semvec/pypi-packages huggingface dataset:
mandarin 0.0.1a2
Template engine for Python

![Amber](./logo/Group.png)
A Template engine for Python.

## Install
```bash
pip install mandarin==0.0.1a1
```
🌱 Work in progress, please call back soon...

### Example
```
div(class="my-class" id="my-id"):
    h1 "hello"
    
# Output: "<div class='my-class' id='my-id'><h1>hello</h1></div>"
```
daxpy 0.2
A pre-machine-learning model package
Requires Python: >=3.6

# daxpy

A pre-machine-learning model package.

Package is consisted of following features:

	module checker (check_modules): ensures if necessary modules are installed in the environment.

	library importer (import_libraries) : imports required libraries.

	file reader (read_files): reads or reloads all csv, excel and pickle files given path.

	dataset describers (col_stats, df_cols): describes dataset's information.

	column summarizers (cat_sum, num_sum, col_sum): gives specific informations about columns.

Installation:

```
pip install daxpy
```

Usage:

```
from daxpy import analyzer

display_ = True 		# Displays the calculated tables.
plot_    = True 		# Plots related data given function.
data     = '../data/' 	# Directory where csv, excel and pickle data is loaded or pd.DataFrame() object.
sep_     = ',' 			# Seperator for csvs.


# Field belov should be run if the pickle files have not been created before. Otherwise next cell can be run to save some time.

mp = analyzer(display_=True, plot_=True, data=data, sep_='|')
mp.rename_dfs({'data1_111':'data_1','data2_1231': 'data2'})
mp.save_dfs('./data')


# Field below can be run after the pickle files are created. (can be run repeatedely)

mp = analyzer(data='./', plot_=True, display_=True)


mp.head_tail(mp.data1);

col_stats = mp.col_stats(mp.data1, display=False)

id_cols, date_cols, num_cols, cat_cols, cat_but_car_cols, num_but_cat_cols, num_but_cat_but_car_cols = mp.df_cols(mp.data1, display_=True, cat_th=32, car_th=10)

# Plot all columns' summary
for cat_col in cat_but_car_cols:
    mp.cat_summ(mp.data1, cat_col, threshold=0.01);
for cat_col in cat_cols:
    mp.cat_summ(mp.data1, cat_col, threshold=False);
# Analyze num_cols
for num_col in num_cols:
    mp.num_summ(mp.data1, num_col)

```
keravis 1.0.2
A high-level API for ConvNet visualizations in Keras
Requires Python: >=3.6

# keravis

keravis is a high-level API for ConvNet visualizations in Keras. As of v1.0, it supports visualizations of

1. Convolutional layer activations
2. 2-dimensional feature space representations
3. Saliency maps (vanilla backprop, guided backprop, and occlusion)
4. Generated inputs that result in maximal class scores
5. Patches in a set of images that maximally activate an intermediate neuron

with support for nested pretrained models.

This is a hobby project that was inspired by lecture 14 of Stanford's CS231n: Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/. It is not yet optimized for serious use (see [keras-vis](https://github.com/raghakot/keras-vis) instead).

## Installation

You can install keravis using pip

```bash
pip install keravis
```

## Usage

Read the [documentation](https://keravis.readthedocs.io/en/latest/?)

## Sample Visualizations

Below are sample visualizations from a small convolutional network trained on MNIST

```python
from keravis import feature_space
feature_space(model,X=x_test[:5000],y=y_test[:5000],kind='tsne')
```

![MNIST_TSNE](https://user-images.githubusercontent.com/65565946/132919099-7468290d-bc5d-4cfe-9cd4-22bea87f3849.png)

```python
from keravis import saliency_backprop
saliency_backprop(model,test_img,class_idx=7)
```

![saliency_1](https://user-images.githubusercontent.com/65565946/132919163-b4c4e5a8-a410-451c-9f23-7efbc3076110.png)

```python
from keravis import saliency_guided_backprop
saliency_guided_backprop(model,test_img,class_idx=7)
```

![saliency](https://user-images.githubusercontent.com/65565946/132919195-76ede1b1-a410-418e-ab75-1d2fa8e355bd.png)

```python
from keravis import maximal_class_score_input
maximal_class_score_input(model,class_idx=5,dim=(28,28,1))
```

![gradient_ascent_5](https://user-images.githubusercontent.com/65565946/132919308-2040b537-bdee-439b-b130-1f63c6547d4c.png)

```python
from keravis import maximally_activating_patches
maximally_activating_patches(model,'conv2d_1',X=x_test)
```

![MNIST_CONV_FEATURES](https://user-images.githubusercontent.com/65565946/132919503-2d3cd491-cfdb-4e79-a8ec-0cb8307392b5.png)