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 |
|---|---|---|---|---|---|
Custom sorting of plot series | import pandas as pd
import numpy as np
from pandas.api.types import CategoricalDtype
from plotnine import *
from plotnine.data import mpg
%matplotlib inline | _____no_output_____ | MIT | demo_plot/plotnine-examples/tutorials/miscellaneous-order-plot-series.ipynb | hermanzhaozzzz/DataScienceScripts |
Bar plot of manufacturer - Default Output | (ggplot(mpg)
+ aes(x='manufacturer')
+ geom_bar(size=20)
+ coord_flip()
+ labs(y='Count', x='Manufacturer', title='Number of Cars by Make')
) | _____no_output_____ | MIT | demo_plot/plotnine-examples/tutorials/miscellaneous-order-plot-series.ipynb | hermanzhaozzzz/DataScienceScripts |
Bar plot of manufacturer - Ordered by count (Categorical)By default the discrete values along axis are ordered alphabetically. If we want aspecific orderingwe use a pandas.Categorical variable with categories ordered to our preference. | # Determine order and create a categorical type
# Note that value_counts() is already sorted
manufacturer_list = mpg['manufacturer'].value_counts().index.tolist()
manufacturer_cat = pd.Categorical(mpg['manufacturer'], categories=manufacturer_list)
# assign to a new column in the DataFrame
mpg = mpg.assign(manufacturer... | _____no_output_____ | MIT | demo_plot/plotnine-examples/tutorials/miscellaneous-order-plot-series.ipynb | hermanzhaozzzz/DataScienceScripts |
We could also modify the **existing manufacturer category** to set it as orderedinstead of having to create a new CategoricalDtype and apply that to the data. | mpg = mpg.assign(manufacturer_cat =
mpg['manufacturer'].cat.reorder_categories(manufacturer_list)) | _____no_output_____ | MIT | demo_plot/plotnine-examples/tutorials/miscellaneous-order-plot-series.ipynb | hermanzhaozzzz/DataScienceScripts |
Bar plot of manufacturer - Ordered by count (limits)Another method to quickly reorder a discrete axis without changing the data is to change it's limits | # Determine order and create a categorical type
# Note that value_counts() is already sorted
manufacturer_list = mpg['manufacturer'].value_counts().index.tolist()
(ggplot(mpg)
+ aes(x='manufacturer_cat')
+ geom_bar(size=20)
+ scale_x_discrete(limits=manufacturer_list)
+ coord_flip()
+ labs(y='Count', x='Manufactu... | _____no_output_____ | MIT | demo_plot/plotnine-examples/tutorials/miscellaneous-order-plot-series.ipynb | hermanzhaozzzz/DataScienceScripts |
You can 'flip' an axis (independent of limits) by reversing the order of the limits. | # Determine order and create a categorical type
# Note that value_counts() is already sorted
manufacturer_list = mpg['manufacturer'].value_counts().index.tolist()[::-1]
(ggplot(mpg)
+ aes(x='manufacturer_cat')
+ geom_bar(size=20)
+ scale_x_discrete(limits=manufacturer_list)
+ coord_flip()
+ labs(y='Count', x='Man... | _____no_output_____ | MIT | demo_plot/plotnine-examples/tutorials/miscellaneous-order-plot-series.ipynb | hermanzhaozzzz/DataScienceScripts |
Breast Cancer Wisconsin (Diagnostic) Prediction*Predict whether the cancer is benign or malignant*Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. n the 3-dimensional space is that described in: [K. P. ... | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
import seaborn as sns # used for plot interactive graph. I like it most for plot
from sklearn.linear_model import LogisticRegression # to apply the Logistic regression
from s... | _____no_output_____ | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
Data Cleaning | #df is dataframe and its self a variable & here i import the dataset into this variable
df=pd.read_csv("cancer.csv")
#it will show top 5 data rows
df.head()
#deleting the useless columns
df.drop(['id'], 1, inplace=True)
df.drop(['Unnamed: 32'], 1, inplace=True)
df.info()
# lets now start with features_mean
# now as ou... | _____no_output_____ | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
Data Analysis | # plotting the diagonisis result
