Stefano Bosisio Steboss89
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| use mnist::*; | |
| fn main()-> Result<(), Box<dyn, Error>>{ | |
| // Deconstruct the returned Mnist struct. | |
| let Mnist { | |
| trn_img, | |
| trn_lbl, | |
| val_img, | |
| val_lbl, | |
| tst_img, |
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| [package] | |
| name = "simple_neural_networks" | |
| version = "0.1.0" | |
| edition = "2021" | |
| # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html | |
| [dependencies] | |
| tch = "0.8.0" | |
| mnist = {version = "0.5.0", features = ["download"]} |
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| pub fn linear<'a, T: Borrow<super::Path<'a>>>( | |
| vs: T, | |
| in_dim: i64, | |
| out_dim: i64, | |
| c: LinearConfig, | |
| ) -> Linear |
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| pub struct LinearConfig { | |
| pub ws_init: super::Init, | |
| pub bs_init: Option<super::Init>, | |
| pub bias: bool, | |
| } | |
| impl Default for LinearConfig { | |
| fn default() -> Self { | |
| LinearConfig { ws_init: super::Init::KaimingUniform, bs_init: None, bias: true } | |
| } |
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| impl Init { | |
| /// Re-initializes an existing tensor with the specified initialization | |
| pub fn set(self, tensor: &mut Tensor) { | |
| match self { | |
| Init::Const(cst) => { | |
| let _ = tensor.fill_(cst); | |
| } |
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| match i { | |
| Init::Const(cst) => { | |
| // Optimize the case for which a single C++ code can be done. | |
| if cst == 0. { | |
| Tensor::f_zeros(dims, (Kind::Float, device)) | |
| } else if (cst - 1.).abs() <= std::f64::EPSILON { | |
| Tensor::f_ones(dims, (Kind::Float, device)) | |
| } else { | |
| Tensor::f_ones(dims, (Kind::Float, device)).map(|t| t * cst) | |
| } |
Steboss89
/ init_constant.rs
Created
July 6, 2022 21:11
Constant method to initialize weights and biases
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| Init::Const(cst) => { | |
| // Optimize the case for which a single C++ code can be done. | |
| if cst == 0. { | |
| Tensor::f_zeros(dims, (Kind::Float, device)) | |
| } else if (cst - 1.).abs() <= std::f64::EPSILON { | |
| Tensor::f_ones(dims, (Kind::Float, device)) | |
| } else { | |
| Tensor::f_ones(dims, (Kind::Float, device)).map(|t| t * cst) | |
| } | |
| } |
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| !pip install sentence_transformers | |
| from sentence_transformers import SentenceTransformer, util | |
| # use roberta | |
| model = SentenceTransformer('stsb-roberta-large') | |
| def create_heatmap(similarity, cmap = "YlGnBu"): | |
| df = pd.DataFrame(similarity) | |
| df.columns = ['john', 'luke','mark', 'matt'] #ohn 0 mark 2 matt 3 luke 1 | |
| df.index = ['john', 'luke','mark', 'matt'] |
Steboss89
/ TfIdfVectorizer.py
Created
June 2, 2022 21:31
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| def create_heatmap(similarity, cmap = "YlGnBu"): | |
| df = pd.DataFrame(similarity) | |
| df.columns = ['john', 'luke','mark', 'matt'] #ohn 0 mark 2 matt 3 luke 1 | |
| df.index = ['john', 'luke','mark', 'matt'] | |
| fig, ax = plt.subplots(figsize=(5,5)) | |
| sns.heatmap(df, cmap=cmap) | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| import seaborn as sns |
Steboss89
/ countvectorizer.py
Created
June 2, 2022 21:31
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| def create_heatmap(similarity, cmap = "YlGnBu"): | |
| df = pd.DataFrame(similarity) | |
| df.columns = ['john', 'luke','mark', 'matt'] #ohn 0 mark 2 matt 3 luke 1 | |
| df.index = ['john', 'luke','mark', 'matt'] | |
| fig, ax = plt.subplots(figsize=(5,5)) | |
| sns.heatmap(df, cmap=cmap) | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| import seaborn as sns |