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 |
|---|---|---|---|---|---|
Override these arguments as needed: | address = args.address
smoke_test = args.smoke_test
num_actors = args.num_actors
cpus_per_actor = args.cpus_per_actor
num_actors_inference = args.num_actors_inference
cpus_per_actor_inference = args.cpus_per_actor_inference | _____no_output_____ | Apache-2.0 | doc/source/ray-core/examples/modin_xgboost/modin_xgboost.ipynb | richardsliu/ray |
Connecting to the Ray clusterNow, let's connect our Python script to this newly deployed Ray cluster! | if not ray.is_initialized():
ray.init(address=address) | _____no_output_____ | Apache-2.0 | doc/source/ray-core/examples/modin_xgboost/modin_xgboost.ipynb | richardsliu/ray |
Data PreparationWe will use the [HIGGS dataset from the UCI Machine Learning datasetrepository](https://archive.ics.uci.edu/ml/datasets/HIGGS). The HIGGSdataset consists of 11,000,000 samples and 28 attributes, which is largeenough size to show the benefits of distributed computation. | LABEL_COLUMN = "label"
if smoke_test:
# Test dataset with only 10,000 records.
FILE_URL = "https://ray-ci-higgs.s3.us-west-2.amazonaws.com/simpleHIGGS" ".csv"
else:
# Full dataset. This may take a couple of minutes to load.
FILE_URL = (
"https://archive.ics.uci.edu/ml/machine-learning-databases"... | _____no_output_____ | Apache-2.0 | doc/source/ray-core/examples/modin_xgboost/modin_xgboost.ipynb | richardsliu/ray |
Split data into training and validation. | df_train, df_validation = train_test_split(df)
print(df_train, df_validation) | _____no_output_____ | Apache-2.0 | doc/source/ray-core/examples/modin_xgboost/modin_xgboost.ipynb | richardsliu/ray |
Distributed TrainingThe ``train_xgboost`` function contains all the logic necessary fortraining using XGBoost-Ray.Distributed training can not only speed up the process, but also allow youto use datasets that are too large to fit in memory of a single node. Withdistributed training, the dataset is sharded across diffe... | def train_xgboost(config, train_df, test_df, target_column, ray_params):
train_set = RayDMatrix(train_df, target_column)
test_set = RayDMatrix(test_df, target_column)
evals_result = {}
train_start_time = time.time()
# Train the classifier
bst = train(
params=config,
dtrain=tra... | _____no_output_____ | Apache-2.0 | doc/source/ray-core/examples/modin_xgboost/modin_xgboost.ipynb | richardsliu/ray |
We can now pass our Modin dataframes and run the function. We will use``RayParams`` to specify that the number of actors and CPUs to train with. | # standard XGBoost config for classification
config = {
"tree_method": "approx",
"objective": "binary:logistic",
"eval_metric": ["logloss", "error"],
}
bst, evals_result = train_xgboost(
config,
df_train,
df_validation,
LABEL_COLUMN,
RayParams(cpus_per_actor=cpus_per_actor, num_actors=n... | _____no_output_____ | Apache-2.0 | doc/source/ray-core/examples/modin_xgboost/modin_xgboost.ipynb | richardsliu/ray |
PredictionWith the model trained, we can now predict on unseen data. For thepurposes of this example, we will use the same dataset for prediction asfor training.Since prediction is naively parallelizable, distributing it over multipleactors can measurably reduce the amount of time needed. | inference_df = RayDMatrix(df, ignore=[LABEL_COLUMN, "partition"])
results = predict(
bst,
inference_df,
ray_params=RayParams(
cpus_per_actor=cpus_per_actor_inference, num_actors=num_actors_inference
),
)
print(results) | _____no_output_____ | Apache-2.0 | doc/source/ray-core/examples/modin_xgboost/modin_xgboost.ipynb | richardsliu/ray |
Задача 1Проектирование функций для построения обучающих моделей по данным. В данной задача вам нужно разработать прототипы функций(объявление функций без реализаций) для задачи анализа данных из машинного обучения, должны быть учтены следующие шаги:* Загрузка данных из внешних источников* Обработка не заданных значени... | def loading_dataframe(path,source="file",type='csv'):
"""
Функция загружает файл из внешних источников.
Параметры:
path — путь, из которого загружается документ,
source — тип документа (file (по умолчанию), http, https, ftp),
type — расширение документа (txt,csv,xls).