sns.countplot(df['diagnosis'],label="Count")
feat_mean= list(df.columns[1:11])
feat_se= list(df.columns[11:20])
feat_worst=list(df.columns[21:31])
corr = df[feat_mean].corr() # .corr is used for find corelation
plt.figure(figsize=(14,14))
sns.heatmap(corr, cbar = True, square = True,... | _____no_output_____ | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
Spliting the Dataset into two part | # spliting the dataset into two part
train_set,test_set=train_test_split(df, test_size=0.2)
#printing the data shape
print(train_set.shape)
print(test_set.shape) | (455, 31)
(114, 31)
| MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
**Training Set :** | x_train=train_set[main_pred_var]
y_train=train_set.diagnosis
print(y_train.shape)
print(x_train.shape) | (455,)
(455, 5)
| MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
**Test Set :** | x_test=test_set[main_pred_var]
y_test=test_set.diagnosis
print(y_test.shape)
print(x_test.shape) | (114,)
(114, 5)
| MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
Various AlgorithmNow i will train this Breast cancer dataset using various Algorithm from scratch see how each of them behaves with respect to one another. Random Forest SVM RandomForst Algorithm | #define the algorithm class into the algo_one variable
algo_one=RandomForestClassifier()
algo_one.fit(x_train,y_train)
#predicting the algorithm into the non trained dataset that is test set
prediction = algo_one.predict(x_test)
metrics.accuracy_score(prediction,y_test) | _____no_output_____ | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
SupportVector Machine Algorithm (SVM) | algo_two=svm.SVC()
algo_two.fit(x_train,y_train)
#predicting the algorithm into the non trained dataset that is test set
prediction = algo_two.predict(x_test)
metrics.accuracy_score(prediction,y_test) | _____no_output_____ | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
Decision Tree Classifier Algorithm | algo_three=DecisionTreeClassifier()
algo_three.fit(x_train,y_train)
#predicting the algorithm into the non trained dataset that is test set
prediction = algo_three.predict(x_test)
metrics.accuracy_score(prediction,y_test) | _____no_output_____ | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
K-Nearest NeighborsClassifier Algorithm | algo_four=KNeighborsClassifier()
algo_four.fit(x_train,y_train)
#predicting the algorithm into the non trained dataset that is test set
prediction = algo_four.predict(x_test)
metrics.accuracy_score(prediction,y_test) | _____no_output_____ | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
GaussianNB Algorithm | algo_five=GaussianNB()
algo_five.fit(x_train,y_train)
prediction = algo_five.predict(x_test)
metrics.accuracy_score(prediction,y_test) | _____no_output_____ | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
Tuning Parameters using grid search CVLets Start with Random Forest Classifier Tuning the parameters means using the best parameter for predict there are many parameters need to model a Machine learning Algorithm for RandomForestClassifier. | pred_var = ['radius_mean','perimeter_mean','area_mean','compactness_mean','concave points_mean']
#creating new variable
x_grid= df[pred_var]
y_grid= df["diagnosis"]
# lets Make a function for Grid Search CV
def Classification_model_gridsearchCV(model,param_grid,x_grid,y_grid):
clf = GridSearchCV(model,param_grid,cv... | The best parameter found on development set is :
{'max_features': 'log2', 'min_samples_leaf': 2, 'min_samples_split': 6}
the bset estimator is
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='log2', max_leaf_nodes=None,
min_impurity_decre... | MIT | Breast Cancer .ipynb | suvhradipghosh07/Breast-Cancer-prediction-using-Machine-Learning-Various-Algorithms |
๋จ์ํ ๊ธฐ์ธ๊ธฐ ๊ณ์ฐ - z = 2x^2+3 | # ๋จผ์ ํ์ดํ ์น๋ฅผ ๋ถ๋ฌ์ต๋๋ค.
import torch
# x๋ฅผ [2.0,3.0]์ ๊ฐ์ ๊ฐ์ง ํ
์๋ก ์ด๊ธฐํ ํด์ฃผ๊ณ ๊ธฐ์ธ๊ธฐ ๊ณ์ฐ์ True๋ก ์ผ ๋์ต๋๋ค.
