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 |
|---|---|---|---|---|---|
Priming and generating text Typically you'll want to prime the network so you can build up a hidden state. Otherwise the network will start out generating characters at random. In general the first bunch of characters will be a little rough since it hasn't built up a long history of characters to predict from. | def sample(net, size, prime='The', top_k=None):
if(train_on_gpu):
net.cuda()
else:
net.cpu()
net.eval() # eval mode
# First off, run through the prime characters
chars = [ch for ch in prime]
h = net.init_hidden(1)
for ch in prime:
char, h = predict(... | Annad
and she samidad hit
and the
serstaly to would the had the ware a sompal it his warl on the him and and have to his all some the shings at a mile of thoughs. And he his
are whrouther stell wouse as it her words with him had wounds, his the
called wint,
and wame to mare the witt the
hed and andarisched a than the s... | Apache-2.0 | Recurrent Neural Networks/Character_Level_RNN_Exercise.ipynb | sayakpaul/Favorite-Execises-from-Udacity-s-Deep-Learning-Course |
Loading a checkpoint | # Here we have loaded in a model that trained over 20 epochs `rnn_20_epoch.net`
with open('rnn_sayak_5.net', 'rb') as f:
checkpoint = torch.load(f)
loaded = CharRNN(checkpoint['tokens'], n_hidden=checkpoint['n_hidden'], n_layers=checkpoint['n_layers'])
loaded.load_state_dict(checkpoint['state_dict'])
# Sample ... | And Levin said."
"The was wame
that thet have and shy whis seid wost has as with the
had havled, her
was a shas to seall have the cond whor
had
thile to dere to to this toming and that were to the wourd of
a derited his andad wom and
whith the whas alladide thet and the wert the promass.
The hessing he hive wos there... | Apache-2.0 | Recurrent Neural Networks/Character_Level_RNN_Exercise.ipynb | sayakpaul/Favorite-Execises-from-Udacity-s-Deep-Learning-Course |
Fine-tune T5 locally for machine translation on COVID-19 Health Service Announcements with Hugging Face[](https://studiolab.sagemaker.aws/import/github/aws/studio-lab-examples/blob/main/natural-language-processing/NLP_Disaster_Recovery_Trans... | %%writefile requirements.txt
ipywidgets
git+https://github.com/huggingface/transformers
datasets
sacrebleu
torch
sentencepiece
%pip install -r requirements.txt
import IPython
# make sure to restart your kernel to use the newly install packages
# IPython.Application.instance().kernel.do_shutdown(True) | _____no_output_____ | Apache-2.0 | natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb | dr-natetorious/studio-lab-examples |
Step 1. Explore the available datasets on Translators without Borders Next, download a preferred language pair for training the translation model. This notebook chose English to Spanish, but you can select any two languages.- Overall site page: https://tico-19.github.io/- Page with all language pairs: https://tico-19.... | path_to_my_data = 'https://tico-19.github.io/data/TM/all.en-es-LA.tmx.zip'
!wget {path_to_my_data}
local_file = path_to_my_data.split('/')[-1]
print (local_file)
filename = local_file.split('.zip')[0]
print (filename)
!unzip {local_file} | _____no_output_____ | Apache-2.0 | natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb | dr-natetorious/studio-lab-examples |
Step 2: Extract data from `.tmx` file type Next, you can use this local function to extract data from the `.tmx` file type and format for local training with Hugging Face. | # paste the name of your file and language codes here
source_code_1 = 'en'
target_code_2 = 'es'
def parse_tmx(filename, source_code_1, target_code_2):
'''
Takes a local TMX filename and codes for source and target languages.
Walks through your file, row by row, looking for tmx / html specific formatting.
... | _____no_output_____ | Apache-2.0 | natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb | dr-natetorious/studio-lab-examples |
Step 3. Format extracted data for machine translation with Hugging FaceHugging Face’s translation module is documented on [GitHub](https://github.com/huggingface/transformers/tree/master/examples/pytorch/translation), and their official documentation also includes additional details for [loading the data set](https://... | import pandas as pd
df = pd.read_csv('language_pairs.csv')
df.head() | _____no_output_____ | Apache-2.0 | natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb | dr-natetorious/studio-lab-examples |
The translation task only supports custom [JSONLINES formatted files](https://jsonlines.org/). Each line is a dictionary with a key "translation" and its value another dictionary whose keys are the language pair. For example:```json{ "translation": { "en": "Others have dismissed him as a joke.", "ro": "Alții l-au numit... | objs = []
for idx, row in df.iterrows():
obj = {"translation": {source_code_1: row[source_code_1], target_code_2: row[target_code_2]}}
objs.append(obj)
objs[:5]
import json
!mkdir data
with open('data/train.json', 'w') as f:
for row in objs:
j = json.dumps(row, ensure_ascii = False)
... | _____no_output_____ | Apache-2.0 | natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb | dr-natetorious/studio-lab-examples |
Step 4 - Finetune a machine translation model locallyFourth, download the raw Python file from Hugging Face to fine-tune your model. | !wget https://raw.githubusercontent.com/huggingface/transformers/master/examples/pytorch/translation/run_translation.py
# full hugging face Trainer API args available here
# https://github.com/huggingface/transformers/blob/de635af3f1ef740aa32f53a91473269c6435e19e/src/transformers/training_args.py
# T5 trainig args avai... | _____no_output_____ | Apache-2.0 | natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb | dr-natetorious/studio-lab-examples |
Step 5. Test your newly fine-tuned translation model | from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("t5-small")
model = AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path = 'output/tst-translation')
# line to make sure your model supports local inference
model.eval() | _____no_output_____ | Apache-2.0 | natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb | dr-natetorious/studio-lab-examples |