Результат:
load_dat... | _____no_output_____ | Apache-2.0 | module_001_python/lesson_004_function/student_tasks/HomeWork.ipynb | VanyaTihonov/ML |
Задача 2Задача повышенной сложности. Реализовать вывод треугольника паскаля, через функцию. Пример треугольника:Глубина 10 по умолчанию | def print_pascal(primary,deep=10):
for i in range(1,deep+1):
print(pascal(primary,i))
def pascal(primary,deep):
if deep == 1:
new_list = [primary]
elif deep == 2:
new_list = []
for i in range (deep):
new_list.extend(pascal(primary,1))
else:
new_list = ... | [1]
[1, 1]
[1, 2, 1]
[1, 3, 3, 1]
[1, 4, 6, 4, 1]
[1, 5, 10, 10, 5, 1]
[1, 6, 15, 20, 15, 6, 1]
[1, 7, 21, 35, 35, 21, 7, 1]
[1, 8, 28, 56, 70, 56, 28, 8, 1]
[1, 9, 36, 84, 126, 126, 84, 36, 9, 1]
| Apache-2.0 | module_001_python/lesson_004_function/student_tasks/HomeWork.ipynb | VanyaTihonov/ML |
IntroductionThis notebook describe how you can use VietOcr to train OCR model | ! pip install --quiet vietocr | [?25l
[K |█████▌ | 10kB 26.4MB/s eta 0:00:01
[K |███████████ | 20kB 1.7MB/s eta 0:00:01
[K |████████████████▋ | 30kB 2.3MB/s eta 0:00:01
[K |██████████████████████▏ | 40kB 2.5MB/s eta 0:00:01
[K |███████████████████████████▋ ... | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Inference | import matplotlib.pyplot as plt
from PIL import Image
from vietocr.tool.predictor import Predictor
from vietocr.tool.config import Cfg
config = Cfg.load_config_from_name('vgg_transformer') | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Change weights to your weights or using default weights from our pretrained model. Path can be url or local file | # config['weights'] = './weights/transformerocr.pth'
config['weights'] = 'https://drive.google.com/uc?id=13327Y1tz1ohsm5YZMyXVMPIOjoOA0OaA'
config['cnn']['pretrained']=False
config['device'] = 'cuda:0'
config['predictor']['beamsearch']=False
detector = Predictor(config)
! gdown --id 1uMVd6EBjY4Q0G2IkU5iMOQ34X0bysm0b
! ... | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Download sample dataset | ! gdown https://drive.google.com/uc?id=19QU4VnKtgm3gf0Uw_N2QKSquW1SQ5JiE
! unzip -qq -o ./data_line.zip | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Train model 1. Load your config2. Train model using your dataset above Load the default config, we adopt VGG for image feature extraction | from vietocr.tool.config import Cfg
from vietocr.model.trainer import Trainer | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Change the config * *data_root*: the folder save your all images* *train_annotation*: path to train annotation* *valid_annotation*: path to valid annotation* *print_every*: show train loss at every n steps* *valid_every*: show validation loss at every n steps* *iters*: number of iteration to train your model* *export*... | config = Cfg.load_config_from_name('vgg_transformer')
#config['vocab'] = 'aAàÀảẢãÃáÁạẠăĂằẰẳẲẵẴắẮặẶâÂầẦẩẨẫẪấẤậẬbBcCdDđĐeEèÈẻẺẽẼéÉẹẸêÊềỀểỂễỄếẾệỆfFgGhHiIìÌỉỈĩĨíÍịỊjJkKlLmMnNoOòÒỏỎõÕóÓọỌôÔồỒổỔỗỖốỐộỘơƠờỜởỞỡỠớỚợỢpPqQrRsStTuUùÙủỦũŨúÚụỤưƯừỪửỬữỮứỨựỰvVwWxXyYỳỲỷỶỹỸýÝỵỴzZ0123456789!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~ '
dataset_para... | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
you can change any of these params in this full list below | config | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
You should train model from our pretrained | trainer = Trainer(config, pretrained=True) | Downloading: "https://download.pytorch.org/models/vgg19_bn-c79401a0.pth" to /root/.cache/torch/hub/checkpoints/vgg19_bn-c79401a0.pth
| Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Save model configuration for inference, load_config_from_file | trainer.config.save('config.yml') | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Visualize your dataset to check data augmentation is appropriate | trainer.visualize_dataset() | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Train now | trainer.train() | iter: 000200 - train loss: 1.657 - lr: 1.91e-05 - load time: 1.08 - gpu time: 158.33
iter: 000400 - train loss: 1.429 - lr: 3.95e-05 - load time: 0.76 - gpu time: 158.76
iter: 000600 - train loss: 1.331 - lr: 7.14e-05 - load time: 0.73 - gpu time: 158.38
iter: 000800 - train loss: 1.252 - lr: 1.12e-04 - load time: 1.29... | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Visualize prediction from our trained model | trainer.visualize_prediction() | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Compute full seq accuracy for full valid dataset | trainer.precision() | _____no_output_____ | Apache-2.0 | vietocr_gettingstart.ipynb | uMetalooper/vietocr |
Heroes Of Pymoli Data Analysis* Of the 1163 active players, the vast majority are male (82%). There also exists, a smaller, but notable proportion of female players (16%).* Our peak age demographic falls between 20-24 (42%) with secondary groups falling between 15-19 (17.80%) and 25-29 (15.48%).* Our players are putti... | import pandas as pd
import numpy as np | _____no_output_____ | ADSL | HeroesOfPymoli/.ipynb_checkpoints/HeroesOfPymoli_Example-checkpoint.ipynb | dimpalsuthar91/RePanda |
Metadata preprocessing tutorial Melusine **prepare_data.metadata_engineering subpackage** provides classes to preprocess the metadata :- **MetaExtension :** a transformer which creates an 'extension' feature extracted from regex in metadata. It extracts the extensions of mail adresses.- **MetaDate :** a transformer wh... | from melusine.data.data_loader import load_email_data
df_emails = load_email_data()
df_emails = df_emails[['from','date']]
df_emails['from']
df_emails['date'] | _____no_output_____ | Apache-2.0 | tutorial/tutorial05_metadata_preprocessing.ipynb | milidris/melusine |
MetaExtension transformer A **MetaExtension transformer** creates an *extension* feature extracted from regex in metadata. It extracts the extensions of mail adresses. | from melusine.prepare_email.metadata_engineering import MetaExtension
meta_extension = MetaExtension()
df_emails = meta_extension.fit_transform(df_emails)
df_emails.extension | _____no_output_____ | Apache-2.0 | tutorial/tutorial05_metadata_preprocessing.ipynb | milidris/melusine |
MetaExtension transformer A **MetaDate transformer** creates new features from dates : **hour**, **minute** and **dayofweek**. | from melusine.prepare_email.metadata_engineering import MetaDate
meta_date = MetaDate()
df_emails = meta_date.fit_transform(df_emails)
df_emails.date[0]
df_emails.hour[0]
df_emails.loc[0,'min']
df_emails.dayofweek[0] | _____no_output_____ | Apache-2.0 | tutorial/tutorial05_metadata_preprocessing.ipynb | milidris/melusine |
Dummifier transformer A **Dummifier transformer** dummifies categorial features.Its arguments are :- **columns_to_dummify** : a list of the metadata columns to dummify. | from melusine.prepare_email.metadata_engineering import Dummifier
dummifier = Dummifier(columns_to_dummify=['extension', 'dayofweek', 'hour', 'min'])
df_meta = dummifier.fit_transform(df_emails)
df_meta.columns
df_meta.head() | _____no_output_____ | Apache-2.0 | tutorial/tutorial05_metadata_preprocessing.ipynb | milidris/melusine |
Table of Contents1 Seq2Seq With Attention1.1 Data Preparation1.2 Model Implementation1.2.1 Encoder1.2.2 Attention1.2.3 Decoder1.2.4 Seq2Seq1.3 Training Seq2Seq1.4 Evaluating Seq2Seq1.5 Summary2 Refer... | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(css_style='custom2.css', plot_style=False)
os.chdir(path)
# 1. magic for inline plot
... | Ethen 2019-10-09 13:46:01