# z = 2x^2+3
x = torch.tensor(data=[2.0,3.0],requires_grad=True)
y = x**2
z = 2*y +3
# https://pytorch.org/docs/stable/autograd.html?highlight=backward#torch.autograd.backward
# ๋ชฉํ๊ฐ์ ์ง์ ํฉ๋๋ค.
target = torch.tensor([3.0,4.0])
# z์ ... | tensor([ 8., 12.]) None None
| MIT | Linear Regression Analysis/Calculate Gradients.ipynb | TaehoLi/Pytorch-secondstep |
Step1. Define the function wich integrate with. |
import numpy as np
def f(x):
return np.log(3+x)
| _____no_output_____ | MIT | integration.ipynb | henrymorenoespitia/numerical_methods_and_analysis |
Step 2define functions for diferent methods |
# Simple Trapezoid
def simpleTrapezoid(a, b):
return (b-a) * (f(a) + f(b))
def simpleTrapezoidError(realIntegrate, a, b):
return (abs(realIntegrate - simpleTrapezoid(a, b)) / realIntegrate ) * 100
# compound trapezoid
def compoundTrapezoid(a, b, x):
sum = 0
for i in range(1, n):
sum += 2 * f(x[i... | _____no_output_____ | MIT | integration.ipynb | henrymorenoespitia/numerical_methods_and_analysis |
Step 3 Define entry values | # code proof
a = 0.1
b = 3.1
realIntegrate = -5.82773
| _____no_output_____ | MIT | integration.ipynb | henrymorenoespitia/numerical_methods_and_analysis |
a. proof of simple rules |
# simple trapezoid
print(f"La integral aproximada por la regla de trapecio simple es: {simpleTrapezoid(a, b)} ; error (%): {simpleTrapezoidError(realIntegrate, a, b)} ")
#simple Simpson 1/3
n= 2
x = np.linspace(a, b, n+1)
print(f"La integral aproximada por la regla de Simpson 1/3 simle es: {simpleSimpson1_3(x)} : er... | La integral aproximada por la regla de trapecio simple es: 8.819072648011097 ; error (%): -251.32946529799932
La integral aproximada por la regla de Simpson 1/3 simle es: 4.521958048325281 : error (%): -1.7759381523037754
La integral aproximada por la regla de Simpson 3/8 simple es: 4.522640033621997 ; error (%): -1.... | MIT | integration.ipynb | henrymorenoespitia/numerical_methods_and_analysis |
a. proof of compound rules |
# compound trapezoid
n= 18 # number of segments
x = np.linspace(a, b, n+1)
print(f"La integral aproximada por la regla de trapecio compuesto es: {compoundTrapezoid(a, b, x)} ; error (%): {compoundTrapezoidError(realIntegrate, a, b, x)} ")
#compound Simpson 1/3
n= 18 # even number
x = np.linspace(a, b, n+1)
print(f"La... | La integral aproximada por la regla de trapecio compuesto es: 4.522847784399211 ; error (%): -177.6090825141043
La integral aproximada por la regla de Simpson 1/3 compuesta es: 4.523214709695509 : error (%): -1.776153787099867
La integral aproximada por la regla de Simpson 3/8 compuesta es: 4.523214401106994 ; error ... | MIT | integration.ipynb | henrymorenoespitia/numerical_methods_and_analysis |
Step 5: Gauss quadrature to 2 and 3 points |
# 2 points
xd_0 = -0.577350269
xd_1 = 0.577350269
C0 = 1
C1 = 1
dx = ( b- a ) / 2
x0 = ((b+a) + (b-a) * xd_0 )/ 2
x1 = ((b+a) + (b-a) * xd_1)/ 2
F0 = f(x0) * dx
F1 = f(x1) * dx
integralGaussQuadrature = C0 *F0 + C1 * F1
integralGaussQuadratureError = (abs(realIntegrate - integralGaussQuadrature) / realIntegrate) *... | la integral aproximada por el metodo de cuadratura de Gauss con 2 puntos es: 4.5240374652688375 ; y el error (%): -177.62949665253603
la integral aproximada por el metodo de cuadratura de Gauss con 3 puntos es: 4.52323074338855 ; y el error (%): -177.6156538375757
| MIT | integration.ipynb | henrymorenoespitia/numerical_methods_and_analysis |
Example with real audio recordingsThe iterations are dropped in contrast to the offline version. To use past observations the correlation matrix and the correlation vector are calculated recursively with a decaying window. $\alpha$ is the decay factor. Setup | channels = 8
sampling_rate = 16000
delay = 3
alpha=0.9999
taps = 10
frequency_bins = stft_options['size'] // 2 + 1 | _____no_output_____ | MIT | examples/WPE_Numpy_online.ipynb | mdeegen/nara_wpe |
Audio data | file_template = 'AMI_WSJ20-Array1-{}_T10c0201.wav'
signal_list = [
sf.read(str(project_root / 'data' / file_template.format(d + 1)))[0]
for d in range(channels)
]
y = np.stack(signal_list, axis=0)
IPython.display.Audio(y[0], rate=sampling_rate) | _____no_output_____ | MIT | examples/WPE_Numpy_online.ipynb | mdeegen/nara_wpe |
