markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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MXNet Model ObjectThe `MXNetModel` class allows you to define an environment for making inference using yourmodel artifact. Like `MXNet` class we discussed [in this notebook for training an MXNet model](get_started_mnist_train.ipynb), it is high level API used to set up a docker image for your model hosting service.On... | model = MXNetModel(
entry_point="inference.py",
source_dir="code",
role=role,
model_data=mx_mnist_model_data,
framework_version="1.7.0",
py_version="py3",
) | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
Entry Point for the Inference ImageYour model artifacts pointed by `model_data` is pulled by the `MXNetModel` and it is decompressed and saved inin the docker image it defines. They become regular model checkpoint files that you would produce outside SageMaker. This means in order to use your trained model for serving... | from sagemaker.serializers import JSONSerializer
from sagemaker.deserializers import JSONDeserializer
# set local_mode to False if you want to deploy on a remote
# SageMaker instance
local_mode = False
if local_mode:
instance_type = "local"
else:
instance_type = "ml.c4.xlarge"
predictor = model.deploy(
... | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
The `predictor` we get above can be used to make prediction requests agaist a SageMaker endpoint. For moreinformation, check [the api reference for SageMaker Predictor](https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.htmlsagemaker.predictor.Predictor)Now, let's test the endpoint with some dummy data... | import random
dummy_data = {"inputs": [random.random() for _ in range(784)]} | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
In `transform_fn`, we declared that the parsed data is a python dictionary with a key `inputs` and its value should be a 1D array of length 784. Hence, the definition of `dummy_data`. | res = predictor.predict(dummy_data)
print("Predicted digit:", *map(int, res)) | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
If the input data does not look exactly like `dummy_data`, the endpoint will raise an exception. This is because of the stringent way we defined the `transform_fn`. Let's test the following example. | dummy_data = [random.random() for _ in range(784)] | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
When the `dummy_data` is parsed in `transform_fn`, it does not have an `inputs` field, so `transform_fn` will crush. | # uncomment the following line to make inference on incorrectly formated input data
# res = predictor.predict(dummy_data) | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
Now, let's use real MNIST test to test the endpoint. We use helper functions defined in `code.utils` to download MNIST data set and normalize the input data. | import random
import boto3
import matplotlib.pyplot as plt
import os
import numpy as np
import gzip
import json
%matplotlib inline
# Donwload MNIST test set from a public bucket
with open("code/config.json", "rb") as f:
CONFIG = json.load(f)
fname = "t10k-images-idx3-ubyte.gz"
bucket = CONFIG["public_bucket"]
ke... | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
First, let us use the model to infer the samples one-by-one. This is the typical use casefor an online application. | # convert to float and normalize normalize the input
def normalize(x, axis):
eps = np.finfo(float).eps
mean = np.mean(x, axis=axis, keepdims=True)
# avoid division by zero
std = np.std(x, axis=axis, keepdims=True) + eps
return (x - mean) / std
samples = normalize(samples.astype(np.float32), axis... | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
Since in `transform_fn`, the parsed numpy array could have take on any value for its batchdimension, we can send the entire `samples` at once and let the model do a batch inference. | data = {"inputs": samples.tolist()}
res = predictor.predict(data)
print("Predictions: ", *map(int, res)) | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
Test and debug the entry point before deploymentWhen deploying a model to a SageMaker endpoint, it is a good practice to test the entry point. The following snippet shows you how you can test and debug the `model_fn` and `transform_fn` you implemented in the entry point for the inference image. | !pygmentize code/test_inference.py | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
The `test` function simulates how the inference container works. It pulls the modelartifact and loads the model into memory by calling `model_fn` and parse it with `model_dir`. When it receives a request, it calls `transform_fn` and parse it with the loaded model, the payload of the request, request content type and re... | import os
if not local_mode:
predictor.delete_endpoint()
else:
# detach the inference container from port 8080 (in local mode)
os.system("docker container ls | grep 8080 | awk '{print $1}' | xargs docker container rm -f") | _____no_output_____ | Apache-2.0 | frameworks/mxnet/get_started_mnist_deploy.ipynb | Amirosimani/amazon-sagemaker-examples |
Nova Tarefa - ExperimentoPreencha aqui com detalhes sobre a tarefa. **Em caso de dúvidas, consulte os [tutoriais da PlatIAgro](https://platiagro.github.io/tutorials/).** Declaração de parâmetros e hiperparâmetrosDeclare parâmetros com o botão na barra de ferramentas.O parâmetro `dataset` identifica os conjuntos de d... | dataset = "" #@param {type:"string"} | _____no_output_____ | Apache-2.0 | projects/config/Experiment.ipynb | Matheus158257/projects |
Acesso ao conjunto de dadosO conjunto de dados utilizado nesta etapa será o mesmo carregado através da plataforma.O tipo da variável retornada depende do arquivo de origem:- [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) para CSV e compressed CSV: .csv .csv.zip .cs... | import pandas as pd
data = pd.read_csv(f'/tmp/data/{dataset}')
data | _____no_output_____ | Apache-2.0 | projects/config/Experiment.ipynb | Matheus158257/projects |
Acesso aos metadados do conjunto de dadosUtiliza a função `stat_dataset` do [SDK da PlatIAgro](https://platiagro.github.io/sdk/) para carregar metadados.Por exemplo, arquivos CSV possuem `metadata['featuretypes']` para cada coluna no conjunto de dados (ex: categorical, numerical, or datetime). | from platiagro import stat_dataset
metadata = stat_dataset(name=dataset)
metadata | _____no_output_____ | Apache-2.0 | projects/config/Experiment.ipynb | Matheus158257/projects |
Conteúdo da tarefa | # adicione seu código aqui... | _____no_output_____ | Apache-2.0 | projects/config/Experiment.ipynb | Matheus158257/projects |
Salva alterações no conjunto de dadosO conjunto de dados será salvo (e sobrescrito com as respectivas mudanças) localmente, no container da experimentação, utilizando a função `pandas.DataFrame.to_csv`. | df.to_csv(f'/tmp/data/{dataset}', index=False) | _____no_output_____ | Apache-2.0 | projects/config/Experiment.ipynb | Matheus158257/projects |