CPython 3.6.4
IPython 7.7.0
numpy 1.16.5
torch 1.1.0.post2
torchtext 0.3.1
spacy 2.1.6
| MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Seq2Seq With Attention Seq2Seq framework involves a family of encoders and decoders, where the encoder encodes a source sequence into a fixed length vector from which the decoder picks up and aims to correctly generates the target sequence. The vanilla version of this type of architecture looks something along the lin... | # !python -m spacy download de
# !python -m spacy download en
SEED = 2222
random.seed(SEED)
torch.manual_seed(SEED)
# tokenize sentences into individual tokens
# https://spacy.io/usage/spacy-101#annotations-token
spacy_de = spacy.load('de_core_news_sm')
spacy_en = spacy.load('en_core_web_sm')
def tokenize_de(text):
... | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Model Implementation | # adjustable parameters
INPUT_DIM = len(source.vocab)
OUTPUT_DIM = len(target.vocab)
ENC_EMB_DIM = 256
DEC_EMB_DIM = 256
ENC_HID_DIM = 512
DEC_HID_DIM = 512
N_LAYERS = 1
ENC_DROPOUT = 0.5
DEC_DROPOUT = 0.5 | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
The following sections are heavily "borrowed" from the wonderful tutorial on this topic listed below.- [Jupyter Notebook: Neural Machine Translation by Jointly Learning to Align and Translate](https://nbviewer.jupyter.org/github/bentrevett/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointl... | class Encoder(nn.Module):
def __init__(self, input_dim, emb_dim, hid_dim, n_layers, dropout):
super().__init__()
self.emb_dim = emb_dim
self.hid_dim = hid_dim
self.input_dim = input_dim
self.n_layers = n_layers
self.dropout = dropout
self.embedding = nn.Embed... | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Notice that output's last dimension is 1024, which is the hidden dimension (512) multiplied by the number of directions (2). Whereas the hidden's first dimension is 2, representing the number of directions (2).- The returned outputs of bidirectional RNN at timestep $t$ is the output after feeding input to both the reve... | # the outputs are concatenated at the last dimension
assert (outputs[-1, :, :ENC_HID_DIM] == hidden[0]).all()
assert (outputs[0, :, ENC_HID_DIM:] == hidden[1]).all()
# experiment with n_layers = 2
n_layers = 2
encoder = Encoder(INPUT_DIM, ENC_EMB_DIM, ENC_HID_DIM, n_layers, ENC_DROPOUT).to(device)
outputs, hidden = enc... | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Notice now the first dimension of the hidden cell becomes 4, which represents the number of layers (2) multiplied by the number of directions (2). The order of the hidden state is stacked by [forward_1, backward_1, forward_2, backward_2, ...] | assert (outputs[-1, :, :ENC_HID_DIM] == hidden[2]).all()
assert (outputs[0, :, ENC_HID_DIM:] == hidden[3]).all() | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
We'll need some final touches for our actual encoder. As our encoder's hidden state will be used as the decoder's initial hidden state, we need to make sure we make them the same shape. In our example, the decoder is not bidirectional, and only needs a single context vector, $z$, to use as its initial hidden state, $s_... | class Encoder(nn.Module):
def __init__(self, input_dim, emb_dim, enc_hid_dim, dec_hid_dim, n_layers, dropout):
super().__init__()
self.emb_dim = emb_dim
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
self.input_dim = input_dim
self.n_layers = n_layers
... | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Attention The next part is the hightlight. The attention layer will take in the previous hidden state of the decoder $s_{t-1}$, and all of the stacked forward and backward hidden state from the encoder $H$. The output will be an attention vector $a_t$, that is the length of the source sentece, each element of this vec... | class Attention(nn.Module):
def __init__(self, enc_hid_dim, dec_hid_dim):
super().__init__()
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
# enc_hid_dim multiply by 2 due to bidirectional
self.fc1 = nn.Linear(enc_hid_dim * 2 + dec_hid_dim, dec_hid_dim)
... | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Decoder Now comes the decoder, within the decoder, we first use the attention layer that we've created in the previous section to compute the attention weight, this gives us the weight for each source sentence that the model should pay attention to when generating the current target output in the sequence. Along with ... | class Decoder(nn.Module):
def __init__(self, output_dim, emb_dim, enc_hid_dim, dec_hid_dim, n_layers,
dropout, attention):
super().__init__()
self.emb_dim = emb_dim
self.enc_hid_dim = enc_hid_dim
self.dec_hid_dim = dec_hid_dim
self.output_dim = output_dim
... | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Seq2Seq This part is about putting the encoder and decoder together and is very much identical to the vanilla seq2seq framework, hence the explanation is omitted. | class Seq2Seq(nn.Module):
def __init__(self, encoder, decoder, device):
super().__init__()
self.encoder = encoder
self.decoder = decoder
self.device = device
def forward(self, src_batch, trg_batch, teacher_forcing_ratio=0.5):
max_len, batch_size = trg_batch.shape
... | The model has 12,975,877 trainable parameters
| MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Training Seq2Seq We've done the hard work of defining our seq2seq module. The final touch is to specify the training/evaluation loop. | optimizer = optim.Adam(seq2seq.parameters())
# ignore the padding index when calculating the loss
PAD_IDX = target.vocab.stoi['<pad>']
criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)
def train(seq2seq, iterator, optimizer, criterion):
seq2seq.train()
epoch_loss = 0
for batch in iterator:
... | Epoch: 01 | Time: 2m 30s
Train Loss: 4.844 | Train PPL: 126.976
Val. Loss: 4.691 | Val. PPL: 108.948
Epoch: 02 | Time: 2m 30s
Train Loss: 3.948 | Train PPL: 51.808
Val. Loss: 4.004 | Val. PPL: 54.793
Epoch: 03 | Time: 2m 31s
Train Loss: 3.230 | Train PPL: 25.281
Val. Loss: 3.498 | Val. PPL: 33.059
Epoch... | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Evaluating Seq2Seq | seq2seq.load_state_dict(torch.load('tut2-model.pt'))
test_loss = evaluate(seq2seq, test_iterator, criterion)
print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |') | | Test Loss: 3.237 | Test PPL: 25.467 |
| MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Here, we pick a random example in our dataset, print out the original source and target sentence. Then takes a look at whether the "predicted" target sentence generated by the model. | example_idx = 0
example = train_data.examples[example_idx]
print('source sentence: ', ' '.join(example.src))
print('target sentence: ', ' '.join(example.trg))
src_tensor = source.process([example.src]).to(device)
trg_tensor = target.process([example.trg]).to(device)
print(trg_tensor.shape)
seq2seq.eval()
with torch.no... | _____no_output_____ | MIT | deep_learning/seq2seq/2_torch_seq2seq_attention.ipynb | certara-ShengnanHuang/machine-learning |
Categorical deduction (generic and all inferences)1. Take a mix of generic and specific statements2. Create powerset of combinations of specific statements3. create a inference graph for each combination of specific statements.4. Make all possible inferences for each graph (chain)5. present the union of possible concl... | # Syllogism specific statements