Fifth, let's test it! Remember that, in using the default settings of only three epochs, your translation is probably not going to be SOTA. For achieving state of the art, (SOTA), we recommend migrating to Amazon SageMaker to scale up and out. Scaling up means moving your code to more advanced compute types, such as th... | input_sequences = ['about how long have these symptoms been going on?',
'and all chest pain should be treated this way especially with your age ',
'and along with a fever ',
'and also needs to be checked your cholesterol blood pressure',
'and are you having a fever now? ',
'and are you having any of the following sym... | _____no_output_____ | Apache-2.0 | natural-language-processing/NLP_Disaster_Recovery_Translation.ipynb | dr-natetorious/studio-lab-examples |
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def load():
fremont_bridge = 'https://data.seattle.gov/api/views/65db-xm6k/rows.csv?accessType=DOWNLOAD'
bicycle_weather = 'https://raw.githubusercontent.com/jakevdp/PythonDataScienceHandbook/master/notebooks/data/Bi... | _____no_output_____ | MIT | LS_DS_243_Model_Interpretation_Assignment.ipynb | bkrant/DS-Unit-2-Sprint-4-Practicing-Understanding | |
[](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb)... | !wget https://setup.johnsnowlabs.com/nlu/colab.sh -O - | bash
import nlu | --2021-05-01 21:59:41-- https://raw.githubusercontent.com/JohnSnowLabs/nlu/master/scripts/colab_setup.sh
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.109.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... ... | Apache-2.0 | nlu/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb | fcivardi/spark-nlp-workshop |
NLU makes NER easy. You just need to load the NER model via ner.load() and predict on some dataset. It could be a pandas dataframe with a column named text or just an array of strings. | import nlu
example_text = ["A person like Jim or Joe",
"An organisation like Microsoft or PETA",
"A location like Germany",
"Anything else like Playstation",
"Person consisting of multiple tokens like Angela Merkel or Donald Trump",
"Organisations consisting of multiple tokens like JP Morgan",
"Locations con... | _____no_output_____ | Apache-2.0 | nlu/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb | fcivardi/spark-nlp-workshop |
Lets explore our data which the predicted NER tags and visalize them! We specify [1:] so we dont se the count for the O-tag wich is the most common, since most words in a sentence are not named entities and thus not part of a chunk | ner_df['entities'].value_counts()[1:].plot.bar(title='Occurence of Named Entity tokens in dataset')
ner_type_to_viz = 'LOC'
ner_df[ner_df.entities_class == ner_type_to_viz]['entities'].value_counts().plot.bar(title='Most often occuring LOC labeled tokens in the dataset')
ner_type_to_viz = 'ORG'
ner_df[ner_df.entities_c... | _____no_output_____ | Apache-2.0 | nlu/colab/component_examples/named_entity_recognition_(NER)/NLU_ner_CONLL_2003_5class_example.ipynb | fcivardi/spark-nlp-workshop |
Calculate Mutual Information Values | num_cols = df.select_dtypes('number').columns.tolist()
cat_cols = df.select_dtypes('object').columns.tolist()
print(f'{len(num_cols)} numerical columns, {len(cat_cols)} categorical columns')
cat_cols
# encoding the categorical columns
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import O... | _____no_output_____ | MIT | docs/Prelim Analysis.ipynb | Cemlyn/CreditRiskVAE |
 Fundamentos de Ciência de Dados ---[](https://zenodo.org/badge/latestdoi/335308405) --- PPGI/UFRJ 2020.3 Prof Sergio Serra e Jorge Zavaleta --- Módulo 2 - Numpy Avançado - Tensores Tensor>Um **tensor** é uma matriz **ndimensional** (narr... | # importando o pacote numpy
import numpy as np | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
Tensores básicos > Tensor escalar ou de categoria "0".> Um escalar contém um único valor e nenhum "eixo". | # tensor escalar
rank_0_tensor = np.array(4,dtype=np.int64)
print('T:',rank_0_tensor)
print('R:',rank_0_tensor.shape) | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
> Tensor de "vetor" ou categoria "1"> Lista de valores. Este tensor tem um eixo. | # tensor de vetor ou categoria 1
rank_1_tensor = np.array([2,3,4],dtype=np.float64)
print('T:',rank_1_tensor)
print('R:',rank_1_tensor.shape) # dimensão da matriz/vetor | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
> Tensor de "Matriz" ou categoria 2> O tensor matriz tem dois eixos. | # tensor de matriz ou categoria 2
rank_2_tensor = np.array([[1,2],[3,4],[5,6]],dtype=np.float64)
print('T:',rank_2_tensor)
print('R:',rank_2_tensor.shape) # dimensão da matriz | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
 | # tensor de matriz pxqxr ou categoria 3
rank_3_tensor = np.array( [[[0, 1, 2, 3, 4],[5, 6, 7, 8, 9]],
[[10, 11, 12, 13, 14],[15, 16, 17, 18, 19]],
[[20, 21, 22, 23, 24],[25, 26, 27, 28, 29]]],dtype=np.float64)
print('T:',rank_3_tensor)
print('R:',rank_3_tensor.shap... | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
 > Sobre as formas:> - **Forma** : O comprimento (número de elementos) de cada um dos eixos de um tensor.> - **Rank** : Número de eixos tensores. Um escalar tem posto 0, um vetor tem posto 1, uma matriz tem posto 2.> - **Eixo ou dimensão** : uma dimensão particular de um tensor.> ... | # exemplo de adicao de tensores
# define o tensor A
A = np.array([[[1,2,3], [4,5,6], [7,8,9]],
[[11,12,13], [14,15,16], [17,18,19]],
[[21,22,23], [24,25,26], [27,28,29]]])
print('Tensor A:')
print(A)
print('D:',A.shape)
# define o tensor B
B = np.array([[[1,2,3], [4,5,6], [7,8,9]],
... | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
Subtração de Tensores> Os tensores devem pertencer à mesma categoria e ter as mesmas dimensões. A substração de tensores é outro tensor. O elemento solução é o resultado da operação entre elementos correspondentes dos tensores. | # tensor subtraction
# define tensor A
A = np.array([[[1,2,3], [4,5,6], [7,8,9]],
[[11,12,13], [14,15,16], [17,18,19]],
[[21,22,23], [24,25,26], [27,28,29]]])
print('Tensor A:')
print(A)
print('D:',A.shape)
# define o tensor B
B = np.array([[[1,2,3], [4,5,6], [7,8,9]],
[[11,12,... | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
Multiplicação de Tensores (hadamart*)> Os tensores devem pertencer à mesma categoria e ter as mesmas dimensões. O produto de tensores é outro tensor. O elemento solução é o resultado da operação entre elementos correspondentes dos tensores. | # define o tensor A
A = np.array([[[1,2,3], [4,5,6], [7,8,9]],
