []int, stride []int)maxpool2d adds a new maxpool2d layer to the network with the given kernel size and stride.
- +fn (SequentialInfo[T]) mse_loss #
+ -fn (Sequential[T]) mse_loss #
-
+fn (mut ls SequentialInfo[T]) mse_loss()fn (mut nn Sequential[T]) mse_loss()mse_loss sets the loss function to the mean squared error loss.
- +fn (SequentialInfo[T]) sigmoid_cross_entropy_loss #
+ -fn (Sequential[T]) sigmoid_cross_entropy_loss #
-
+fn (mut ls SequentialInfo[T]) sigmoid_cross_entropy_loss()fn (mut nn Sequential[T]) sigmoid_cross_entropy_loss()sigmoid_cross_entropy_loss sets the loss function to the sigmoid cross entropy loss.
- +fn (SequentialInfo[T]) softmax_cross_entropy_loss #
+ -fn (Sequential[T]) softmax_cross_entropy_loss #
-
+fn (mut ls SequentialInfo[T]) softmax_cross_entropy_loss()fn (mut nn Sequential[T]) softmax_cross_entropy_loss()softmax_cross_entropy_loss sets the loss function to the softmax cross entropy loss.
- +fn (SequentialInfo[T]) flatten #
+ -fn (Sequential[T]) flatten #
-
+fn (mut ls SequentialInfo[T]) flatten()fn (mut nn Sequential[T]) flatten()flatten adds a new flatten layer to the network.
- +fn (SequentialInfo[T]) relu #
+ -fn (Sequential[T]) relu #
-
+fn (mut ls SequentialInfo[T]) relu()fn (mut nn Sequential[T]) relu()relu adds a new relu layer to the network.
- +fn (SequentialInfo[T]) leaky_relu #
+ -fn (Sequential[T]) leaky_relu #
-
+fn (mut ls SequentialInfo[T]) leaky_relu()fn (mut nn Sequential[T]) leaky_relu()leaky_relu adds a new leaky_relu layer to the network.
- +fn (SequentialInfo[T]) elu #
+ -fn (Sequential[T]) elu #
-
+fn (mut ls SequentialInfo[T]) elu()fn (mut nn Sequential[T]) elu()elu adds a new elu layer to the network.
- +fn (SequentialInfo[T]) sigmod #
+ + +fn (Sequential[T]) sigmod #
-
+fn (mut ls SequentialInfo[T]) sigmod()fn (mut nn Sequential[T]) sigmod()sigmod adds a new sigmod layer to the network.
++ + ++fn (Sequential[T]) forward #
+
+ + +fn (mut nn Sequential[T]) forward(mut train autograd.Variable[T]) !&autograd.Variable[T]+ +fn (Sequential[T]) loss #
+
+ +fn (mut nn Sequential[T]) loss(output &autograd.Variable[T], target &vtl.Tensor[T]) !&autograd.Variable[T]@@ -370,7 +370,7 @@ - +- fn sequential
- fn sequential_with_layers
- type SequentialInfo[T] +
- type Sequential[T] -
- type SequentialInfo[T] -
- struct Sequential
- fn adam_optimizer vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- interface Layer vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- fn absdev vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
- Constants
- fn from_array
-
diff --git a/vtl.html b/vtl.html
index 3ab0e3f7..615dc400 100644
--- a/vtl.html
+++ b/vtl.html
@@ -36,7 +36,7 @@
- An n-dimensional
Tensordata structure - Sophisticated reduction, elementwise, and accumulation operations - Data Structures that can easily be passed to C libraries - Powerful linear algebra routines backed by VSL that uses LAPACKE and OpenBLAS.
- An n-dimensional
Tensordata structure - Sophisticated reduction, elementwise, and accumulation operations
- Data Structures that can easily be passed to C libraries
- Powerful linear algebra routines backed by VSL that uses LAPACKE and OpenBLAS.