# First statement A __ B.
# Second statement B __ C.
# Third statement A ___ C -> look up tables to check if true, possible, or false.
specific_statement_options = {'disjoint from','overlaps with','subset of','superset of','identical to'}
# make a dictionary. key is a tuple with firs... | _____no_output_____ | MIT | reasoning_engine/categorical reasoning/Categorical_deduction_generic_all_inferences.ipynb | rts1988/IntelligentTutoringSystem_Experiments |
Understanding ROS NodesThis tutorial introduces ROS graph concepts and discusses the use of `roscore`, `rosnode`, and `rosrun` commandline tools.Source: [ROS Wiki](http://wiki.ros.org/ROS/Tutorials/UnderstandingNodes) Quick Overview of Graph Concepts* Nodes: A node is an executable that uses ROS to communicate with o... | %%bash --bg
roscore | Starting job # 0 in a separate thread.
| MIT | notebooks/ROS_Tutorials/.ipynb_checkpoints/ROS Nodes-checkpoint.ipynb | GimpelZhang/git_test |
Using `rosnode``rosnode` displays information about the ROS nodes that are currently running. The `rosnode list` command lists these active nodes: | %%bash
rosnode list
%%bash
rosnode info rosout | --------------------------------------------------------------------------------
Node [/rosout]
Publications:
* /rosout_agg [rosgraph_msgs/Log]
Subscriptions:
* /rosout [unknown type]
Services:
* /rosout/get_loggers
* /rosout/set_logger_level
contacting node http://localhost:43395/ ...
Pid: 18703
| MIT | notebooks/ROS_Tutorials/.ipynb_checkpoints/ROS Nodes-checkpoint.ipynb | GimpelZhang/git_test |
Using `rosrun``rosrun` allows you to use the package name to directly run a node within a package (without having to know the package path). | %%bash --bg
rosrun turtlesim turtlesim_node | Starting job # 2 in a separate thread.
| MIT | notebooks/ROS_Tutorials/.ipynb_checkpoints/ROS Nodes-checkpoint.ipynb | GimpelZhang/git_test |
NOTE: The turtle may look different in your turtlesim window. Don't worry about it - there are [many types of turtle](http://wiki.ros.org/DistributionsCurrent_Distribution_Releases) and yours is a surprise! | %%bash
rosnode list | /rosout
/turtlesim
| MIT | notebooks/ROS_Tutorials/.ipynb_checkpoints/ROS Nodes-checkpoint.ipynb | GimpelZhang/git_test |
One powerful feature of ROS is that you can reassign Names from the command-line.Close the turtlesim window to stop the node. Now let's re-run it, but this time use a [Remapping Argument](http://wiki.ros.org/Remapping%20Arguments) to change the node's name: | %%bash --bg
rosrun turtlesim turtlesim_node __name:=my_turtle | Starting job # 3 in a separate thread.
| MIT | notebooks/ROS_Tutorials/.ipynb_checkpoints/ROS Nodes-checkpoint.ipynb | GimpelZhang/git_test |
Now, if we go back and use `rosnode list`: | %%bash
rosnode list | /my_turtle
/rosout
/turtlesim
| MIT | notebooks/ROS_Tutorials/.ipynb_checkpoints/ROS Nodes-checkpoint.ipynb | GimpelZhang/git_test |
print('Welcome to Techno Quiz: ')
ans = input('''Ready to begin (yes/no): ''')
score=0
total_Q=15
if ans.lower() =='yes' :
ans = input(''' 1.How to check your current python version ?