Salva métricasUtiliza a função `save_metrics` do [SDK da PlatIAgro](https://platiagro.github.io/sdk/) para salvar métricas. Por exemplo: `accuracy`, `precision`, `r2_score`, `custom_score` etc. | from platiagro import save_metrics
save_metrics(accuracy=0.5, custom_score=1000) | _____no_output_____ | Apache-2.0 | projects/config/Experiment.ipynb | Matheus158257/projects |
Salva figurasUtiliza a função `save_figures` do [SDK da PlatIAgro](https://platiagro.github.io/sdk/) para salvar figuras do [matplotlib](https://matplotlib.org/3.2.1/gallery/index.html). | from platiagro import save_figures
save_figures(figure=matplotfig) | _____no_output_____ | Apache-2.0 | projects/config/Experiment.ipynb | Matheus158257/projects |
Salva modelo e outros artefatosUtiliza a função `save_model` do [SDK da PlatIAgro](https://platiagro.github.io/sdk/) para salvar modelos e outros artefatos.Essa função torna estes artefatos disponíveis para o notebook de implantação. | from platiagro import save_model
save_model(model=model, other_artifact={"key": "value"}) | _____no_output_____ | Apache-2.0 | projects/config/Experiment.ipynb | Matheus158257/projects |
12-Web-Scraping-and-Document-DatabasesEric Nordstrom Setup | # dependencies
from selenium import webdriver
from bs4 import BeautifulSoup as BS
import requests
import pandas as pd
# set up selenium driver
driver = webdriver.Firefox() | _____no_output_____ | ADSL | mission_to_mars.ipynb | eanord4/scraping-challenge |
NASA Mars News | # get html to parse
url = "https://mars.nasa.gov/news"
driver.get(url)
soup = BS(driver.page_source, "html.parser")
# parse html
item = soup.find('li', class_="slide")
date = item.find('div', class_="list_date").text
title_a = item.find('div', class_="content_title").a
title = title_a.text
href = title_a['href']
para ... | February 27, 2020
The MarCO Mission Comes to an End
The pair of briefcase-sized satellites made history when they sailed past Mars in 2019.
More: https://mars.nasa.gov/news/8408/the-marco-mission-comes-to-an-end/
| ADSL | mission_to_mars.ipynb | eanord4/scraping-challenge |
JPL Mars Space Images - Featured Image | # get html to parse
url = "https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars"
r = requests.get(url)
assert(r.status_code == 200)
soup = BS(r.text, "html.parser")
# parse html
button_a = soup.find('a', id="full_image")
featured_image_url = "https://www.jpl.nasa.gov" + button_a['data-fancybox-href']
title = bu... | Curiosity Self-Portrait at 'Big Sky' Drilling Site
This self-portrait of NASA's Curiosity Mars rover shows the vehicle at the 'Big Sky' site, where its drill collected the mission's fifth taste of Mount Sharp.
Image: https://www.jpl.nasa.gov/spaceimages/images/mediumsize/PIA19920_ip.jpg
| ADSL | mission_to_mars.ipynb | eanord4/scraping-challenge |
Mars Weather | # get html to parse
url = "https://twitter.com/marswxreport?lang=en"
r = requests.get(url)
assert(r.status_code == 200)
soup = BS(r.text, "html.parser")
# parse html
# for some reason this shows up as a `span` via the inspector, but something
# goes wrong via requests and even selenium. the 'p' tag below was found via... | InSight sol 448 (2020-02-29) low -94.1ºC (-137.3ºF) high -8.3ºC (17.0ºF)
winds from the SSW at 5.5 m/s (12.4 mph) gusting to 19.9 m/s (44.6 mph)
pressure at 6.30 hPa
| ADSL | mission_to_mars.ipynb | eanord4/scraping-challenge |
Mars Facts | #get html to parse
url = "http://space-facts.com/mars/"
r = requests.get(url)
assert(r.status_code == 200)
# parse html
# "HTML table string"? i think just a data frame makes sense?
tables = pd.read_html(r.text)
# display results
tables[0]
tables[1]
# assign variables
mars_facts = tables[0].rename(columns={0: "Proper... | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>Value</th>
</tr>
<tr>
<th>Property</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th>Equatorial Diameter:</th>
<td>6,792 km</td>
</tr>
<tr>
<th>Polar Diamet... | ADSL | mission_to_mars.ipynb | eanord4/scraping-challenge |
Mars Hemispheres | # get html to parse
url = "https://astrogeology.usgs.gov/search/results?q=hemisphere+enhanced&k1=target&v1=Mars"
driver.get(url)
# parse html on main page
imgs = {} # initially the urls of the images pages, then replaced with actual image urls
for a in driver.find_elements_by_tag_name('a'):
if a.get_attribute('cl... | _____no_output_____ | ADSL | mission_to_mars.ipynb | eanord4/scraping-challenge |
PyTorch 1.0 Distributed Trainer with Amazon AWS===============================================**Author**: `Nathan Inkawhich `_**Edited by**: `Teng Li `_ In this tutorial we will show how to setup, code, and run a PyTorch 1.0distributed trainer across two multi-gpu Amazon AWS nodes. We will startwith describing the AWS ... | import time
import sys
import torch
if __name__ == '__main__':
torch.multiprocessing.set_start_method('spawn')
import torch.nn as nn
import torch.nn.parallel
import torch.distributed as dist
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Helper Functions~~~~~~~~~~~~~~~~We must also define some helper functions and classes that will maketraining easier. The ``AverageMeter`` class tracks training statisticslike accuracy and iteration count. The ``accuracy`` function computesand returns the top-k accuracy of the model so we can track learningprogress. Bot... | class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Train Functions~~~~~~~~~~~~~~~To simplify the main loop, it is best to separate a training epoch stepinto a function called ``train``. This function trains the input modelfor one epoch of the *train\_loader*. The only distributed trainingartifact in this function is setting the`non\_blocking `__attributes of the data a... | def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Validation Function~~~~~~~~~~~~~~~~~~~To track generalization performance and simplify the main loop furtherwe can also extract the validation step into a function called``validate``. This function runs a full validation step of the inputmodel on the input validation dataloader and returns the top-1 accuracyof the mode... | def validate(val_loader, model, criterion):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
end = time.time()
for i, (input, target) in enumerate(val_loader):
... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Inputs~~~~~~With the helper functions out of the way, now we have reached theinteresting part. Here is where we will define the inputs for the run.Some of the inputs are standard model training inputs such as batch sizeand number of training epochs, and some are specific to our distributedtraining task. The required in... | print("Collect Inputs...")