[[11,12,13], [14,15,16], [17,18,19]],
[[21,22,23], [24,25,26], [27,28,29]]])
print('Tensor A:')
print('D:',A.shape)
print(A)
# define tensor B
B = np.array([[[1,2,3], [4,5,6], [7,8,9]],
[[11,12,13], [14,15,16], [17,18,19]... | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
Divisão de Tensores> Os tensores devem pertencer à mesma categoria e ter as mesmas dimensões. A divisão de tensores é outro tensor. O elemento solução é o resultado da operação entre elementos correspondentes dos tensores. | # define o tensor A
A = np.array([[[1,2,3], [4,5,6], [7,8,9]],
[[11,12,13], [14,15,16], [17,18,19]],
[[21,22,23], [24,25,26], [27,28,29]]])
print('Tensor A:')
print('D:',A.shape)
print(A)
# define o tensor B
B = np.array([[[1,2,3], [4,5,6], [7,8,9]],
[[11,12,13], [14,15,16], [17,... | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
Produto de Tensores ($\otimes$)> O produto de tensores de dimensôes diferentes é otro tensor de dimensão (dim(T1)+dim(T2)). Seja o tensor A de **q** dimensões e o tensor B de **r** dimensões. O produto dos tensores C = $A \otimes B$ de **q+r** dimensões. O numpy usa a função **tensordot()** para calcular o tensor prod... | # produto de tensores
# define tensor A (vetor)
A = np.array([1,2])
print('Tensor A:')
print('D:',A.shape)
print(A)
# define tensor B (vetor)
B = np.array([3,4])
print('Tensor B:')
print('D:',B.shape)
print(B)
# calculate tensor produto: C = A x B
C = np.tensordot(A, B, axes=0)
print('Tensor C:')
print('D:',C.shape)
p... | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
TensorFlow>  > **TensorFlow** é uma biblioteca de aprendizado profundo (deep learning) desenvolvida pela Google. Ela fornece primitivas parafunções defininda em **tensores** e cálculos automaticos de suas operações derivadas. O que é um tensor?> Formalmente, os tensores são aplicaçõ... | # definir dois tensores em numpy
Ta = np.zeros((2,2)) # tensor A
Tb = np.ones((2,2)) # Tensor B
print('DTa:',Ta.shape) # dimensão do tensor Ta
print('Ta:')
print(Ta)
print('DTb:',Tb.shape) # dimensão do tensor Tb
print('Tb:')
print(Tb)
sTb = np.sum(Tb, axis=1) # soma do tensor Tb, axis =1 (vetor)
print('Soma Tb:... | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
Exemplo em TensorFlow | # usando o tensorflow
import tensorflow as tf # importar a biblioteca
#
sess = tf.compat.v1.InteractiveSession() # ativa a sessão para tensorflow 2.0 ou inferior
#
ta = tf.zeros((2,2), dtype = tf.float32) # cria o tenso a
print('Tensor A:')
print(ta)
print()
tb = t... | _____no_output_____ | CC0-1.0 | FCD_M2_1_Numpy_avancado.ipynb | zavaleta/Fundamentos_DS |
---_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-text-mining/resources/d9pwm) course resource._--- Assignment 1In this assignm... | import pandas as pd
import numpy as np
import re
doc = []
with open('dates.txt') as file:
for line in file:
doc.append(line)
df = pd.Series(doc)
list(df)
#df.size
'''
def date_sorter1():
# Your code here
dates1 = []
dates2 = []
dates3 = []
for line in df:
match = re.search... | _____no_output_____ | MIT | 4_Applied Text Mining in Python/Assignment 1.ipynb | Pankaj-Ra/Coursera-Data-Science-in-Python |
Contextual Bandits data Load data | import pandas as pd
df = pd.read_csv(r'test_data/cb/01.csv', parse_dates=['t']).set_index('t')
df.head() | _____no_output_____ | BSD-3-Clause | from_mwt_ds/DataScience/estimate.ipynb | cheng-tan/data-science |
Apply estimators | from cb.estimators import ips_snips
def init_ips_snips(r, p, p_log, n):
result = ips_snips()
result.add(r, p_log, p, n * int(p > 0))
return result
policies = ['random', 'baseline1']
for p in policies:
df[p] = df.apply(lambda r: init_ips_snips(r['r'], r[f"('b', '{p}')"], r['p'], r['n']), axis = 1)
df... | _____no_output_____ | BSD-3-Clause | from_mwt_ds/DataScience/estimate.ipynb | cheng-tan/data-science |
Visualize | import matplotlib.pyplot as plt
df.apply(lambda r: r['random'].get('snips'), axis=1).plot(label='random')
df.apply(lambda r: r['baseline1'].get('snips'), axis=1).plot(label='baseline1')
plt.legend(loc='best') | _____no_output_____ | BSD-3-Clause | from_mwt_ds/DataScience/estimate.ipynb | cheng-tan/data-science |
Reaggregate (if needed) | df = df.resample('10min').sum()
df | _____no_output_____ | BSD-3-Clause | from_mwt_ds/DataScience/estimate.ipynb | cheng-tan/data-science |
Visualize | df.apply(lambda r: r['random'].get('snips'), axis=1).plot(label='random')
df.apply(lambda r: r['baseline1'].get('snips'), axis=1).plot(label='baseline1')
plt.legend(loc='best') | _____no_output_____ | BSD-3-Clause | from_mwt_ds/DataScience/estimate.ipynb | cheng-tan/data-science |
Conditional Contextual Bandits | import pandas as pd
df = pd.read_pickle(r'test_data\ccb\01.pickle')
df.head() | _____no_output_____ | BSD-3-Clause | from_mwt_ds/DataScience/estimate.ipynb | cheng-tan/data-science |
Understanding Factors in Animal Shelter Pet Adoption - Data StoryIn efforts to understand trends in pet adoption outcomes, the Austin Animal Center has provided data relating to the pets in their adoption center. Understanding this data and using it to model the factors that influence pet adoption could lead to recomm... | # For working with dataframes and manipulation
import numpy as np
import pandas as pd
# Used to create and customize graphics/plots
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
# Used to work with datetime and timedelta objects
from datetime import datetime, timedelta
# Load the formatted dat... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
1. How Likely are adoptions for Cats vs. Dogs?It is very important to understand the general distributions of outcomes for cats and dogs, as well as the total number of each that the center recieves, in order to efficiently provide resources to shelter these animals. This section will break down the outcomes for both ... | # Separate dataset entries into those for cats and dogs
cats = data[data['Animal Type'] == 'Cat']
dogs = data[data['Animal Type'] == 'Dog']
# Set figure and font size
plt.subplots(figsize=(12, 7))
plt.rc('font', size=14)
# Create pie chart for cat outcomes
plt.subplot(1, 2, 1)
cat_homes = [(cats['Found Home'] == 1).su... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
We can see here that dogs are much more likely to have an outcome resulting in a permanent home than cats.We can also break these outcomes down further by their specific outcome type: | # Set figure and font size