A. python version
B. python -V
Ans:''')
... | _____no_output_____ | Apache-2.0 | Quiz.ipynb | sandeepkumarpradhan71/sandeepkumar | |
input x, truth y, predict (y-x) in bins.major changes:- in Datagenerator(), add y=y-X[output_idxs]- in create_predictions(): when unnormalizing, only multiply with std, dont add mean- included adaptive binsObservations- DOI takes much longer to train to same loss than normal categorical.- not much better performance wi... | %load_ext autoreload
%autoreload 2
import xarray as xr
import numpy as np
import matplotlib.pyplot as plt
from src.data_generator import *
from src.train import *
from src.utils import *
from src.networks import *
tf.__version__
import os
import tensorflow as tf
os.environ["CUDA_VISIBLE_DEVICES"]=str(0)
print("Num GPUs... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Training | # model = build_resnet_categorical(
# **args, input_shape=dg_train.shape,
# )
# # model.summary()
# categorical_loss = create_lat_categorical_loss(dg_train.data.lat, 2)
# model.compile(keras.optimizers.Adam(1e-3), loss=categorical_loss)
# model_history=model.fit(dg_train, epochs=50)
#training is slower compared t... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Predictions | exp_id='categorical_doi_v1'
model_save_dir=args['model_save_dir']
#args['ext_mean'] = xr.open_dataarray(f'{args["model_save_dir"]}/{args["exp_id"]}_mean.nc')
#args['ext_std'] = xr.open_dataarray(f'{args["model_save_dir"]}/{args["exp_id"]}_std.nc')
#dg_test = load_data(**args, only_test=True)
model = keras.models.load_m... | 257.84134 258.57373 258.57373
| MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Most likely class | # Using bin_mid_points of prediction with highest probability
das = []
for var in ['z', 't']:
idxs = np.argmax(preds[var], -1)
most_likely = preds[var].mid_points[idxs]
das.append(xr.DataArray(
most_likely, dims=['time', 'lat', 'lon'],
coords = [preds.time, preds.lat, preds.lon],
nam... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
RMSE | compute_weighted_rmse(preds_ml_new, valid).load()
#still very bad. for comparison, training on the same data for same epochs (loss=1.7) without difference to input method had rmse of 685 | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Binned CRPS | preds['t'].mid_points
#changing Observation directly instead of predictions for binned crps
obs=valid-valid.shift(time=-dg_test.lead_time)
obs=obs.sel(time=preds.time) #reducing to preds size
obs=obs.sel(time=slice(None,'2018-12-28T22:00:00'))#removing nan values
print(
valid.t.isel(lat=0,lon=0).sel(time='2018-05-05T2... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
compare to - Adaptive binning Adaptive binning | #Finding bin edges on full 1 year training data (Not possible for 40 years)
args['is_categorical']=False
dg_train, dg_valid, dg_test = load_data(**args)
args['is_categorical']=True
x,y=dg_train[0]; print(x.shape, y.shape)
diff=y-x[...,dg_train.output_idxs]
print(diff.min(), diff.max(), diff.mean())
plt.hist(diff.resha... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Training for adaptive bins | # model2 = build_resnet_categorical(
# **args, input_shape=dg_train.shape,
# )
# # model.summary()
# categorical_loss = create_lat_categorical_loss(dg_train.data.lat, 2)
# model2.compile(keras.optimizers.Adam(1e-3), loss=categorical_loss)
# model_history=model2.fit(dg_train, epochs=50)
# exp_id='categorical_doi_... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Predictions for Adaptive bins | exp_id='categorical_doi_adaptive_bins_v1'
model_save_dir=args['model_save_dir']
model2 = keras.models.load_model(
f'{model_save_dir}/{exp_id}.h5',
custom_objects={'PeriodicConv2D': PeriodicConv2D, 'categorical_loss': keras.losses.mse}
)
#args
#full-data (apr-dec 2018)
preds = create_predictions(model2, dg_test,... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Most likely class | # Using bin_mid_points of prediction with highest probability
das = []
for var in ['z', 't']:
idxs = np.argmax(preds[var], -1)
most_likely = preds[var].mid_points[idxs]
das.append(xr.DataArray(
most_likely, dims=['time', 'lat', 'lon'],
coords = [preds.time, preds.lat, preds.lon],
nam... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
RMSE | compute_weighted_rmse(preds_ml_new, valid).load()
#almost same as non-adaptive. loss comparable (~2.9 for no-adaptive. ~2.3 for adaptive) | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Binned CRPS | preds['t'].mid_points
#changing Observation directly instead of predictions for binned crps
obs=valid-valid.shift(time=-dg_test.lead_time)
obs=obs.sel(time=preds.time) #reducing to preds size
obs=obs.sel(time=slice(None,'2018-12-28T22:00:00'))#removing nan values
print(
valid.t.isel(lat=0,lon=0).sel(time='2018-05-05T2... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
comparing to - without input difference | from src.data_generator import *
args['bin_min']=-5; args['bin_max']=5 #checked min, max of (x-y) in train.
args['num_bins'], args['bin_min'], args['bin_max']
dg_train, dg_valid, dg_test = load_data(**args)
x,y=dg_train[0]; print(x.shape, y.shape)
x,y=dg_valid[0]; print(x.shape, y.shape)
x,y=dg_test[0]; print(x.shape,... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Binned CRPS | def compute_bin_crps(obs, preds, bin_edges):
"""
Last axis must be bin axis
obs: [...]
preds: [..., n_bins]
"""
obs = obs.values
preds = preds.values
# Convert observation
a = np.minimum(bin_edges[1:], obs[..., None])
b = bin_edges[:-1] * (bin_edges[0:-1] > obs[..., None])
y ... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Adaptive binning | args['is_categorical']=False
dg_train, dg_valid, dg_test = load_data(**args)
args['is_categorical']=True
x,y=dg_train[0]; print(x.shape, y.shape)
diff=y-x[...,dg_train.output_idxs]
print(diff.min(), diff.max(), diff.mean())
plt.hist(diff.reshape(-1))
diff=[]
for x,y in dg_train:
diff.append(y-x[...,dg_train.outp... | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
Unnormalized Data | a1=np.arange(100)
mean=np.mean(a1); std=np.std(a1)
a1_norm=(a1-mean)/std
a1_norm
a1[4]-a1[2]
diff=a1_norm[4]-a1_norm[2]
diff
diff*std | _____no_output_____ | MIT | nbs_probabilistic/07.2 - Difference of Input.ipynb | sagar-garg/WeatherBench |
$f(x)=exp(\sin(\pi x))$ integrate from $-1$ to $1$.--- | import math
import numpy as np
def f(x):
return math.exp(np.sin(np.pi*x))
n=10
k=-1
result=0
for i in range(n):
result+=f(k)/n
result+=f(k+2/n)/n
k=k+2/n
print(result)
n=20
k=-1
result=0
for i in range(n):
result+=f(k)/n
result+=f(k+2/n)/n
k=k+2/n
print(result)
n=40
k=-1
result=0
for i in range(n):
re... | _____no_output_____ | MIT | sec5exercise02a.ipynb | teshenglin/computational_mathematics |
Day and Night Image Classifier---The day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images.We'd like to build a classifier that can accurately label these images as day or night, and that relies on finding... | import cv2 # computer vision library
import helpers
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline | _____no_output_____ | MIT | Intro-To-Computer-Vision-1/1_1_Image_Representation/6_1. Visualizing the Data.ipynb | prakhargurawa/PyTorch-ML |
Training and Testing DataThe 200 day/night images are separated into training and testing datasets. * 60% of these images are training images, for you to use as you create a classifier.* 40% are test images, which will be used to test the accuracy of your classifier.First, we set some variables to keep track of some w... | # Image data directories
image_dir_training = "day_night_images/training/"
image_dir_test = "day_night_images/test/" | _____no_output_____ | MIT | Intro-To-Computer-Vision-1/1_1_Image_Representation/6_1. Visualizing the Data.ipynb | prakhargurawa/PyTorch-ML |
Load the datasetsThese first few lines of code will load the training day/night images and store all of them in a variable, `IMAGE_LIST`. This list contains the images and their associated label ("day" or "night"). For example, the first image-label pair in `IMAGE_LIST` can be accessed by index: ``` IMAGE_LIST[0][:]``... | # Using the load_dataset function in helpers.py
# Load training data
IMAGE_LIST = helpers.load_dataset(image_dir_training)
len(IMAGE_LIST) | _____no_output_____ | MIT | Intro-To-Computer-Vision-1/1_1_Image_Representation/6_1. Visualizing the Data.ipynb | prakhargurawa/PyTorch-ML |
--- 1. Visualize the input images | # Select an image and its label by list index
image_index = 0
selected_image = IMAGE_LIST[image_index][0]
selected_label = IMAGE_LIST[image_index][1]
## TODO: Print out 1. The shape of the image and 2. The image's label `selected_label`
print(selected_image.shape)
print(selected_label)
## TODO: Display a night image
#... | (458, 800, 3)
day
(700, 1280, 3)
night
| MIT | Intro-To-Computer-Vision-1/1_1_Image_Representation/6_1. Visualizing the Data.ipynb | prakhargurawa/PyTorch-ML |
# Installs
%%capture
!pip install --upgrade category_encoders plotly
# Imports
import os, sys
os.chdir('/content')
!git init .