- README
- fn bernoulli
-
@@ -2033,6 +2027,29 @@
- interface AnyTensor
- type TensorAxisIterator[T]
-
+
- fn next +
+ - type TensorDataType + +
- type TensorIterator[T]
-
+
- fn next +
- type Tensor[T]
- fn abs
- fn acos @@ -2189,29 +2206,6 @@
- fn with_dims
The V Tensor Library
- - type TensorAxisIterator[T]
-
-
- fn next -
- - type TensorDataType - -
- type TensorIterator[T]
-
-
- fn next -
- type TensorsIterator[T]
The V Tensor Library
- interface AnyTensor
vtl- 0.2.0 3db7b44 + 0.2.0 3f725fevtl #
import vtl t := vtl.from_array([1.0, 2, 3, 4], [2, 2])! t.get([1, 1]) -// 4.0VTL Provides
In the docs you can find more information about this module
Installation
Install dependencies (optional)
We use VSL as backend for some functionalities. VTL requires VSL's linear algebra module.If you wish you to use vtl without these, the
vtlmodule will still function as normal.Follow this install instructions at VSL docs in order to install VSL with all needed dependencies.
Install VTL
v install vtl -Done. Installation completed.
Testing
To test the module, just type the following command:
v test . -License
Contributors
This work was originally based on the work done by > Christopher (christopherzimmerman).
The development of this library continues its course after having reimplemented its core > and a large part of its interface. In the same way, we do not want to stop recognizingthe work and inspiration that the library done by Christopher has given.
Made with contributors-img.
+// 4.0VTL Provides
In the docs you can find more information about this module
Installation
Install dependencies (optional)
We use VSL as backend for some functionalities. VTL requires VSL's linear algebra module. If you wish you to use vtl without these, the
vtlmodule will still function as normal.Follow this install instructions at VSL docs in order to install VSL with all needed dependencies.
Install VTL
v install vtlDone. Installation completed.
Testing
To test the module, just type the following command:
v test .License
Contributors
This work was originally based on the work done by > Christopher (christopherzimmerman).
The development of this library continues its course after having reimplemented its core > and a large part of its interface. In the same way, we do not want to stop recognizing > the work and inspiration that the library done by Christopher has given.
Made with contributors-img.
The V Tensor Library
}AnyTensor is an interface that allows for any tensor to be used in the vtl library
+ + ++ + ++fn (TensorAxisIterator[T]) next #
+
+fn (mut s TensorAxisIterator[T]) next[T]() ?(T, []int)next calls the iteration type for a given iterator which is either flat or strided and returns a Num containing the current value
+ ++ + ++type TensorDataType #
+
+type TensorDataType = bool | f32 | f64 | i16 | i64 | i8 | int | string | u16 | u32 | u64 | u8TensorDataType is a sum type that lists the possible types to be used to define storage
+ ++ + ++fn (TensorDataType) string #
+
+fn (v TensorDataType) string() stringstring returns
+ +TensorDataTypeas a string.+ + ++fn (TensorDataType) int #
+
+fn (v TensorDataType) int() intint uses
+ +TensorDataTypeas an integer.+ + ++fn (TensorDataType) i64 #
+
+fn (v TensorDataType) i64() i64i64 uses
+ +TensorDataTypeas a 64-bit integer.+ + ++fn (TensorDataType) i8 #
+
+fn (v TensorDataType) i8() i8i8 uses
+ +TensorDataTypeas a 8-bit unsigned integer.+ + ++fn (TensorDataType) i16 #
+
+fn (v TensorDataType) i16() i16i16 uses