# Batch Size for training and testing
batch_size = 32
# Number of additional worker processes for dataloading
workers = 2
# Number of epochs to train for
num_epochs = 2
# Starting Learning Rate
starting_lr = 0.1
# Number of distributed processes
world_size = 4
# Distributed backend type... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Initialize process group~~~~~~~~~~~~~~~~~~~~~~~~One of the most important parts of distributed training in PyTorch is toproperly setup the process group, which is the **first** step ininitializing the ``torch.distributed`` package. To do this, we will usethe ``torch.distributed.init_process_group`` function which takes... | print("Initialize Process Group...")
# Initialize Process Group
# v1 - init with url
dist.init_process_group(backend=dist_backend, init_method=dist_url, rank=int(sys.argv[1]), world_size=world_size)
# v2 - init with file
# dist.init_process_group(backend="nccl", init_method="file:///home/ubuntu/pt-distributed-tutorial/... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Initialize Model~~~~~~~~~~~~~~~~The next major step is to initialize the model to be trained. Here, wewill use a resnet18 model from ``torchvision.models`` but any model maybe used. First, we initialize the model and place it in GPU memory.Next, we make the model ``DistributedDataParallel``, which handles thedistributi... | print("Initialize Model...")
# Construct Model
model = models.resnet18(pretrained=False).cuda()
# Make model DistributedDataParallel
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=dp_device_ids, output_device=local_rank)
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyL... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Initialize Dataloaders~~~~~~~~~~~~~~~~~~~~~~The last step in preparation for the training is to specify whichdataset to use. Here we use the `STL-10dataset `__ from`torchvision.datasets.STL10 `__.The STL10 dataset is a 10 class dataset of 96x96px color images. For usewith our model, we resize the images to 224x224px in... | print("Initialize Dataloaders...")
# Define the transform for the data. Notice, we must resize to 224x224 with this dataset and model.
transform = transforms.Compose(
[transforms.Resize(224),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
# Initialize Datasets. STL10 will... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Training Loop~~~~~~~~~~~~~The last step is to define the training loop. We have already done mostof the work for setting up the distributed training so this is notdistributed training specific. The only detail is setting the currentepoch count in the ``DistributedSampler``, as the sampler shuffles thedata going to each... | best_prec1 = 0
for epoch in range(num_epochs):
# Set epoch count for DistributedSampler
train_sampler.set_epoch(epoch)
# Adjust learning rate according to schedule
adjust_learning_rate(starting_lr, optimizer, epoch)
# train for one epoch
print("\nBegin Training Epoch {}".format(epoch+1))
... | _____no_output_____ | BSD-3-Clause | docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb | leejh1230/PyTorch-tutorials-kr |
Figure 1.1: Three types of Iris flowers: Setosa, Versicolor and Virginica. Used with kind permission of Dennis Kramb and SIGNA | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.2: Illustration of the image classification problem. From https://cs231n.github.io/ . Used with kind permission of Andrej Karpathy | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.3: Visualization of the Iris data as a pairwise scatter plot. On the diagonal we plot the marginal distribution of each feature for each class. The off-diagonals contain scatterplots of all possible pairs of features. Figure(s) generated by [iris_plot.py](https://github.com/probml/pyprobml/blob/master/sc... | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.4: Example of a decision tree of depth 2 applied to the Iris data, using just the petal length and petal width features. Leaf nodes are color coded according to the predicted class. The number of training samples that pass from the root to a node is shown inside each box; we show how many values of each cl... | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.5: (a) Linear regression on some 1d data. (b) The vertical lines denote the residuals between the observed output value for each input (blue circle) and its predicted value (red cross). The goal of least squares regression is to pick a line that minimizes the sum of squared residuals. Figure(s) generated... | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.6: Linear and polynomial regression applied to 2d data. Vertical axis is temperature, horizontal axes are location within a room. Data was collected by some remote sensing motes at Intel's lab in Berkeley, CA (data courtesy of Romain Thibaux). (a) The fitted plane has the form $ f ( \bm x ) = w_0 + w_1 x_... | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.7: (a-c) Polynomials of degrees 2, 14 and 20 fit to 21 datapoints (the same data as in \cref fig:linreg ). (d) MSE vs degree. Figure(s) generated by [linreg_poly_vs_degree.py](https://github.com/probml/pyprobml/blob/master/scripts/linreg_poly_vs_degree.py) | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.8: (a) A scatterplot of the petal features from the iris dataset. (b) The result of unsupervised clustering using $K=3$. Figure(s) generated by [iris_kmeans.py](https://github.com/probml/pyprobml/blob/master/scripts/iris_kmeans.py) | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.9: (a) Scatterplot of iris data (first 3 features). Points are color coded by class. (b) We fit a 2d linear subspace to the 3d data using PCA. The class labels are ignored. Red dots are the original data, black dots are points generated from the model using $ \bm x = \mathbf W \bm z + \bm \mu $, wh... | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.10: Examples of some control problems. (a) Space Invaders Atari game. From