!git remote add origin https://github.com/LambdaSchool/DS-Unit-2-Kaggle-Challenge.git
!git pull origin master
!pip install -r requirements.txt
os.chdir('module1')
# Disable warning
import wa... | _____no_output_____ | MIT | Kaggle_Challenge_Assignment7.ipynb | JimKing100/DS-Unit-2-Kaggle-Challenge | |
 [](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-workshop/blob/master/tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier... | import json
from google.colab import files
license_keys = files.upload()
with open(list(license_keys.keys())[0]) as f:
license_keys = json.load(f)
%%capture
for k,v in license_keys.items():
%set_env $k=$v
!wget https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/jsl_colab_setup.sh
!bas... | 3.0.1
3.0.0
| Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
ADE Classifier Pipeline (with a pretrained model)`True` : The sentence is talking about a possible ADE`False` : The sentences doesn't have any information about an ADE. ADE Classifier with BioBert \
.setInputCol("text")\
.setOutputCol("sentence")
# Tokenizer splits words in a relevant format for NLP
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutp... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
As you can see `gastric problems` is not detected as `ADE` as it is in a negative context. So, classifier did a good job detecting that. | text="I just took a Metformin and started to feel dizzy."
ade_lp_pipeline.annotate(text)['class'][0]
t='''
Always tired, and possible blood clots. I was on Voltaren for about 4 years and all of the sudden had a minor stroke and had blood clots that traveled to my eye. I had every test in the book done at the hospital,... | True
False
True
False
| Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
ADE Classifier trained with conversational (short) sentences This model is trained on short, conversational sentences related to ADE and is supposed to do better on the text that is short and used in a daily context. ![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABc4AAAESCAYAAADEw0ZmAAAgAElEQVR4Aeyd2Y8U1fu... | conv_classsifierdl = ClassifierDLModel.pretrained("classifierdl_ade_conversational_biobert", "en", "clinical/models")\
.setInputCols(["sentence", "sentence_embeddings"]) \
.setOutputCol("class")
conv_ade_clf_pipeline = Pipeline(
stages=[documentAssembler,
tokenizer,
... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
ADE NERExtracts `ADE` and `DRUG` entities from text. \
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
word_embeddings = WordEmbeddingsM... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
As you can see `gastric problems` is not detected as `ADE` as it is in a negative context. So, NER did a good job ignoring that. ADE NER with Bert embeddings \
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("token")
bert_embeddings = BertEmbeddings.... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
Looks like Bert version of NER returns more entities than clinical embeddings version. NER and Classifier combined with AssertionDL Model | assertion_ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ass_ner_chunk")\
.setWhiteList(['ADE'])
biobert_assertion = AssertionDLModel.pretrained("assertion_dl_biobert", "en", "clinical/models") \
.setInputCols(["sentence", "ass_ner_chunk", "embeddings"]) \... | I feel a bit drowsy & have a little blurred vision, so far no gastric problems. I have been on Arthrotec 50 for over 10 years on and off, only taking it when I needed it. Due to my arthritis getting progressively worse, to the point where I am in tears with the agony, gp's started me on 75 twice a day and I have to tak... | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
Looks great ! `gastric problems` is detected as `ADE` and `absent` ADE models applied to Spark Dataframes | import pyspark.sql.functions as F
! wget -q https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-workshop/master/tutorials/Certification_Trainings/Healthcare/data/sample_ADE_dataset.csv
ade_DF = spark.read\
.option("header", "true")\
.csv("./sample_ADE_dataset.csv")\
... | +--------------------------------------------------+-----+
| text|label|
+--------------------------------------------------+-----+
|Do U know what Meds are R for bipolar depressio...|False|
|# hypercholesterol: Because of elevated CKs (pe...| True|
|Her weight, respirtory s... | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
**With BioBert version of NER** (will be slower but more accurate) | import pyspark.sql.functions as F
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")\
.setWhiteList(['ADE'])
ner_pipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
bert_embeddings,
ade_ner_bert,
ner_c... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
**Doing the same with clinical embeddings version** (faster results) | import pyspark.sql.functions as F
ner_converter = NerConverter() \
.setInputCols(["sentence", "token", "ner"]) \
.setOutputCol("ner_chunk")\
.setWhiteList(['ADE'])
ner_pipeline = Pipeline(stages=[
documentAssembler,
sentenceDetector,
tokenizer,
word_embeddings,
ade_ner,
ner_converter])
... | +----------------------------------------------------------------------+----------------------------------------------------------------------+
| text| ADE_phrases|
+-------------------------------... | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
Creating sentence dataframe (one sentence per row) and getting ADE entities and categories | documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("document")
sentenceDetector = SentenceDetector()\