plt.subplots(figsize=(12, 10))
plt.rc('font', size=14)
# Create pie chart for cat outcomes
plt.subplot(1, 2, 1)
cats['Outcome Type'].value_counts().plot(kind='pie',
autopct='%1.1f%%',
labels=None,
... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
While numbers for adoption are similar for both cats and dogs, many more dogs are classified as 'Return to Owner' than cats, which denotes dogs that were lost and returned to their owners. A large majority of cats are transferred to other facilities, which may indicate that other facilities are either better equipped t... | # Separate Cats by Sex upon Outcome
male_n = cats[cats['Sex upon Outcome'] == 'Neutered Male']
male_i = cats[cats['Sex upon Outcome'] == 'Intact Male']
female_s = cats[cats['Sex upon Outcome'] == 'Spayed Female']
female_i = cats[cats['Sex upon Outcome'] == 'Intact Female']
unknown = cats[cats['Sex upon Outcome'] == 'Un... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
The distribution of outcomes for male and females in the cases of both cats and dogs shows that there is not a strong preference for either gender. Naturally, since animals are spayed and neutered when possible at animal shelters, most adoptions occur for these types rather than intact gender animals. ii. Age | # Convert ages to years
cat_ages_in_years = cats['Age upon Outcome'].apply(lambda x: x//timedelta(days=365.25))
dog_ages_in_years = dogs['Age upon Outcome'].apply(lambda x: x//timedelta(days=365.25))
# Plot distribution of cat ages
plt.subplots(figsize=(14,8))
plt.subplot(1, 2, 1)
plt.hist(cat_ages_in_years, bins=23, ... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
There seems to be a wide spread of ages for both cats and dogs, up to 22 years for cats and 20 years for dogs. Most of the animals are less than 4 years old in both cases. It would also be helpful to see the breakdown of outcomes for each of these age groups. | cat_ages_in_years[cats['Found Home'] == 0]
# Plot distribution of cat ages for cats who found homes
plt.subplots(figsize=(14,8))
plt.subplot(1, 2, 1)
cats_fh_freq, cats_bins, _ = plt.hist(cat_ages_in_years[cats['Found Home'] == 1],
bins=23,
c... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
As shown above, we can see that while all age groups have a higher frequency of dogs that are placed/returned to their homes, cats have a more complicated distribution. Both young ( 12 years old) seem to have mixed chances of being placed in a permanent home.One interesting note is that in both cases, the oldest animal... | # Generate Top 10 ranking breeds for cats by frequency
top10_cat_breeds = pd.DataFrame(data= {'Cat Breed': cats['Breed'].value_counts().index.values[:10],
'# of Cats': cats['Breed'].value_counts().values[:10]},
columns = ['Cat Breed', '# of Cats'],
index=range(1,11)
... | 10 Most Common Cat Breeds
Cat Breed # of Cats
Rank
1 Domestic Shorthair Mix 22773
2 Domestic Medium Hair Mix 2257
3 Domestic Longhair Mix 1204
4 Siamese Mix 997
5 Domestic Shorthair 378... | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
In both categories, we can see that mixed breeds are the most common. This is not surprising, though the distribution above shows that the breeds of dogs are much more varied than cats. The 10 most common breeds of dogs only account for about 58% of the total population of dogs that have gone through the center, but f... | plt.subplots(figsize=(14, 8))
# Create plot to show distribution of most common cat breeds by percent of total cats
plt.subplot(1, 2, 1)
(100*cats['Breed'].value_counts()[:10]/cats['Breed'].value_counts().sum()).plot(kind='bar', color='gold', edgecolor='k')
plt.ylabel('% of Total Cats')
plt.xlabel('Breed')
plt.title('... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
In the plot shown above, we can see that Domestic Shorthair mixed breeds in cats account for almost 80% of the cat entries alone. All entries in the most common cat breeds are mixed, since 'Domestic Shorthair' and 'Domestic Medium Hair' breeds are themselves mixed breed classifications.Next we can investigate the outco... | # Find percentage of cats that found homes by breed
cat_breeds_fh = {}
for breed in cats['Breed'].unique():
fh_temp = cats.loc[(cats['Found Home'] == 1) & (cats['Breed'] == breed)].shape[0]
total_temp = cats.loc[cats['Breed'] == breed].shape[0]
cat_breeds_fh[breed] = fh_temp/total_temp
# Create ranking lis... | Cat Breeds with highest percentage of Homes Found
Breed % of Breed that Found Home
Rank
1 Turkish Van Mix 100.0
2 British Shorthair 100.0
3 Devon Rex Mix ... | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
All of the cat breeds with the highest percentages of placement in permanent homes represent breeds that are somewhat exotic when compared to the population that can be found in Austin, TX. We can also see that the Domestic Shorthair Mix that dominates the cat population in this dataset has a fairly low rate of adoptio... | # Find percentage of dogs that found homes by breed
dog_breeds_fh = {}
for breed in dogs['Breed'].unique():
fh_temp = dogs.loc[(dogs['Found Home'] == 1) & (dogs['Breed'] == breed)].shape[0]
total_temp = dogs.loc[dogs['Breed'] == breed].shape[0]
dog_breeds_fh[breed] = fh_temp/total_temp
# Create ranking lis... | Dog Breeds with highest percentage of Homes Found
Breed % of Breed that Found Home
Rank
1 Affenpinscher Mix 100.0
2 Norfolk Terrier 100.0
3 Boerboel 100.0
4 ... | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
The distribution for dogs similarly shows that exotic breeds seem to occupy many of the top ranking spots for adoption rates, although the wide variety of dog breeds also shows that there are many breeds that don't fare well. This might indicate that breed alone is not a good enough indication of the chances of adoptio... | # Cat Breed vs. Mixed
mixed_cats = cats[cats['Breed'].str.contains('Mix')]['Found Home']
pure_cats = cats[~cats['Breed'].str.contains('Mix')]['Found Home']
pure_cats_fh = 100*(pure_cats == 1).sum()/pure_cats.shape[0]
mixed_cats_fh = 100*(mixed_cats == 1).sum()/mixed_cats.shape[0]
# Dog Breed vs. Mixed
mixed_dogs = do... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