https://gym.openai.com/envs/SpaceInvaders-v0/ . (b) Controlling a humanoid robot in the MuJuCo simulator so it walks as fast as possible without falling over. From https://gym.openai.com/envs/Humanoid-v2/ | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.11: The three types of machine learning visualized as layers of a chocolate cake. This figure (originally from https://bit.ly/2m65Vs1 ) was used in a talk by Yann LeCun at NIPS'16, and is used with his kind permission | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.12: (a) Visualization of the MNIST dataset. Each image is $28 \times 28$. There are 60k training examples and 10k test examples. We show the first 25 images from the training set. Figure(s) generated by [mnist_viz_tf.py](https://github.com/probml/pyprobml/blob/master/scripts/mnist_viz_tf.py) [emnist_viz_p... | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.13: (a) Visualization of the Fashion-MNIST dataset [XRV17] . The dataset has the same size as MNIST, but is harder to classify. There are 10 classes: T-shirt/top, Trouser, Pullover, Dress, Coat, Sandal, Shirt, Sneaker, Bag, Ankle-boot. We show the first 25 images from the training set. Figure(s) generated ... | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.14: (a) Sample images from the \bf ImageNet dataset [Rus+15] . This subset consists of 1.3M color training images, each of which is $256 \times 256$ pixels in size. There are 1000 possible labels, one per image, and the task is to minimize the top-5 error rate, i.e., to ensure the correct label is within ... | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Figure 1.15: Example of a term-document matrix, where raw counts have been replaced by their TF-IDF values (see \cref sec:tfidf ). Darker cells are larger values. From https://bit.ly/2kByLQI . Used with kind permission of Christoph Carl Kling | #@title Click me to run setup { display-mode: "form" }
try:
if PYPROBML_SETUP_ALREADY_RUN:
print('skipping setup')
except:
PYPROBML_SETUP_ALREADY_RUN = True
print('running setup...')
!git clone --depth 1 https://github.com/probml/pyprobml /pyprobml &> /dev/null
%cd -q /pyprobml/scripts
%reload_ext aut... | _____no_output_____ | MIT | pml1/figure_notebooks/chapter1_introduction_figures.ipynb | brickbitbot/pml-book |
Introduction | import jupyter_bifrost
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
cols = ["foo","y", "bar", "baz", "something", "else"]
dist = np.random.uniform(0,1, size=(1000,len(cols)))
scatter_df = pd.DataFrame(dist, columns=cols)
bar_df = pd.DataFrame([
["John", "Developer", 1],
["Jay",... | _____no_output_____ | BSD-3-Clause | examples/introduction.ipynb | jahn96/Jupyter-Bifrost |
Using -pipe- notation **Connecting unit operations can be simplified through -pipe- notation. As an example, here we create a process with multiple units and connect them as a demonstration.** With -pipe- notation you can get stream outputs and set stream inputs in the following format: | # U1-n -> U1.outs[n]
# U1-[0, 1] -> [U1.outs[i] for i in [0, 1]]
# s1-U1 -> U1.ins[:] = [s1]
# s1-n-U1 -> U1.ins[n] = s1
# [s1, s2]-U1 -> U1.ins[:] = [s1, s2]
# U1-n1-n2-U2 -> U2.ins[n2] = U1.outs[n1] | _____no_output_____ | MIT | docs/tutorial/Using -pipe- notation.ipynb | Ecoent/biosteam |
As an example, create 2 feeds, 2 Mixers and 2 Splitters: | import biosteam as bst
import thermosteam as tmo
from biosteam.units import Mixer, Splitter
# Set property pacakge
chemicals = tmo.Chemicals(['Water'])
tmo.settings.set_thermo(chemicals)
# Set feed stream and units
feed1 = tmo.Stream('feed1')
M1 = Mixer('M1', outs='s1')
S1 = Splitter('S1', outs=('s2', 'product1'), sp... | _____no_output_____ | MIT | docs/tutorial/Using -pipe- notation.ipynb | Ecoent/biosteam |
Now connect streams linearly along the units, and create a loop between S2 and M1: | # In -pipe- notation:
(feed1, S2-0)-M1-S1
(feed2, S1-0)-M2-S2
# Without -pipe- notation:
# M1.ins[:] = (feed1, S2.outs[0])
# S1.ins[:] = M1.outs
# M2.ins[:] = (feed2, S1.outs[0])
# S2.ins[:] = M2.outs
bst.find.diagram(format='png') | _____no_output_____ | MIT | docs/tutorial/Using -pipe- notation.ipynb | Ecoent/biosteam |
Python Indention | if 5<2:
print("Five is less than two")
else:
print("Five is greater than two") | Five is greater than two
| Apache-2.0 | Intro_to_Python.ipynb | Daenarics/Elective-1-3 |
Python Comments | # This is a program that displays Hello, World
print("Hello, World")
print('Welcome to Python Programming') | Hello, World
Welcome to Python Programming
| Apache-2.0 | Intro_to_Python.ipynb | Daenarics/Elective-1-3 |
Common Workflow Language with BioExcel Building Blocks Based on the Protein MD Setup tutorial using BioExcel Building Blocks (biobb)***This tutorial aims to illustrate the process of **building up a CWL workflow** using the **BioExcel Building Blocks library (biobb)**. The tutorial is based on the **Protein Gromacs MD... | hints:
DockerRequirement:
dockerPull: quay.io/biocontainers/biobb_io:latest | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
* **namespaces and schemas**: Input and output **metadata** may be represented within a tool or workflow. Such **metadata** must use a **namespace prefix** listed in the **$namespaces and $schemas sections** of the document. All **BioExcel building blocks CWL specifications** use the **EDAM ontology** (http://edamontol... | $namespaces:
edam: http://edamontology.org/
$schemas:
- http://edamontology.org/EDAM_1.22.owl | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
* **inputs**: The **inputs section** of a **tool** contains a list of input parameters that **control how to run the tool**. Each parameter has an **id** for the name of parameter, and **type** describing what types of values are valid for that parameter. Available primitive types are *string, int, long, float, double,... | inputs:
output_pdb_path:
type: string
inputBinding:
position: 1
prefix: --output_pdb_path
default: 'downloaded_structure.pdb'
config:
type: string?