+ +TensorDataTypeas a 16-bit unsigned integer.+ + ++fn (TensorDataType) u8 #
+
+fn (v TensorDataType) u8() u8u8 uses
+ +TensorDataTypeas a 8-bit unsigned integer.+ + ++fn (TensorDataType) u16 #
+
+fn (v TensorDataType) u16() u16u16 uses
+ +TensorDataTypeas a 16-bit unsigned integer.+ + ++fn (TensorDataType) u32 #
+
+fn (v TensorDataType) u32() u32u32 uses
+ +TensorDataTypeas a 32-bit unsigned integer.+ + ++fn (TensorDataType) u64 #
+
+fn (v TensorDataType) u64() u64u64 uses
+ +TensorDataTypeas a 64-bit unsigned integer.+ + ++fn (TensorDataType) f32 #
+
+fn (v TensorDataType) f32() f32f32 uses
+ +TensorDataTypeas a 32-bit float.+ + ++fn (TensorDataType) f64 #
+
+fn (v TensorDataType) f64() f64f64 uses
+ +TensorDataTypeas a float.+ + ++fn (TensorDataType) bool #
+
+fn (v TensorDataType) bool() boolbool uses
+ +TensorDataTypeas a bool+ +fn (TensorIterator[T]) next #
+
+fn (mut s TensorIterator[T]) next[T]() ?(T, []int)next calls the iteration type for a given iterator which is either flat or strided and returns a Num containing the current value
+@@ -1652,126 +1766,6 @@ - -The V Tensor Library
fn (t &Tensor[T]) with_dims[T](n int) !&Tensor[T]with_dims returns a new Tensor adding dimensions so that it has at least
-ndimensions- - --fn (TensorAxisIterator[T]) next #
-
-fn (mut s TensorAxisIterator[T]) next[T]() ?(T, []int)next calls the iteration type for a given iterator which is either flat or strided and returns a Num containing the current value
- -- - --type TensorDataType #
-
-type TensorDataType = bool | f32 | f64 | i16 | i64 | i8 | int | string | u16 | u32 | u64 | u8TensorDataType is a sum type that lists the possible types to be used to define storage
- -- - --fn (TensorDataType) string #
-
-fn (v TensorDataType) string() stringstring returns
- -TensorDataTypeas a string.- - --fn (TensorDataType) int #
-
-fn (v TensorDataType) int() intint uses
- -TensorDataTypeas an integer.- - --fn (TensorDataType) i64 #
-
-fn (v TensorDataType) i64() i64i64 uses
- -TensorDataTypeas a 64-bit integer.- - --fn (TensorDataType) i8 #
-
-fn (v TensorDataType) i8() i8i8 uses
- -TensorDataTypeas a 8-bit unsigned integer.- - --fn (TensorDataType) i16 #
-
-fn (v TensorDataType) i16() i16i16 uses
- -TensorDataTypeas a 16-bit unsigned integer.- - --fn (TensorDataType) u8 #
-
-fn (v TensorDataType) u8() u8u8 uses
- -TensorDataTypeas a 8-bit unsigned integer.- - --fn (TensorDataType) u16 #
-
-fn (v TensorDataType) u16() u16u16 uses
- -TensorDataTypeas a 16-bit unsigned integer.- - --fn (TensorDataType) u32 #
-
-fn (v TensorDataType) u32() u32u32 uses
- -TensorDataTypeas a 32-bit unsigned integer.- - --fn (TensorDataType) u64 #
-
-fn (v TensorDataType) u64() u64u64 uses
- -TensorDataTypeas a 64-bit unsigned integer.- - --fn (TensorDataType) f32 #
-
-fn (v TensorDataType) f32() f32f32 uses
- -TensorDataTypeas a 32-bit float.- - --fn (TensorDataType) f64 #
-
-fn (v TensorDataType) f64() f64f64 uses
- -TensorDataTypeas a float.- - --fn (TensorDataType) bool #
-
-fn (v TensorDataType) bool() boolbool uses
- -TensorDataTypeas a bool- -fn (TensorIterator[T]) next #
-
-fn (mut s TensorIterator[T]) next[T]() ?(T, []int)next calls the iteration type for a given iterator which is either flat or strided and returns a Num containing the current value
-@@ -1922,7 +1916,7 @@ - +The V Tensor Library
- An n-dimensional
- fn absdev
- interface Layer
vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- + - fn sequential
- fn loss_loss
- fn dropout_layer
- fn compute_fans
- fn mse_gate
- fn dropout_gate
- fn elu_gate
- fn absolute_error