We can see that there are two opposing trends for cats and dogs here. For cats, purebreeds have a noticeably higher rate of adoption, while dogs see a drop in adoption rates for those that are not mixed breeds. This may be related to the high occurences of Domestic Shorthair cats at the center. When people come in to b... | # Most Common Colors
plt.subplots(figsize=(14, 8))
# Create plot to show distribution of most common cat colors by percent of total cats
plt.subplot(1, 2, 1)
cat_colors=['brown', 'black', 'orange', 'lightsteelblue', 'white', 'lightsteelblue', 'saddlebrown', 'sandybrown', 'saddlebrown', 'wheat']
(100*cats['Primary Colo... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
Above are the 10 most common colors for both cats and dogs. If we investigate the rates of placement in permanent homes by color, it may be possible to extract information on which color animals are preferred by people looking for pets at the Austin Animal Center. | ## Find percentage of cats that found homes by primary color
cat_colors_fh = {}
for color in cats['Primary Color'].unique():
fh_temp = cats.loc[(cats['Found Home'] == 1) & (cats['Primary Color'] == color)].shape[0]
total_temp = cats.loc[cats['Primary Color'] == color].shape[0]
cat_colors_fh[color] = fh_temp... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
The respective ranks in highest adoption rates for cats and dogs are denoted above the bars for each of the 10 most common colors. We can see again that none of the most common colors for both cats and dogs appear in their respective top lists of adoption rates. This further supports that a sense of exotic appearance o... | # % of Homes Found vs. cats with secondary colors
cats_secondary = cats[cats['Secondary Color'].notnull()]
cats_no_secondary = cats[~cats['Secondary Color'].notnull()]
cats_secondary_fh = 100*(cats_secondary['Found Home'] == 1).sum()/cats_secondary.shape[0]
cats_no_secondary_fh = 100*(cats_no_secondary['Found Home'] =... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
The data above shows that for both cats and dogs, a secondary color slightly improves the rates of adoption. Animals with distinctive color combinations in their coats may stand out more visually to potential pet owners. 3. Analysis of Adoption Outcomes vs. YearFinally, I will take a brief look at the trends of cat an... | print('The dataset covers a time period between {0} and {1}'.format(data['DateTime'].min(), data['DateTime'].max()))
# Reset indices for cats and dogs
cats.reset_index(drop=True, inplace=True)
dogs.reset_index(drop=True, inplace=True)
# Separate data into years and months
cat_years = []
cat_months = []
for cat in ca... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
The graph above shows the average rates of placement in permanent homes for cats and dogs broken down by year. Although dogs have experienced a relative upward trend with time, cats seem to show a drop in rates from 2013 to 2014, but then the same relative upward trend. This may be an anomaly of our dataset, since we o... | # cat outcomes vs. year
cat_month_fh = []
for month in range(1,13):
cat_month = 100*(cats[cat_months == month]['Found Home'] == 1).sum()/(cats[cat_months == month].shape[0])
cat_month_fh.append(cat_month)
# dog outcomes vs. year
dog_month_fh = []
for month in range(1,13):
dog_month = 100*(dogs[dog_months... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
By looking at the average adoption rates broken down by month we can see that for both cats and dogs, there seems to be spikes in adoptions around winter months (Nov. - Feb.) and summer months (Jun. - Aug.). The main difference here is that cats seem to have a much stronger dependence on the month of the year than dogs... | # cat outcomes vs. year
cat_monthyear_fh = []
for year in range(2014,2018):
for month in range(1,13):
cat_month = 100*(cats.loc[(cat_years == year) & (cat_months == month)]['Found Home'] == 1).sum()/(cats.loc[(cat_years == year) & (cat_months == month)].shape[0])
cat_monthyear_fh.append(cat_month)
... | _____no_output_____ | Apache-2.0 | Capstone Project 1/Data Story - Pet Adoption.ipynb | emenriquez/Springboard-Coursework |
Cirq to Tensor NetworksHere we demonstrate turning circuits into tensor network representations of the circuit's unitary, final state vector, final density matrix, and final noisy density matrix. Imports | import cirq
import numpy as np
import pandas as pd
from cirq.contrib.svg import SVGCircuit
import cirq.contrib.quimb as ccq
import quimb
import quimb.tensor as qtn | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
Create a random circuit | qubits = cirq.LineQubit.range(3)
circuit = cirq.testing.random_circuit(qubits, n_moments=10, op_density=0.8, random_state=52)
circuit = cirq.drop_empty_moments(circuit)
SVGCircuit(circuit) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
Circuit to TensorsThe circuit defines a tensor network representation. By default, the initial state is the `|0...0>` state (represented by the "zero qubit" operations labeled "Q0" in the legend. "Q1" are single qubit operations and "Q2" are two qubit operations. The open legs are the indices into the state vector and... | tensors, qubit_frontier, fix = ccq.circuit_to_tensors(circuit, qubits)
tn = qtn.TensorNetwork(tensors)
print(qubit_frontier)
from matplotlib import pyplot as plt
tn.graph(fix=fix, color=['Q0', 'Q1', 'Q2'], figsize=(8,8)) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
To dense | psi_tn = ccq.tensor_state_vector(circuit, qubits)
psi_cirq = cirq.final_state_vector(circuit, qubit_order=qubits)
np.testing.assert_allclose(psi_cirq, psi_tn, atol=1e-7) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
Circuit UnitaryWe can also leave the input legs open which gives a tensor network representation of the unitary | tensors, qubit_frontier, fix = ccq.circuit_to_tensors(circuit, qubits, initial_state=None)
tn = qtn.TensorNetwork(tensors)
print(qubit_frontier)
tn.graph(fix=fix, color=['Q0', 'Q1', 'Q2'], figsize=(8, 8)) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
To dense | u_tn = ccq.tensor_unitary(circuit, qubits)
u_cirq = circuit.unitary(qubit_order=qubits)
np.testing.assert_allclose(u_cirq, u_tn, atol=1e-7) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
Density MatrixWe can also turn a circuit into its density matrix. The density matrix resulting from the evolution of the `|0><0|` initial state can be thought of as two copies of the circuit: one going "forwards" and one going "backwards" (i.e. use the complex conjugate of each operation). Kraus operator noise operati... | tensors, qubit_frontier, fix = ccq.circuit_to_density_matrix_tensors(circuit=circuit, qubits=qubits)