inputBinding:
position: 2
prefix: --config
default: '{"pdb_code" : "1aki"}' | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
* **outputs**: The **outputs section** of a **tool** contains a list of output parameters that should be returned after running the **tool**. Similarly to the inputs section, each parameter has an **id** for the name of parameter, and **type** describing what types of values are valid for that parameter. The **outputBi... | outputs:
output_pdb_file:
type: File
format: edam:format_1476
outputBinding:
glob: $(inputs.output_pdb_path) | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
For more information on CWL tools description, please refer to the [CWL User Guide](https://www.commonwl.org/user_guide/index.html) or the [CWL specifications](https://www.commonwl.org/v1.1/).*** Complete Pdb Building Block CWL description:Example of a **BioExcel building block CWL description** (pdb from biobb_io pack... | # Example of a BioExcel building block CWL description (pdb from biobb_io package)
#!/usr/bin/env cwl-runner
cwlVersion: v1.0
class: CommandLineTool
baseCommand: pdb
hints:
DockerRequirement:
dockerPull: quay.io/biocontainers/biobb_io:latest
inputs:
output_pdb_path:
type: string
inputBindi... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
*** BioExcel building blocks WORKFLOWS CWL DescriptionsNow that we have seen the **BioExcel building blocks CWL descriptions**, we can use them to build our first **biomolecular workflow** as a demonstrator. All **CWL workflows** are divided in **two files**: the **CWL description** and the **YAML** or **JSON** files c... | # !/usr/bin/env cwl-runner
cwlVersion: v1.0
class: Workflow
label: Example CWL Header
doc: |
An example of how to create a CWl header. We have specified the version
of CWL that we are using; the class, which is a 'workflow'. The label
field should provide a short title or description of the workflow and
the d... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Inputs:The **inputs section** describes the inputs for **each of the steps** of the workflow. The **BioExcel building blocks (biobb)** have three types of **input parameters**: **input**, **output**, and **properties**. The **properties** parameter, which contains all the input parameters that are neither **input** no... | # CWL workflow inputs section example
inputs:
step1_output_name: string
step1_properties: string | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Outputs:The **outputs:** section describes the set of **final outputs** from the **workflow**. These outputs can be a collection of outputs from **different steps of the workflow**. Each output is a `key: value` pair. The `key` should be a unique identifier, and the value should be a dictionary (consisting of `key: va... | outputs:
pdb: #unique identifier
label: Protein structure
doc: |
Step 1 of the workflow, download a 'protein structure' from the
'PDB database'. The *pdb* 'output' is a 'file' containing the
'protein structure' in 'PDB format', which is connected to the
output parameter *output_pdb_f... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Steps:The **steps section** describes the actual steps of the workflow. Steps are **connected** one to the other through the **input parameters**.**Workflow steps** are not necessarily run in the order they are listed, instead **the order is determined by the dependencies between steps**. In addition, workflow steps w... | # CWL workflow steps section example
step1_pdb:
label: Fetch PDB Structure
doc: |
Download a protein structure from the PDB database
run: biobb/biobb_adapters/cwl/biobb_io/mmb_api/pdb.cwl
in:
output_pdb_path: step1_pdb_name
config: step1_pdb_config
out: [output_pdb_file]
step2... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Input of a run:As previously stated, all **CWL workflows** are divided in **two files**: the **CWL description** and the **YAML** or **JSON** files containing **all workflow inputs**. In this example, we are going to produce a **YAML** formatted object in a separate file describing the **inputs of our run**.**Example... | step1_output_name: 'tutorial_1aki.pdb'
step1_properties: '{"pdb_code" : "1aki"}' | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Complete workflow:Example of a short **CWL workflow** with **BioExcel building blocks**, which retrieves a **PDB file** for the **Lysozyme protein structure** from the RCSB PDB database (**step1: pdb.cwl**), and fixes the possible problems in the structure, adding **missing side chain atoms** if needed (**step2: fix_s... | # !/usr/bin/env cwl-runner
cwlVersion: v1.0
class: Workflow
label: Example of a short CWL workflow with BioExcel building blocks
doc: |
Example of a short 'CWL workflow' with 'BioExcel building blocks', which
retrieves a 'PDB file' for the 'Lysozyme protein structure' from the RCSB PDB
database ('step1: pdb.cwl'... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Running the CWL workflow:The final step of the process is **running the workflow described in CWL**. For that, the description presented in the previous cell should be written to a file (e.g. BioExcel-CWL-firstWorkflow.cwl), the **YAML** input should be written to a separate file (e.g. BioExcel-CWL-firstWorkflow-job.y... | # Run CWL workflow with CWL tool description reference implementation (cwltool).