Online bufferFor simplicity the STFT is performed before providing the frames.Shape: (frames, frequency bins, channels)frames: K+delay+1 | Y = stft(y, **stft_options).transpose(1, 2, 0)
T, _, _ = Y.shape
def aquire_framebuffer():
buffer = list(Y[:taps+delay+1, :, :])
for t in range(taps+delay+1, T):
yield np.array(buffer)
buffer.append(Y[t, :, :])
buffer.pop(0) | _____no_output_____ | MIT | examples/WPE_Numpy_online.ipynb | mdeegen/nara_wpe |
Non-iterative frame online approachA frame online example requires, that certain state variables are kept from frame to frame. That is the inverse correlation matrix $\text{R}_{t, f}^{-1}$ which is stored in Q and initialized with an identity matrix, as well as filter coefficient matrix that is stored in G and initial... | Z_list = []
Q = np.stack([np.identity(channels * taps) for a in range(frequency_bins)])
G = np.zeros((frequency_bins, channels * taps, channels))
for Y_step in tqdm(aquire_framebuffer()):
Z, Q, G = online_wpe_step(
Y_step,
get_power_online(Y_step.transpose(1, 2, 0)),
Q,
G,
a... | _____no_output_____ | MIT | examples/WPE_Numpy_online.ipynb | mdeegen/nara_wpe |
Frame online WPE in class fashion: Online WPE class holds the correlation Matrix and the coefficient matrix. | Z_list = []
online_wpe = OnlineWPE(
taps=taps,
delay=delay,
alpha=alpha
)
for Y_step in tqdm(aquire_framebuffer()):
Z_list.append(online_wpe.step_frame(Y_step))
Z = np.stack(Z_list)
z = istft(np.asarray(Z).transpose(2, 0, 1), size=stft_options['size'], shift=stft_options['shift'])
IPython.display.Audi... | _____no_output_____ | MIT | examples/WPE_Numpy_online.ipynb | mdeegen/nara_wpe |
Power spectrumBefore and after applying WPE. | fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(20, 8))
im1 = ax1.imshow(20 * np.log10(np.abs(Y[200:400, :, 0])).T, origin='lower')
ax1.set_xlabel('')
_ = ax1.set_title('reverberated')
im2 = ax2.imshow(20 * np.log10(np.abs(Z_stacked[200:400, :, 0])).T, origin='lower')
_ = ax2.set_title('dereverberated')
cb = fig.colorbar... | _____no_output_____ | MIT | examples/WPE_Numpy_online.ipynb | mdeegen/nara_wpe |
_Lambda School Data Science_ Scrape and process dataObjectives- scrape and parse web pages- use list comprehensions- select rows and columns with pandasLinks- [Automate the Boring Stuff with Python, Chapter 11](https://automatetheboringstuff.com/chapter11/) - Requests - Beautiful Soup- [Python List Comprehensions: E... | url = 'https://us.pycon.org/2018/schedule/talks/list/'
| _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling |
5 ways to look at long titlesLet's define a long title as greater than 80 characters 1. For Loop | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
2. List Comprehension | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
3. Filter with named function | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
4. Filter with anonymous function | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
5. Pandaspandas documentation: [Working with Text Data](https://pandas.pydata.org/pandas-docs/stable/text.html) | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Make new dataframe columnspandas documentation: [apply](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.apply.html) | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
title length | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
long title | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
first letter | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
word countUsing [`textstat`](https://github.com/shivam5992/textstat) | #!pip install textstat
| _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling |
Rename column`title length` --> `title character count`pandas documentation: [rename](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.rename.html) | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Analyze the dataframe Describepandas documentation: [describe](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.describe.html) | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Sort valuespandas documentation: [sort_values](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html) Five shortest titles, by character count | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Titles sorted reverse alphabetically | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Get value countspandas documentation: [value_counts](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) Frequency counts of first letters | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Percentage of talks with long titles | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Plotpandas documentation: [Visualization](https://pandas.pydata.org/pandas-docs/stable/visualization.html) Top 5 most frequent first letters | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Histogram of title lengths, in characters | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling | |
Assignment**Scrape** the talk descriptions. Hint: `soup.select('.presentation-description')`**Make** new columns in the dataframe:- description- description character count- description word count- description grade level (use [this `textstat` function](https://github.com/shivam5992/textstatthe-flesch-kincaid-grade-le... | import bs4
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
result = requests.get(url)
soup = bs4.BeautifulSoup(result.text)
descriptions = [tag.text.strip()
for tag in soup.select('.presentation-description')]
print (len(descriptions))
print (descriptions)
df = pd.D... | _____no_output_____ | MIT | module1-scrape-and-process-data/LS_DS_121_Scrape_and_process_data.ipynb | wjarvis2/DS-Unit-1-Sprint-2-Data-Wrangling |
SpecificationsThese parameters need to be specified prior to running the predictive GAN. | # modes
gp = False
sn = True
# steps
train = False
fixed_input = True
eph = '9999' # the model to read if not training
# paramters
DATASET = 'sine' # sine, moon, 2spirals, circle, helix
suffix = '_sn' # suffix of output folder
LATENT_DIM = 2 # latent space dimension
DIM = 512 # 512 Model dimensionality
INPUT_DIM =... | _____no_output_____ | MIT | gan_toy_example.ipynb | acse-wx319/gans-on-multimodal-data |
Make generator and discriminatorInitializing generator and discriminator objects. The architectures have been declared in lib.models. | netG = Generator(LATENT_DIM, DIM, DROPOUT_RATE, INPUT_DIM)
if sn:
netD = DiscriminatorSN(DIM, INPUT_DIM)
else:
netD = Discriminator(DIM, INPUT_DIM)
netG.apply(weights_init)
netD.apply(weights_init) | _____no_output_____ | MIT | gan_toy_example.ipynb | acse-wx319/gans-on-multimodal-data |
Train or load modelIf in training mode, a WGAN with either SN or GP will be trained on the specified type of synthetic data (sine, circle, half-moon, helix or double-spirals). A log file will be created with all specifications to keep track of the runs. Loss will be plotted against the number of epochs. Randomly gener... | if train:
# start writing log
f = open(TMP_PATH + "log.txt", "w")
# print specifications
f.write('gradient penalty: ' + str(gp))
f.write('\n spectral normalization: ' + str(sn))
f.write('\n datasest: ' + DATASET)
f.write('\n hidden layer dimension: ' + str(DIM))
f.write('\n latent space ... | _____no_output_____ | MIT | gan_toy_example.ipynb | acse-wx319/gans-on-multimodal-data |
PredictionFor a list of x, use the trained GAN to make multiple predictions for y. For each x, many predictions will be made. A subset is taken depending on the similarity between the generated x and the specified x. | if fixed_input:
data = make_data_iterator(DATASET, BATCH_SIZE)
# sine data
preds = None
for x in np.linspace(-4., 4., 17):
print(x)
out = predict_fixed(netG, x, 80, 8, INPUT_DIM, LATENT_DIM, use_cuda)
if preds == None:
preds = out
else:
preds = to... | -4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
| MIT | gan_toy_example.ipynb | acse-wx319/gans-on-multimodal-data |
A pplication Programming Interface (API) An API lets two pieces of software talk to each other. Just like a function, you donโt have to know how the API works only its inputs and outputs. An essential type of API is a REST API that allows you to access resources via the internet. In this lab, we will review the ... | !pip install nba_api | Collecting nba_api
[?25l Downloading https://files.pythonhosted.org/packages/fd/94/ee060255b91d945297ebc2fe9a8672aee07ce83b553eef1c5ac5b974995a/nba_api-1.1.8-py3-none-any.whl (217kB)
[K |โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 225kB 2.7MB/s