tn = qtn.TensorNetwork(tensors)
tn.graph(fix=fix, color=['Q0', 'Q1', 'Q2']) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
NoiseNoise operations entangle the forwards and backwards evolutions. The new tensors labeled "kQ1" are 1-qubit Kraus operators. | noise_model = cirq.ConstantQubitNoiseModel(cirq.DepolarizingChannel(p=1e-3))
circuit = cirq.Circuit(noise_model.noisy_moments(circuit.moments, qubits))
SVGCircuit(circuit)
tensors, qubit_frontier, fix = ccq.circuit_to_density_matrix_tensors(circuit=circuit, qubits=qubits)
tn = qtn.TensorNetwork(tensors)
tn.graph(fix=fi... | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
For 6 or fewer qubits, we specify the contraction ordering.For low-qubit-number circuits, a reasonable contraction ordering is to go in moment order (as a normal simulator would do). Otherwise, quimb will try to find an optimal ordering which was observed to take longer than it takes to do the contraction itself. We s... | partial = 12
tags_seq = [(f'i{i}b', f'i{i}f') for i in range(partial)]
tn.graph(fix=fix, color = [x for x, _ in tags_seq] + [y for _, y in tags_seq], figsize=(8, 8)) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
The result of a partial contraction | tn2 = tn.contract_cumulative(tags_seq, inplace=False)
tn2.graph(fix=fix, color=['Q0', 'Q1', 'Q2', 'kQ1'], figsize=(8, 8)) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
To Dense | rho_tn = ccq.tensor_density_matrix(circuit, qubits)
rho_cirq = cirq.final_density_matrix(circuit, qubit_order=qubits)
np.testing.assert_allclose(rho_cirq, rho_tn, atol=1e-5) | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
ProfileFor low-qubit-number, deep, noisy circuits, the quimb contraction is faster. | import timeit
def profile(n_qubits: int, n_moments: int):
qubits = cirq.LineQubit.range(n_qubits)
circuit = cirq.testing.random_circuit(qubits, n_moments=n_moments, op_density=0.8)
noise_model = cirq.ConstantQubitNoiseModel(cirq.DepolarizingChannel(p=1e-3))
circuit = cirq.Circuit(noise_model.noisy_mome... | _____no_output_____ | Apache-2.0 | cirq-core/cirq/contrib/quimb/Cirq-to-Tensor-Networks.ipynb | anonymousr007/Cirq |
MNIST Dataset NotebookThe MNIST dataset is a dataset that is built up of hand written digits derived from the NIST dataset. It is used for people to derive machine learning models for pattern recognition from a real world set of data. The MNIST data set is widely used for training image recognition classifiers. The da... | %%html
<style>
table {
display: inline-block
}
</style> | _____no_output_____ | MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
MNIST Dataset File format> All the integers in the files are stored in the MSB first (high endian) format used by most non-Intel processors. Users of Intel processors and other low-endian machines must flip the bytes of the header.The MNIST dataset is stored with the IDX file format extension. The IDX file format is a... | import gzip
# Open zip file with gzip
with gzip.open('data/t10k-images-idx3-ubyte.gz', 'rb') as f:
file_content = f.read()
# Read in first 4 bytes which we know is the magic number
# Convert bytes to int with big endian byte order (most intel CPUs are big endian)
magic = int.from_bytes(file_content[0:4], byt... | Magic number: 2051
Number of images: 10000
Number of rows: 28
Number of columns: 28
| MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
Display an ImageWe will use the matplotlib.pyplot package and the numpy package to display the first image in the dataset. The first image in the dataset is known to be a 7. We can see this by the plot below but can also validate this when we read in the labels from the labels file. | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
# flip the bytes from black/white with tilda(~).
image = ~np.array(list(file_content[16:800])).reshape(28,28).astype(np.uint8)
# use the imshow method and map the image to gray scale
plt.imshow(image, cmap='gray') | _____no_output_____ | MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
Read labels | with gzip.open('data/t10k-labels-idx1-ubyte.gz', 'rb') as f:
labels = f.read()
magic = int.from_bytes(labels[0:4], byteorder="big")
print("Magic number is: ", magic)
# Read in number of labels
num_labels = int.from_bytes(labels[4:8], byteorder="big")
print("Number of labels: ", num_labels)
# Finally read in the ... | Magic number is: 2049
Number of labels: 10000
First label: 7
| MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
Read entire dataset into memoryTo read the dataset into memory we will use gzip as described above. We will then reshape the files into a format that can be used by our model by normalizing the inputs and one-hot encoding the outputs (labels). | import gzip
# Read in entire training set with gzip
with gzip.open('data/train-images-idx3-ubyte.gz', 'rb') as f:
train_img = f.read()
# Read in training labels
with gzip.open('data/train-labels-idx1-ubyte.gz', 'rb') as f:
train_labels = f.read()
# Read in training images
# Convert images from black backgrou... | _____no_output_____ | MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
Build a simple modelWe will build a simple linear model with keras. The model will have an input layer of 784 (number of pixels in a single image) represents an image with dimensions 28 x 28. We will read in all 784 pixels as we reshaped the input array into a two dimensional vector with the dimesionsions 60,000 x 784... | # For building neural networks.
import keras as kr
# For interacting with data sets.
import pandas as pd
# For encoding categorical variables.
import sklearn.preprocessing as pre
# For splitting into training and test sets.
import sklearn.model_selection as mod
# Use keras Sequential model
model = kr.models.Sequent... | _____no_output_____ | MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
One Hot encodingOne Hot encoding is a pre proccessing process for data that is used for ML algorithms. It is mainly used for categorical data such as the MNIST dataset. One hot encoding is used because a majority of ML algorithms as they cannot use label data and they can only read data in a numeric format.In this exa... | # Encode the classes as above.