cwltool BioExcel-CWL-firstWorkflow.cwl BioExcel-CWL-firstWorkflow-job.yml | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Cwltool workflow outputThe **execution of the workflow** will write information to the standard output such as the **step being performed**, the **way it is run** (command line, docker container, etc.), **inputs and outputs** used, and **state of each step** (success, failed). The next cell contains a **real output** ... | Resolved 'BioExcel-CWL-firstWorkflow.cwl' to 'file:///PATH/biobb_wf_md_setup/cwl/BioExcel-CWL-firstWorkflow.cwl'
[workflow BioExcel-CWL-firstWorkflow.cwl] start
[step step1_pdb] start
[job step1_pdb] /private/tmp/docker_tmp1g8y0wu0$ docker \
run \
-i \
--volume=/private/tmp/docker_tmp1g8y0wu0:/private/var/s... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
*** Protein MD-Setup CWL workflow with BioExcel building blocks The last step of this **tutorial** illustrates the building of a **complex CWL workflow**. The example used is the **Protein Gromacs MD Setup** [Jupyter Notebook tutorial](https://github.com/bioexcel/biobb_wf_md_setup). It is strongly recommended to take a... | step1_pdb:
label: Fetch PDB Structure
doc: |
Download a protein structure from the PDB database
run: biobb/biobb_adapters/cwl/biobb_io/mmb_api/pdb.cwl
in:
output_pdb_path: step1_pdb_name
config: step1_pdb_config
out: [output_pdb_file]
step2_fixsidechain:
label: Fix Protein ... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Inputs:All inputs for the **BioExcel building blocks** are defined as *strings*. Not all the steps in this particular example need **external inputs**, some of them just works using as input/s an output (or outputs) from **previous steps** (e.g. step2_fixsidechain). For the steps that need input, all of them will rece... | inputs:
step1_pdb_name: string
step1_pdb_config: string
step4_editconf_config: string
step6_gppion_config: string
step7_genion_config: string
step8_gppmin_config: string
step10_energy_min_config: string
step10_energy_min_name: string
step11_gppnvt_config: string
step13_energy_nvt_config: string
st... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Outputs:The **outputs section** contains the set of **final outputs** from the **workflow**. In this case, **outputs** from **different steps** of the **workflow** are considered **final outputs**:* **Trajectories**: * **trr**: Raw trajectory from the *free* simulation step. * **trr_imaged_dry**: Post-processed... | outputs:
trr:
label: Trajectories - Raw trajectory
doc: |
Raw trajectory from the free simulation step
type: File
outputSource: step18_mdrun_md/output_trr_file
trr_imaged_dry:
label: Trajectories - Post-processed trajectory
doc: |
Post-processed trajectory, dehydrated, image... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Complete workflow:The complete **CWL described workflow** to run a **Molecular Dynamics Setup** on a protein structure can be found in the next cell. The **representation of the workflow** using the **CWL Viewer** web service can be found here: XXXXXX. The **full workflow** is a combination of the **inputs**, **output... | # Protein MD-Setup CWL workflow with BioExcel building blocks
# https://github.com/bioexcel/biobb_wf_md_setup
#!/usr/bin/env cwl-runner
cwlVersion: v1.0
class: Workflow
inputs:
step1_pdb_name: string
step1_pdb_config: string
step4_editconf_config: string
step6_gppion_config: string
step7_genion_config: stri... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Input of the run:As previously stated, all **CWL workflows** are divided in **two files**: the **CWL description** and the **YAML** or **JSON** files containing **all workflow inputs**. The following cell presents the **YAML** file describing the **inputs of the run** for the **Protein Gromacs MD Setup** workflow.All... | # Protein MD-Setup CWL workflow with BioExcel building blocks - Input YAML configuration file
# https://github.com/bioexcel/biobb_wf_md_setup
step1_pdb_name: 'tutorial.pdb'
step1_pdb_config: '{"pdb_code" : "1aki"}'
step4_editconf_config: '{"box_type": "cubic","distance_to_molecule": 1.0}'
step6_gppion_config: '{"mdp":... | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Running the CWL workflow:The final step of the process is **running the workflow described in CWL**. For that, the complete **workflow description** should be written to a file (e.g. BioExcel-CWL-MDSetup.cwl), the **YAML** input should be written to a separate file (e.g. BioExcel-CWL-MDSetup-job.yml) and finally both ... | # Run CWL workflow with CWL tool description reference implementation (cwltool).
cwltool BioExcel-CWL-MDSetup.cwl BioExcel-CWL-MDSetup-job.yml | _____no_output_____ | Apache-2.0 | biobb_wf_cwl_tutorial/notebooks/biobb_CWL_tutorial.ipynb | longr/biobb_wf_cwl_tutorial |
Copy Task Plots | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from glob import glob
import json
import os
import sys
sys.path.append(os.path.abspath(os.getcwd() + "./../"))
%matplotlib inline | _____no_output_____ | BSD-3-Clause | notebooks/copy-task-plots.ipynb | arthurlemon/NTM |
Load training historyTo generate the models and training history used in this notebook, run the following commands:```mkdir ./notebooks/copy./train.py --seed 1 --task copy --checkpoint-interval 500 --checkpoint-path ./notebooks/copy./train.py --seed 10 --task copy --checkpoint-interval 500 --checkpoint-path ./notebook... | batch_num = 40000
files = glob("./copy/*-{}.json".format(batch_num))
files
# Read the metrics from the .json files
history = [json.loads(open(fname, "rt").read()) for fname in files]
training = np.array([(x['cost'], x['loss'], x['seq_lengths']) for x in history])
print("Training history (seed x metric x sequence) =", t... | _____no_output_____ | BSD-3-Clause | notebooks/copy-task-plots.ipynb | arthurlemon/NTM |
Evaluate | import torch
from IPython.display import Image as IPythonImage
from PIL import Image, ImageDraw, ImageFont
import io
from tasks.copytask import dataloader
from train import evaluate
from tasks.copytask import CopyTaskModelTraining
model = CopyTaskModelTraining()
model.net.load_state_dict(torch.load("./copy/copy-task-10... | _____no_output_____ | BSD-3-Clause | notebooks/copy-task-plots.ipynb | arthurlemon/NTM |
Create an animated GIFLets see how the prediction looks like in each checkpoint that we saved. | seq_len = 80
_, x, y = next(iter(dataloader(1, 1, 8, seq_len, seq_len)))
frames = []
font = ImageFont.truetype("./fonts/PT_Sans-Web-Regular.ttf", 13)
for batch_num in range(500, 10500, 500):
model = CopyTaskModelTraining()
model.net.load_state_dict(torch.load("./copy/copy-task-10-batch-{}.model".format(batch_n... | _____no_output_____ | BSD-3-Clause | notebooks/copy-task-plots.ipynb | arthurlemon/NTM |
Imports & Installations | !pip install pyforest
!pip install plotnine
!pip install transformers
!pip install psycopg2-binary
!pip uninstall -y tensorflow-datasets
!pip install lit_nlp tfds-nightly transformers==4.1.1
# Automatic library importer (doesn't quite import everything yet)
from pyforest import *
# Expands Dataframe to view entire pan... | _____no_output_____ | MIT | notebooks/labs38_notebooks/FrankenBERT_Training.ipynb | n8mcdunna/labs31_gp2 |
Reading in our Tweets | def get_df(db_url) -> pd.DataFrame:
'''
Connects to our Blue Witness Data Labeler and retrieves manually labelled text before converting them all into a pandas dataframe.