vtl- 0.2.0 3db7b44 + 0.2.0 3f725fediff --git a/la.html b/la.html index b7ae4c56..d0b99f30 100644 --- a/la.html +++ b/la.html @@ -36,7 +36,7 @@-- +README
+ - +README #
[![Mentioned in Awesome V][awesomevbadge]][awesomevurl] [![Continuous Integration][workflowbadge]][workflowurl] [![Deploy Documentation][deploydocsbadge]][deploydocsurl] [![License: MIT][licensebadge]][licenseurl]
import vtl t := vtl.from_array([1.0, 2, 3, 4], [2, 2])! t.get([1, 1]) -// 4.0VTL Provides
In the docs you can find more information about this module
Installation
Install dependencies (optional)
We use VSL as backend for some functionalities. VTL requires VSL's linear algebra module. If you wish you to use vtl without these, the
vtlmodule will still function as normal.Follow this install instructions at VSL docs in order to install VSL with all needed dependencies.
Install VTL
v install vtl -Done. Installation completed.
Testing
To test the module, just type the following command:
v test . -License
Contributors
This work was originally based on the work done by > Christopher (christopherzimmerman).
The development of this library continues its course after having reimplemented its core > and a large part of its interface. In the same way, we do not want to stop recognizing > the work and inspiration that the library done by Christopher has given.
Made with contributors-img.
[awesomevbadge]: https://awesome.re/mentioned-badge.svg [workflowbadge]: https://github.com/vlang/vtl/actions/workflows/ci.yml/badge.svg [deploydocsbadge]: https://github.com/vlang/vtl/actions/workflows/deploy-docs.yml/badge.svg [licensebadge]: https://img.shields.io/badge/License-MIT-blue.svg [awesomevurl]: https://github.com/vlang/awesome-v/blob/master/README.md#scientific-computing [workflowurl]: https://github.com/vlang/vtl/actions/workflows/ci.yml [deploydocsurl]: https://github.com/vlang/vtl/actions/workflows/deploy-docs.yml [licenseurl]: https://github.com/vlang/vtl/blob/main/LICENSE
+// 4.0VTL Provides
In the docs you can find more information about this module
Installation
Install dependencies (optional)
We use VSL as backend for some functionalities. VTL requires VSL's linear algebra module. If you wish you to use vtl without these, the
vtlmodule will still function as normal.Follow this install instructions at VSL docs in order to install VSL with all needed dependencies.
Install VTL
v install vtlDone. Installation completed.
Testing
To test the module, just type the following command:
v test .License
Contributors
This work was originally based on the work done by > Christopher (christopherzimmerman).
The development of this library continues its course after having reimplemented its core > and a large part of its interface. In the same way, we do not want to stop recognizing > the work and inspiration that the library done by Christopher has given.
Made with contributors-img.
[awesomevbadge]: https://awesome.re/mentioned-badge.svg [workflowbadge]: https://github.com/vlang/vtl/actions/workflows/ci.yml/badge.svg [deploydocsbadge]: https://github.com/vlang/vtl/actions/workflows/deploy-docs.yml/badge.svg [licensebadge]: https://img.shields.io/badge/License-MIT-blue.svg [awesomevurl]: https://github.com/vlang/awesome-v/blob/master/README.md#scientific-computing [workflowurl]: https://github.com/vlang/vtl/actions/workflows/ci.yml [deploydocsurl]: https://github.com/vlang/vtl/actions/workflows/deploy-docs.yml [licenseurl]: https://github.com/vlang/vtl/blob/main/LICENSE
vtl- 0.2.0 3db7b44 + 0.2.0 3f725fe- +