encoder = pre.LabelBinarizer()
encoder.fit(train_labels)
# One hot encode output labels
outputs = encoder.transform(train_labels)
# first digit is 5, represented by the 6th position in array with value 1
print(train_labels[0], outputs[0]) | 5 [0 0 0 0 0 1 0 0 0 0]
| MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
Example of One hot encoding | # Example of one-hot encoding values from 1-10
for i in range(10):
print(i, encoder.transform([i])) | 0 [[1 0 0 0 0 0 0 0 0 0]]
1 [[0 1 0 0 0 0 0 0 0 0]]
2 [[0 0 1 0 0 0 0 0 0 0]]
3 [[0 0 0 1 0 0 0 0 0 0]]
4 [[0 0 0 0 1 0 0 0 0 0]]
5 [[0 0 0 0 0 1 0 0 0 0]]
6 [[0 0 0 0 0 0 1 0 0 0]]
7 [[0 0 0 0 0 0 0 1 0 0]]
8 [[0 0 0 0 0 0 0 0 1 0]]
9 [[0 0 0 0 0 0 0 0 0 1]]
| MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
Fit the model | # Fit the model with inputs and outputs set number of epochs to 1 and batch size to 100
model.fit(inputs, outputs, epochs=10, batch_size=200) | Epoch 1/10
60000/60000 [==============================] - 10s 172us/step - loss: 14.5262 - acc: 0.0988
Epoch 2/10
60000/60000 [==============================] - 9s 144us/step - loss: 14.5270 - acc: 0.0987
Epoch 3/10
60000/60000 [==============================] - 9s 149us/step - loss: 14.5270 - acc: 0.0987
Epoch 4/10
60... | MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
Test modelTo test the model that we created we will read in the test images and labels files with gzip. | with gzip.open('data/t10k-images-idx3-ubyte.gz', 'rb') as f:
test_img = f.read()
with gzip.open('data/t10k-labels-idx1-ubyte.gz', 'rb') as f:
test_lbl = f.read()
test_img = ~np.array(list(test_img[16:])).reshape(10000, 784).astype(np.uint8) / 255.0
test_lbl = np.array(list(test_lbl[ 8:])).astype(np.uint8... | _____no_output_____ | MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
Model accuracy | acc = (encoder.inverse_transform(model.predict(test_img)) == test_lbl).sum()
print("Accuracy: %.0f%%" % (acc / 100)) | Accuracy: 10%
| MIT | mnist-dataset.ipynb | lanodburke/Emerging-Technologies-Project |
#@title Students'Grade in OOP
student_name = "Ericka Jane A. Alegre" #@param {type:"string"}
prelim = 96#@param {type:'number'}
midterm = 98#@param {type:'number'}
final = 98#@param {type:'number'}
semestral_grade = round((prelim + midterm+ final)/3,2)
print("My prelim grade is " + str(prelim))
print("My midterm grade... | My prelim grade is 96
My midterm grade is 98
My final grade is 98
My semestral grade is: 97.33
My birthdate is: 2003-11-24
| Apache-2.0 | GUI.ipynb | erickaalgr/OOP-1-1 | |
Import Dependencies | # COCO related libraries
from samples.coco import coco
# MaskRCNN libraries
from mrcnn.config import Config
import mrcnn.utils as utils
from mrcnn import visualize
import mrcnn.model as modellib
# Misc
import os
import sys
import json
import numpy as np
import time
from PIL import Image, ImageDraw | Using TensorFlow backend.
| Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Constants | # Number of classes in dataset. Must be of type integer
NUM_CLASSES = 3
# Relative path to .h5 weights file
WEIGHTS_FILE = None
# Relative path to annotations JSON file
TRAIN_ANNOTATIONS_FILE = "datasets/Downtown_Sliced/train/annotations_split.json"
# Relative path to directory of images that pertain to annotations ... | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Additional setup | # Set the ROOT_DIR variable to the root directory of the Mask_RCNN git repo
ROOT_DIR = os.getcwd()
# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")
# Select which GPU to use
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID";
os.environ["CUDA_VISIBLE_DEVICES"]="0"; | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Declare training configuration | class TrainConfig(coco.CocoConfig):
"""
"""
# Give the configuration a recognizable name
NAME = MODEL_NAME
# Train on 1 image per GPU. Batch size is 1 (GPUs * images/GPU).
GPU_COUNT = 1
IMAGES_PER_GPU = 1
# Number of classes (including background)
NUM_CLASSES = 1 + NUM_CLASSES
... | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Display configuration | TrainConfig().display() |
Configurations:
BACKBONE resnet101
BACKBONE_STRIDES [4, 8, 16, 32, 64]
BATCH_SIZE 1
BBOX_STD_DEV [0.1 0.1 0.2 0.2]
COMPUTE_BACKBONE_SHAPE None
DETECTION_MAX_INSTANCES 114
DETECTION_MIN_CONFIDENCE 0.1
DETECTION_NMS_THRESHOLD ... | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Create class to load dataset | class CocoLikeDataset(utils.Dataset):
""" Generates a COCO-like dataset, i.e. an image dataset annotated in the style of the COCO dataset.
See http://cocodataset.org/#home for more information.
"""
def load_data(self, annotation_json, images_dir):
""" Load the coco-like dataset from json
... | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Load train and validation datasets | dataset_train = CocoLikeDataset()
dataset_train.load_data(TRAIN_ANNOTATIONS_FILE, TRAIN_ANNOTATION_IMAGE_DIR)
dataset_train.prepare()
dataset_val = CocoLikeDataset()
dataset_val.load_data(VALIDATION_ANNOTATIONS_FILE, VALIDATION_ANNOTATION_IMAGE_DIR)
dataset_val.prepare() | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Build MaskRCNN Model | # Create model in training mode
model = modellib.MaskRCNN(mode = "training", config = TrainConfig(), model_dir = MODEL_DIR) | WARNING: Logging before flag parsing goes to stderr.
W0805 20:55:55.028046 139871543367488 deprecation_wrapper.py:119] From /home/venv/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:508: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.