[?25hRequirement already satisfied: requests in /usr/local/lib/python3.6/dis... | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
Pandas is an API You will use this function in the lab: | def one_dict(list_dict):
keys=list_dict[0].keys()
out_dict={key:[] for key in keys}
for dict_ in list_dict:
for key, value in dict_.items():
out_dict[key].append(value)
return out_dict | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
Pandas is an API Pandas is actually set of software components , much of witch is not even written in Python. | import pandas as pd
import matplotlib.pyplot as plt | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
You create a dictionary, this is just data. | dict_={'a':[11,21,31],'b':[12,22,32]} | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
When you create a Pandas object with the Dataframe constructor in API lingo, this is an "instance". The data in the dictionary is passed along to the pandas API. You then use the dataframe to communicate with the API. | df=pd.DataFrame(dict_)
type(df) | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
When you call the method head the dataframe communicates with the API displaying the first few rows of the dataframe. | df.head() | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
When you call the method mean,the API will calculate the mean and return the value. | df.mean() | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
REST APIs Rest APIโs function by sending a request, the request is communicated via HTTP message. The HTTP message usually contains a JSON file. This contains instructions for what operation we would like the service or resource to perform. In a similar manner, API returns a response, via an HTTP message, this respons... | from nba_api.stats.static import teams
import matplotlib.pyplot as plt
#https://pypi.org/project/nba-api/ | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
The method get_teams() returns a list of dictionaries the dictionary key id has a unique identifier for each team as a value | nba_teams = teams.get_teams() | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
The dictionary key id has a unique identifier for each team as a value, let's look at the first three elements of the list: | nba_teams[0:3] | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
To make things easier, we can convert the dictionary to a table. First, we use the function one dict, to create a dictionary. We use the common keys for each team as the keys, the value is a list; each element of the list corresponds to the values for each team.We then convert the dictionary to a dataframe, each row c... | dict_nba_team=one_dict(nba_teams)
df_teams=pd.DataFrame(dict_nba_team)
df_teams.head() | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
Will use the team's nickname to find the unique id, we can see the row that contains the warriors by using the column nickname as follows: | df_warriors=df_teams[df_teams['nickname']=='Warriors']
df_warriors | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
we can use the following line of code to access the first column of the dataframe: | id_warriors=df_warriors[['id']].values[0][0]
#we now have an integer that can be used to request the Warriors information
id_warriors | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
The function "League Game Finder " will make an API call, its in the module stats.endpoints | from nba_api.stats.endpoints import leaguegamefinder | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
The parameter team_id_nullable is the unique ID for the warriors. Under the hood, the NBA API is making a HTTP request. The information requested is provided and is transmitted via an HTTP response this is assigned to the object gamefinder. | # Since https://stats.nba.com does lot allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.
# The following code is comment out, you can run it on jupyter labs on your own computer.
# gamefinder = leaguegamefinder.LeagueGameFinder(team_id_nullable=id_warriors) | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
we can see the json file by running the following line of code. | # Since https://stats.nba.com does lot allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.