Parameters
----------
db_url: psycopg2 database
Returns
-------
df: pandas datafarme
Contains thou... | _____no_output_____ | MIT | notebooks/labs38_notebooks/FrankenBERT_Training.ipynb | n8mcdunna/labs31_gp2 |
Visualizations | %matplotlib inline
(ggplot(force_ranks) # defining what dataframe to use
+ aes(x='labels') # defining what variable/column to use
+ geom_bar(size=20) # defining the type of plot to use and its size
+ labs(title='Number of Tweets Reporting Police Violence per Force Rank', x='Force Rank', y='Nu... | _____no_output_____ | MIT | notebooks/labs38_notebooks/FrankenBERT_Training.ipynb | n8mcdunna/labs31_gp2 |
Preparing Data for BERTSplitting dataframe into training and testing sets before converting to parquet for later reference/resource. | def parquet_and_split():
'''
Splits our data into a format amicable to NLP modeling.
Saves our original dataframe as well as the two split dataframes into parquet files for later reference/use.
-----
Parameters
------
None
Returns
-------
df: pandas dataframes
Contains two split dataframes r... | _____no_output_____ | MIT | notebooks/labs38_notebooks/FrankenBERT_Training.ipynb | n8mcdunna/labs31_gp2 |
BERT Training our NLP Multi-Class Classification Model | def bert_trainer(df, output_dir: str, epochs: int):
start = time()
max_len = 280
if torch.cuda.is_available():
print("CUDA Active")
device = torch.device("cuda")
else:
print("CPU Active")
device = torch.device("cpu")
sentences = df["tweets"].values
labels = df["la... | CUDA Active
| MIT | notebooks/labs38_notebooks/FrankenBERT_Training.ipynb | n8mcdunna/labs31_gp2 |
Making Predictions | class FrankenBert:
"""
Implements BertForSequenceClassification and BertTokenizer
for binary classification from a saved model
"""
def __init__(self, path: str):
"""
If there's a GPU available, tell PyTorch to use the GPU.
Loads model and tokenizer from saved model directory... | _____no_output_____ | MIT | notebooks/labs38_notebooks/FrankenBERT_Training.ipynb | n8mcdunna/labs31_gp2 |
Saving Trained Model | from google.colab import drive
drive.mount('/content/gdrive')
#path that contains folder you want to copy
%cd /content/gdrive/MyDrive/ColabNotebooks/Labs/saved_model
# copy local folder to folder on Google Drive
%cp -av /content/saved_model saved_model | /content/gdrive/MyDrive/ColabNotebooks/Labs/saved_model
'/content/saved_model' -> 'saved_model'
'/content/saved_model/.ipynb_checkpoints' -> 'saved_model/.ipynb_checkpoints'
'/content/saved_model/config.json' -> 'saved_model/config.json'
'/content/saved_model/pytorch_model.bin' -> 'saved_model/pytorch_model.bin'
'/cont... | MIT | notebooks/labs38_notebooks/FrankenBERT_Training.ipynb | n8mcdunna/labs31_gp2 |
HW8: Topic Modeling | ## =======================================================
## IMPORTING
## =======================================================
import os
def get_data_from_files(path):
directory = os.listdir(path)
results = []
for file in directory:
f=open(path+file, encoding = "ISO-8859-1")
results.app... | _____no_output_____ | MIT | assets/all_html/2019_11_25_HW8_clean.ipynb | dskw1/dskw1.github.io |
Image Tools> Tools for images. | # export
class ImageOrientation(Enum):
"""
Image EXIF orientations.
"""
TOP = 1
TOP_FLIPPED = 2
BOTTOM = 3
BOTTOM_FLIPPED = 4
RIGHT_FLIPPED = 5
RIGHT = 6
LEFT_FLIPPED = 7
LEFT = 8
def __str__(self):
return str(self.value)
# export
def get_image_scale(image_size, ... | Converted annotation-core.ipynb.
Converted annotation-folder_category_adapter.ipynb.
Converted annotation-multi_category_adapter.ipynb.
Converted annotation-via_adapter.ipynb.
Converted annotation-yolo_adapter.ipynb.
Converted annotation_converter.ipynb.
Converted annotation_viewer.ipynb.