.setInputCols(["document"])\
.setOutputCol("sentence")\
.setExplodeSentences(True)
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputC... | +------------------------------------------------------------+---------------------------------------------+-------+
| sentence| ADE_phrases| is_ADE|
+------------------------------------------------------------+------------------------... | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
Creating a pretrained pipeline with ADE NER, Assertion and Classifer | # Annotator that transforms a text column from dataframe into an Annotation ready for NLP
documentAssembler = DocumentAssembler()\
.setInputCol("text")\
.setOutputCol("sentence")
# Tokenizer splits words in a relevant format for NLP
tokenizer = Tokenizer()\
.setInputCols(["sentence"])\
.setOutputCol("t... | _____no_output_____ | Apache-2.0 | tutorials/Certification_Trainings/Healthcare/16.Adverse_Drug_Event_ADE_NER_and_Classifier.ipynb | gkovaig/spark-nlp-workshop |
Slice 136 patient 0002 | from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(d[2]),
d[2])
class_weights
import keras
model = keras.models.load_model('trial_0001_MFCcas_dim2_128_acc.h5')
m_... | _____no_output_____ | MIT | model.ipynb | abhi134/Brain_Tumor_Segmentation |
eval on 128th slice 0002 | model.evaluate([X1,X2],y,batch_size = 1024)
model_info = model.fit([X1,X2],y,epochs=30,batch_size=256,class_weight= class_weights)
import matplotlib.pyplot as plt
plt.plot(model_info.history['acc'])
#plt.plot(m_info.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['... | _____no_output_____ | MIT | model.ipynb | abhi134/Brain_Tumor_Segmentation |
eval on 100th slice 0001 | model.evaluate([X1,X2],y,batch_size = 1024)
pred = model.predict([X1,X2],batch_size = 1024)
pred = np.around(pred)
pred1 = np.dot(pred.reshape(17589,5),np.array([0,1,2,3,4]))
y1 = np.dot(y.reshape(17589,5),np.array([0,1,2,3,4]))
y2 = np.argmax(y.reshape(17589,5),axis = 1)
y2.all() == 0
y1.all()==0
from sklearn import m... | _____no_output_____ | MIT | model.ipynb | abhi134/Brain_Tumor_Segmentation |
Slice 128 patient 0001 | from sklearn.utils import class_weight
class_weights = class_weight.compute_class_weight('balanced',
np.unique(d[2]),
d[2])
class_weights
m1.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
m1... | Epoch 1/20
14541/14541 [==============================] - 127s 9ms/step - loss: 1.3402 - acc: 0.8345
Epoch 2/20
14541/14541 [==============================] - 123s 8ms/step - loss: 1.1816 - acc: 0.9560
Epoch 3/20
14541/14541 [==============================] - 123s 8ms/step - loss: 1.0906 - acc: 0.9647
Epoch 4/20
14541/... | MIT | model.ipynb | abhi134/Brain_Tumor_Segmentation |
plot of inputcascade | import matplotlib.pyplot as plt
plt.plot(m1_info.history['acc'])
#plt.plot(m_info.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
m1.save('trial_0001_input_cascade_acc.h5')
plt.plot(m_info.history['acc'])
#plt.plot(m_i... | _____no_output_____ | MIT | model.ipynb | abhi134/Brain_Tumor_Segmentation |
Training on slice 128, evaluating on 136 | m.evaluate([X1,X2],y,batch_size = 1024)
m.save('trial_0001_MFCcas_dim2_128_acc.h5')
pred = m.predict([X1,X2],batch_size = 1024)
print(((pred != 0.) & (pred != 1.)).any())
pred = np.around(pred)
type(y)
pred1 = np.dot(pred.reshape(17589,5),np.array([0,1,2,3,4]))
pred1.shape
y1 = np.dot(y.reshape(17589,5),np.array([0,1,2... | data.ipynb model.ipynb
data_scan_0001.pickle training.ipynb
data_trial_81.h5 trial_0001_81_accuracy.h5
data_trial_dim2_128.h5 trial_0001_81_f1.h5
data_trial.h5 trial_0001_accuracy.h5
data_trial_X.pickle trial_0001_f1.h5
data_trial_Y.pickle trial_0001_input_cascade_acc.h5
data_Y_0001.pickle trial_0001_input_cas... | MIT | model.ipynb | abhi134/Brain_Tumor_Segmentation |
for training over entire image, create batch of patches for one image, batch of labels in Y | import h5py
import numpy as np
hf = h5py.File('data_trial_dim2_128.h5', 'r')
X = hf.get('dataset_1')
Y = hf.get('dataset_2')
y = np.zeros((26169,1,1,5))
for i in range(y.shape[0]):
y[i,:,:,Y[i]] = 1
X = np.asarray(X)
X.shape
keras.__version__
import keras.backend as K
def f1_score(y_true, y_pred):
# Count posit... | _____no_output_____ | MIT | model.ipynb | abhi134/Brain_Tumor_Segmentation |
Load 10 years of accident data, from 2007 to 2016 | #load accidents data from 2007 to 2016
dbf07= DBF('accident/accident2007.dbf')
dbf08= DBF('accident/accident2008.dbf')
dbf09= DBF('accident/accident2009.dbf')
dbf10= DBF('accident/accident2010.dbf')
dbf11 = DBF('accident/accident2011.dbf')
dbf12 = DBF('accident/accident2012.dbf')
dbf13 = DBF('accident/accident2013.dbf... | _____no_output_____ | MIT | .ipynb_checkpoints/Project 3-checkpoint.ipynb | junemore/traffic-accidents-analysis |
First, we want to combine accidents10 ~ accidents16 to one dataframe. Since not all of the accident data downloaded from the U.S. Department of Transportation have the same features, by using the `jion:inner` option in `pd.concat` function, we can get the intersection of features. | # rename column name in frame07 so that columns names are the same with other frames
accidents07.rename(columns={'latitude': 'LATITUDE', 'longitud': 'LONGITUD'}, inplace=True)
# take a look inside how the accident data file looks like
#combine all accidents file
allaccidents = pd.concat([accidents07,accidents08,acciden... | _____no_output_____ | MIT | .ipynb_checkpoints/Project 3-checkpoint.ipynb | junemore/traffic-accidents-analysis |