W0805 20:55:55.066967 139871543... | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Load weights into model if weights file is not None This is meant to be used if you are refining on a set of preexisting weights | if WEIGHTS_FILE is not None:
model.load_weights(WEIGHTS_FILE, by_name = True) | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Train model The model after each epoch will be saved in the logs folder | start_train = time.time()
model.train(dataset_train, dataset_val, learning_rate = TrainConfig().LEARNING_RATE, epochs = NUM_EPOCHS, layers = 'all')
end_train = time.time()
minutes = round((end_train - start_train) / 60, 2)
print(f'Training took {minutes} minutes') |
Starting at epoch 0. LR=0.001
Checkpoint Path: /home/logs/model_220190805T2055/mask_rcnn_model_2_{epoch:04d}.h5
Selecting layers to train
conv1 (Conv2D)
bn_conv1 (BatchNorm)
res2a_branch2a (Conv2D)
bn2a_branch2a (BatchNorm)
res2a_branch2b (Conv2D)
bn2a_branch2b ... | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Include evaluation scripts in training script so that the kernel does not have to be reloaded. Eases the process of rapidly training and evaluating models Import dependencies | # COCO related libraries
from pycocotools.cocoeval import COCOeval
from pycocotools import mask as maskUtils
from samples.coco import coco
from samples.coco.coco import evaluate_coco
from pycocotools.coco import COCO
# MaskRCNN libraries
import mrcnn.model as modellib
import mrcnn.utils as utils
import mrcnn.visualize... | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Declare evaluation configuration | class EvalConfig(coco.CocoConfig):
""" Configuration for evaluation """
# Give the configuration a recognizable name
NAME = MODEL_NAME
# How many GPUs
GPU_COUNT = 1
# How many images per gpu
IMAGES_PER_GPU = 1
# Number of classes (including background)
NUM_CLASSES = 1... | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Display configuration | EvalConfig().display() |
Configurations:
BACKBONE resnet101
BACKBONE_STRIDES [4, 8, 16, 32, 64]
BATCH_SIZE 1
BBOX_STD_DEV [0.1 0.1 0.2 0.2]
COMPUTE_BACKBONE_SHAPE None
DETECTION_MAX_INSTANCES 114
DETECTION_MIN_CONFIDENCE 0.1
DETECTION_NMS_THRESHOLD ... | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Build class to load ground truth data | class CocoDataset(utils.Dataset):
def load_coco_gt(self, annotations_file, dataset_dir):
"""Load a COCO styled ground truth dataset
"""
# Create COCO object
coco = COCO(annotations_file)
# Load all classes
class_ids = sorted(coco.getCatIds())
# Load... | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Build MaskRCNN Model | # Create the model in inference mode
model = modellib.MaskRCNN(mode = "inference", config = EvalConfig(), model_dir = MODEL_DIR) | W0806 02:20:35.781523 139871543367488 deprecation_wrapper.py:119] From /home/venv/src/mask-rcnn/mrcnn/model.py:720: The name tf.sets.set_intersection is deprecated. Please use tf.sets.intersection instead.
W0806 02:20:35.898080 139871543367488 deprecation.py:323] From /home/venv/src/mask-rcnn/mrcnn/model.py:772: to_fl... | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Load weights from last trained model | # Load weights by name
model.load_weights(model.find_last(), by_name = True) | Re-starting from epoch 80
| Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Load dataset | # Relative path to ground truth annotations JSON file
GT_ANNOTATIONS_FILE = "datasets/Downtown_Sliced/test/annotations_split.json"
# Relative path to images associated with ground truth JSON file
GT_DATASET_DIR = "datasets/Downtown_Sliced/test/images"
dataset_val = CocoDataset()
coco = dataset_val.load_coco_gt(annota... | loading annotations into memory...
Done (t=1.24s)
creating index...
index created!
| Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Evaluate model with COCO test If your results come back as a bunch of zeros, check to make sure that the "width" and "height" tag in your COCO dataset are correct | evaluate_coco(model, dataset_val, coco, "segm") | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Calculating mAP as per example in train_shapes.ipynb | # Compute VOC-Style mAP @ IoU=0.5
# Running on 10 images. Increase for better accuracy.
image_ids = np.random.choice(dataset_val.image_ids, len(dataset_val.image_ids))
# Instanciate arrays to create our metrics
APs = []
precisions_arr = []
recalls_arr = []
overlaps_arr = []
class_ids_arr = []
scores_arr = []
for id i... | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
Plot precision recall curve | visualize.plot_precision_recall(AP, precisions, recalls) | _____no_output_____ | Apache-2.0 | Train.ipynb | MrDemeanor/AerialPipeline |
#authenticatiopn script in gcp
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!apt-get install software-properties-common
!apt-get install -y -qq software-properties-common module-init-tools
!apt-get install -y -qq python-software-properties module-init-tools
!add-ap... | _____no_output_____ | MIT | FileOperations.ipynb | DannyML-DSC/Hash-analytics | |
Making a version of *Quantum Rogues*[*Quantum Rogues*](https://github.com/qiskit-community/qiskit-camp-europe-19/issues/5) was a game created at the [Qiskit Camp Europe](https://qiskit.camp/europe/) hackathon in September 2019. In this notebook we create a new game, which takes the basic idea of the original *Quantum ... | %matplotlib notebook | _____no_output_____ | Apache-2.0 | versions/MicroPython/tutorials/Quantum-Rogues.ipynb | TigrisCallidus/MicroQiskit |
In this game, the player controls a flying avatar that can move around a room. I think of it as a bat - a qubat, in fact - but you can interpret it as you wish.First we simply create the room and the bat, and set up the controls. The **O** button makes the bat flap its wings. The ◀︎ and ► buttons move left and right. | import pew
pew.init()
screen = pew.Pix()
# create a border of B=2 pixels
for A in range(8):
for B in [0,7]:
screen.pixel(A,B,2)
screen.pixel(B,A,2)
# the player is a B=3 pixel
X,Y = 4,6
screen.pixel(X,Y,3)
while True: # loop which checks for user input and responds
# use key presses to det... | _____no_output_____ | Apache-2.0 | versions/MicroPython/tutorials/Quantum-Rogues.ipynb | TigrisCallidus/MicroQiskit |
Each room will have doors, through which it can move to neighbouring rooms. There are four possible doors: one in each direction.Whether a door is open or closed is generated randomly from a two qubit quantum circuit. The left door depends on the result from one of the qubits when a z measurement is made: `0` for open ... | import pew
from microqiskit import *
pew.init()
screen = pew.Pix()
# set positions of doors
l = (0,4)
r = (7,4)
u = (3,0)
d = (3,7)
doors = [l,r,u,d]
# create a border of B=2 pixels
for A in range(8):
for B in [0,7]:
screen.pixel(A,B,2)
screen.pixel(B,A,2)
# the player is a B=3 pixel
X,Y = 4,6
... | _____no_output_____ | Apache-2.0 | versions/MicroPython/tutorials/Quantum-Rogues.ipynb | TigrisCallidus/MicroQiskit |
Obviously what *should* happen when you pass through a door is that you end up in another room. This is implemented in the program below. The new room will again has open and closed doors, generated randomly by the circuit. | import pew
from microqiskit import *
pew.init()
screen = pew.Pix()
# set positions of doors
l = (0,4)
r = (7,4)
u = (3,0)
d = (3,7)
doors = [l,r,u,d]
# create a border of B=2 pixels
for A in range(8):
for B in [0,7]:
screen.pixel(A,B,2)
screen.pixel(B,A,2)
# the player is a B=3 pixel
X,Y = 4,6
... | _____no_output_____ | Apache-2.0 | versions/MicroPython/tutorials/Quantum-Rogues.ipynb | TigrisCallidus/MicroQiskit |
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