Converted category_tools.ipynb... | Apache-2.0 | image-tools.ipynb | AI-Force/aiforce |
pandas字符串操作很明显除了数值型，我们处理的数据还有很多字符类型的，而这部分数据显然也非常重要，因此这个部分我们提一提pandas的字符串处理。 | %matplotlib inline
%config ZMQInteractiveShell.ast_node_interactivity='all'
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
pd.set_option('display.mpl_style', 'default')
plt.rcParams['figure.figsize'] = (15, 3)
plt.rcParams['font.family'] = 'sans-serif' | /opt/anaconda3/envs/python27/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2881: FutureWarning:
mpl_style had been deprecated and will be removed in a future version.
Use `matplotlib.pyplot.style.use` instead.
exec(code_obj, self.user_global_ns, self.user_ns)
| Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
前面看到pandas在处理数值型的时候，各种如鱼得水，偷偷告诉你，pandas处理字符串也相当生猛。咱们来读一份天气数据。 | weather_2012 = pd.read_csv('./data/weather_2012.csv', parse_dates=True, index_col='Date/Time')
weather_2012[:5] | _____no_output_____ | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
5.1字符串操作 从上面的数据里面可以看到，有 'Weather' 这一列。我们这里假定包含 "Snow" 的才是下雪天。pandas的str类型提供了一系列方便的函数，比如这里的contains，更多的例子可以查看 [这里](http://pandas.pydata.org/pandas-docs/stable/basics.htmlvectorized-string-methods)。 | weather_description = weather_2012['Weather']
is_snowing = weather_description.str.contains('Snow') | _____no_output_____ | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
你看我们contains返回的其实是布尔型的判定结果的dataframe。 | # 返回bool型内容的dataframe
is_snowing[:5] | _____no_output_____ | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
你以为懒癌晚期的我会一个个去看吗！！图样图森破！！我一个函数就给你画出来了！！！ | # 就是屌！！！
is_snowing.plot() | _____no_output_____ | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
6.2 平均气温 如果我们想知道每个月的温度值中位数，有一个很有用的函数可以调用哈，叫 `resample()` | weather_2012['Temp (C)'].resample('M', how=np.median).plot(kind='bar') | /opt/anaconda3/envs/python27/lib/python2.7/site-packages/ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...)..apply(<func>)
"""Entry point for launching an IPython kernel.
| Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
符合预期对吧，7月和8月是温度最高的 你要知道，布尔型的 `True` 和 `False`其实是不便于运算的，当然，其实他们就是0和1了，所以我们转成float型去做做运算可好？ | is_snowing.astype(float)[:10] | _____no_output_____ | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
然后我们很聪明地用 `resample` 去找到每个月下雪的比例状况（为嘛感觉在做很无聊的事情，其实哪个月下雪多我们知道的对么...） | is_snowing.astype(float).resample('M', how=np.mean)
is_snowing.astype(float).resample('M', how=np.mean).plot(kind='bar') | /opt/anaconda3/envs/python27/lib/python2.7/site-packages/ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...)..apply(<func>)
"""Entry point for launching an IPython kernel.
| Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
So，你也看到了，加拿大的12月是下雪最多的月份。然后你还能观察到一些其他的端倪，比如你会发现，11月开始突然下雪，接着就雪期漫漫，虽然下雪的概率逐步减小，但是可能要到4月或者5月才会停止。 5.3 画一下温度和雪期 我们把温度和下雪概率放到一起，组成dataframe的2列，然后画个图 | temperature = weather_2012['Temp (C)'].resample('M', how=np.median)
is_snowing = weather_2012['Weather'].str.contains('Snow')
snowiness = is_snowing.astype(float).resample('M', how=np.mean)
# 给列取个名字
temperature.name = "Temperature"
snowiness.name = "Snowiness" | /opt/anaconda3/envs/python27/lib/python2.7/site-packages/ipykernel_launcher.py:1: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...)..apply(<func>)
"""Entry point for launching an IPython kernel.
/opt/anaconda3/envs/python27/lib/python2.7/site-packages/ipykernel_launcher.py:3: FutureWarn... | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
我们用concat完成字符串的拼接 用 `concat` 把这两列拼接到一列中，组成一个新的dataframe | stats = pd.concat([temperature, snowiness], axis=1)
stats
stats.plot(kind='bar') | _____no_output_____ | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
你发现，什么鬼！！！紫色的下雪概率呢！！！是的亲，你这2个维度的幅度是不一样的，所以要分开画哦。 | stats.plot(kind='bar', subplots=True, figsize=(15, 10)) | _____no_output_____ | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
总结 | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Make the graphs a bit prettier, and bigger
pd.set_option('display.mpl_style', 'default')
# matplotlib.style.use('ggplot')
plt.rcParams['figure.figsize'] = (15, 5)
plt.rcParams['font.family'] = 'sans-serif'
# This is necessary... | /opt/anaconda3/envs/python27/lib/python2.7/site-packages/ipykernel_launcher.py:22: FutureWarning: how in .resample() is deprecated
the new syntax is .resample(...)..apply(<func>)
/opt/anaconda3/envs/python27/lib/python2.7/site-packages/ipykernel_launcher.py:24: FutureWarning: how in .resample() is deprecated
the new sy... | Apache-2.0 | dataScience/02DataMining/02_Data-Computation-Analysis-Visualization/2-5_Pandas_String_Operations.ipynb | yangpanyang/DemoPractice |
Machine Learning - AA2AR **By Jakke Neiro & Andrei Roibu** 1. Importing All Required Dependencies This script imports all the required dependencies for running the different functions and the codes. Also, by using the _run_ command, the various notebooks are imprted into the main notebook. | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import interp
import glob, os
from sklearn.metrics import accuracy_score, roc_curve, roc_auc_score, auc, roc_auc_score, confusion_matrix, classification_report, log_loss
from sklearn.model_selection import train_test_split
from sklearn.... | _____no_output_____ | BSD-3-Clause | MAIN-AA2AR.ipynb | SABS-R3-projects/JA-ML |
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