The allaccidents table recorded 320874 accidents from 2010-2016, and it has 42 features. Here are the meaning of some of the features according to the `FARS Analytical User’s Manual`. Explaination of variables*VE_TOTAL*: Number of Vehicle in crash *VE_FORMS*: Number of Motor Vehicles in Transport (MVIT) *PED*: Number o... | import warnings
warnings.filterwarnings('ignore')
accidents = allaccidents[['YEAR','ST_CASE','STATE','VE_TOTAL','PERSONS','FATALS','MONTH','DAY_WEEK','HOUR','NHS','LATITUDE','LONGITUD','MAN_COLL','LGT_COND','WEATHER','ARR_HOUR','ARR_MIN','CF1','DRUNK_DR']]
accidents.rename(columns={'ST_CASE':'CASE_NUM','VE_TOTAL':'NUM_... | _____no_output_____ | MIT | .ipynb_checkpoints/Project 3-checkpoint.ipynb | junemore/traffic-accidents-analysis |
combine "year" and "case_num" to reindex accidents dataframe. | accidents['STATE']=accidents['STATE'].astype(int)
accidents['CASE_NUM']=accidents['CASE_NUM'].astype(int)
accidents['YEAR']=accidents['YEAR'].astype(int)
accidents.index = list(accidents['YEAR'].astype(str) + accidents['CASE_NUM'].astype(str))
accidents.head()
accidents.shape | _____no_output_____ | MIT | .ipynb_checkpoints/Project 3-checkpoint.ipynb | junemore/traffic-accidents-analysis |
Load vehicle data file which contains mortality rate We also want to study the mortality rate of fatal accidents. The data element “Fatalities in Vehicle” in the Vehicle data file from the `U.S. Department of Transportation` website provides the number of deaths in a vehicle. | vdbf07= DBF('vehicle_deaths/vehicle2007.dbf')
vdbf08= DBF('vehicle_deaths/vehicle2008.dbf')
vdbf09= DBF('vehicle_deaths/vehicle2009.dbf')
vdbf10= DBF('vehicle_deaths/vehicle2010.dbf')
vdbf11= DBF('vehicle_deaths/vehicle2011.dbf')
vdbf12= DBF('vehicle_deaths/vehicle2012.dbf')
vdbf13= DBF('vehicle_deaths/vehicle2013.dbf'... | _____no_output_____ | MIT | .ipynb_checkpoints/Project 3-checkpoint.ipynb | junemore/traffic-accidents-analysis |
plot | #the total accidents number each year, analysis the difference between every year
year_acci=all[['YEAR','CASE_NUM']].groupby('YEAR').count()
month_acci=all[['MONTH','CASE_NUM']].groupby('MONTH').count()
day_acci=all[['DAY_WEEK','CASE_NUM']].groupby('DAY_WEEK').count()
hour_acci=all[['HOUR','CASE_NUM']].groupby('HOUR').... | _____no_output_____ | MIT | .ipynb_checkpoints/Project 3-checkpoint.ipynb | junemore/traffic-accidents-analysis |
Part 9: Hither to Train, Thither to TestOK, now we know a bit about perceptrons. We'll return to that subject again. But now let's do a couple of things with our 48 colors from lesson 7:* We're going to wiggle some more - perturb the color data - in order to generate even more data.* But now we're going to randomly sp... | from keras.layers import Activation, Dense, Dropout
from keras.models import Sequential
import keras.optimizers, keras.utils, numpy
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
def train(rgbValues, colorNames, epochs = 3, perceptronsPerColorName = 4, batchSize =... | Using TensorFlow backend.
| MIT | Part09_Hither_to_Train_Thither_to_Test.ipynb | erikma/ColorMatching |
Here's our createMoreTrainingData() function, mostly the same but we've doubled the number of perturbValues by adding points in between the previous ones. | def createMoreTrainingData(colorNameToRGBMap):
# The incoming color map is not typically going to be oversubscribed with e.g.
# extra 'red' samples pointing to slightly different colors. We generate a
# training dataset by perturbing each color by a small amount positive and
# negative. We do this for e... | _____no_output_____ | MIT | Part09_Hither_to_Train_Thither_to_Test.ipynb | erikma/ColorMatching |
And our previous 48 crayon colors, and let's try training: | def rgbToFloat(r, g, b): # r, g, b in 0-255 range
return (float(r) / 255.0, float(g) / 255.0, float(b) / 255.0)
# http://www.jennyscrayoncollection.com/2017/10/complete-list-of-current-crayola-crayon.html
colorMap = {
# 8-crayon box colors
'red': rgbToFloat(238, 32, 77),
'yellow': rgbToFloat(252, 232,... | WARNING:tensorflow:From c:\users\erik\appdata\local\programs\python\python36\lib\site-packages\tensorflow\python\framework\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by pl... | MIT | Part09_Hither_to_Train_Thither_to_Test.ipynb | erikma/ColorMatching |
Not bad: We quickly got our loss down to 0.17 in only 3 epochs, but the larger batch size kept it from taking a really long time.But let's examine our new addition, the test data scoring result. From my machine: `Score: loss=0.1681, accuracy=0.9464`Note that we trained with 74,000 data points, but we kept aside an addi... | from ipywidgets import interact
from IPython.core.display import display, HTML
def displayColor(r, g, b):
rInt = min(255, max(0, int(r * 255.0)))
gInt = min(255, max(0, int(g * 255.0)))
bInt = min(255, max(0, int(b * 255.0)))
hexColor = "#%02X%02X%02X" % (rInt, gInt, bInt)
display(HTML('<div style=... | _____no_output_____ | MIT | Part09_Hither_to_Train_Thither_to_Test.ipynb | erikma/ColorMatching |
In my opinion the extra perturbation data made quite a bit of difference. It guesses over 70% for gray at (0.5, 0.5, 0.5), better than before.Here's the hyperparameter slider version so you can try out different epochs, batch sizes, and perceptrons: | @interact(epochs = (1, 10), perceptronsPerColorName = (1, 12), batchSize = (1, 50))
def trainModel(epochs=4, perceptronsPerColorName=3, batchSize=16):
global colorModel
global colorLabels
(colorModel, colorLabels) = train(rgbValues, colorNames, epochs=epochs, perceptronsPerColorName=perceptronsPerColorName,... | _____no_output_____ | MIT | Part09_Hither_to_Train_Thither_to_Test.ipynb | erikma/ColorMatching |
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