# The following code is comment out, you can run it on jupyter labs on your own computer.
# gamefinder.get_json() | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
The game finder object has a method get_data_frames(), that returns a dataframe. If we view the dataframe, we can see it contains information about all the games the Warriors played. The PLUS_MINUS column contains information on the score, if the value is negative the Warriors lost by that many points, if the value i... | # Since https://stats.nba.com does lot allow api calls from Cloud IPs and Skills Network Labs uses a Cloud IP.
# The following code is comment out, you can run it on jupyter labs on your own computer.
# games = gamefinder.get_data_frames()[0]
# games.head() | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
you can download the dataframe from the API call for Golden State and run the rest like a video. | ! wget https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%205/Labs/Golden_State.pkl
file_name = "Golden_State.pkl"
games = pd.read_pickle(file_name)
games.head() | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
We can create two dataframes, one for the games that the Warriors faced the raptors at home and the second for away games. | games_home=games [games ['MATCHUP']=='GSW vs. TOR']
games_away=games [games ['MATCHUP']=='GSW @ TOR'] | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
We can calculate the mean for the column PLUS_MINUS for the dataframes games_home and games_away: | games_home.mean()['PLUS_MINUS']
games_away.mean()['PLUS_MINUS'] | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
We can plot out the PLUS MINUS column for for the dataframes games_home and games_away.We see the warriors played better at home. | fig, ax = plt.subplots()
games_away.plot(x='GAME_DATE',y='PLUS_MINUS', ax=ax)
games_home.plot(x='GAME_DATE',y='PLUS_MINUS', ax=ax)
ax.legend(["away", "home"])
plt.show() | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- |
About the Authors: [Joseph Santarcangelo]( https://www.linkedin.com/in/joseph-s-50398b136/) has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed ... | _____no_output_____ | MIT | Python_For_DSandAI_5_1_Intro_API.ipynb | ornob39/Python_For_DataScience_AI-IBM- | |
Machine Learning Engineer Nanodegree Unsupervised Learning Project: Creating Customer Segments Welcome to the third project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and it will be your job to implement the additional functionality necessary to... | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
from scipy.stats import skew
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline... | Wholesale customers dataset has 440 samples with 6 features each.
| MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Data ExplorationIn this section, you will begin exploring the data through visualizations and code to understand how each feature is related to the others. You will observe a statistical description of the dataset, consider the relevance of each feature, and select a few sample data points from the dataset which you w... | # Display a description of the dataset
display(data.describe())
#by looking at statistics, mean and 50% i.e. median are too far away for every colum. So, skew is found. apply log to every column
#or use box cox test | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Implementation: Selecting SamplesTo get a better understanding of the customers and how their data will transform through the analysis, it would be best to select a few sample data points and explore them in more detail. In the code block below, add **three** indices of your choice to the `indices` list which will rep... | import seaborn as sns
# TODO: Select three indices of your choice you wish to sample from the dataset
indices = [23, 57,234]
# Create a DataFrame of the chosen samples
samples = pd.DataFrame(data.loc[indices], columns = data.keys()).reset_index(drop = True)
print "Chosen samples of wholesale customers dataset:"
displ... | Chosen samples of wholesale customers dataset:
| MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Question 1Consider the total purchase cost of each product category and the statistical description of the dataset above for your sample customers. *What kind of establishment (customer) could each of the three samples you've chosen represent?* **Hint:** Examples of establishments include places like markets, cafes,... | '''# TODO: Make a copy of the DataFrame, using the 'drop' function to drop the given feature
new_data = None
# TODO: Split the data into training and testing sets using the given feature as the target
X_train, X_test, y_train, y_test = (None, None, None, None)
# TODO: Create a decision tree regressor and fit it to th... | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Question 2*Which feature did you attempt to predict? What was the reported prediction score? Is this feature necessary for identifying customers' spending habits?* **Hint:** The coefficient of determination, `R^2`, is scored between 0 and 1, with 1 being a perfect fit. A negative `R^2` implies the model fails to fit ... | # Produce a scatter matrix for each pair of features in the data
pd.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');
import seaborn as sns
sns.heatmap(data.corr(), annot=True) | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Question 3*Are there any pairs of features which exhibit some degree of correlation? Does this confirm or deny your suspicions about the relevance of the feature you attempted to predict? How is the data for those features distributed?* **Hint:** Is the data normally distributed? Where do most of the data points lie?... | # TODO: Scale the data using the natural logarithm
log_data = np.log(data)
# TODO: Scale the sample data using the natural logarithm
log_samples = np.log(samples)
# Produce a scatter matrix for each pair of newly-transformed features
pd.scatter_matrix(log_data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');
log_da... | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
ObservationAfter applying a natural logarithm scaling to the data, the distribution of each feature should appear much more normal. For any pairs of features you may have identified earlier as being correlated, observe here whether that correlation is still present (and whether it is now stronger or weaker than before... | # Display the log-transformed sample data
display(log_samples) | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Implementation: Outlier DetectionDetecting outliers in the data is extremely important in the data preprocessing step of any analysis. The presence of outliers can often skew results which take into consideration these data points. There are many "rules of thumb" for what constitutes an outlier in a dataset. Here, we ... | # For each feature find the data points with extreme high or low values
outindex = {}
for feature in log_data.keys():
# TODO: Calculate Q1 (25th percentile of the data) for the given feature
Q1 = np.percentile(log_data[feature],25)
# TODO: Calculate Q3 (75th percentile of the data) for the given f... | Data points considered outliers for the feature 'Fresh':
| MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Question 4*Are there any data points considered outliers for more than one feature based on the definition above? Should these data points be removed from the dataset? If any data points were added to the `outliers` list to be removed, explain why.* **Answer:**[128, 154, 65, 66, 75] are the outliers and should be rem... | from sklearn.decomposition import PCA
# TODO: Apply PCA by fitting the good data with the same number of dimensions as features
pca = PCA(n_components = 6)
pca.fit(good_data)
# TODO: Transform log_samples using the PCA fit above
pca_samples = pca.transform(log_samples)
# Generate PCA results plot
pca_results = vs.pca_... | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Question 5*How much variance in the data is explained* ***in total*** *by the first and second principal component? What about the first four principal components? Using the visualization provided above, discuss what the first four dimensions best represent in terms of customer spending.* **Hint:** A positive increas... | # Display sample log-data after having a PCA transformation applied
display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values)) | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Implementation: Dimensionality ReductionWhen using principal component analysis, one of the main goals is to reduce the dimensionality of the data โ in effect, reducing the complexity of the problem. Dimensionality reduction comes at a cost: Fewer dimensions used implies less of the total variance in the data is being... | # TODO: Apply PCA by fitting the good data with only two dimensions
pca = PCA(n_components =2)
pca.fit(good_data)
# TODO: Transform the good data using the PCA fit above
reduced_data = pca.transform(good_data)
# TODO: Transform log_samples using the PCA fit above
pca_samples = pca.transform(log_samples)
# Create a Da... | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
ObservationRun the code below to see how the log-transformed sample data has changed after having a PCA transformation applied to it using only two dimensions. Observe how the values for the first two dimensions remains unchanged when compared to a PCA transformation in six dimensions. | # Display sample log-data after applying PCA transformation in two dimensions
display(pd.DataFrame(np.round(pca_samples, 4), columns = ['Dimension 1', 'Dimension 2'])) | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Visualizing a BiplotA biplot is a scatterplot where each data point is represented by its scores along the principal components. The axes are the principal components (in this case `Dimension 1` and `Dimension 2`). In addition, the biplot shows the projection of the original features along the components. A biplot can... | # Create a biplot
vs.biplot(good_data, reduced_data, pca) | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
ObservationOnce we have the original feature projections (in red), it is easier to interpret the relative position of each data point in the scatterplot. For instance, a point the lower right corner of the figure will likely correspond to a customer that spends a lot on `'Milk'`, `'Grocery'` and `'Detergents_Paper'`, ... | from sklearn.metrics import silhouette_score
from sklearn.mixture import GMM
scorer = {}#for n sample points 2 to n-1 clusters can be created.
for i in range(2,10):
clusterer = GMM(n_components = i)
clusterer.fit(reduced_data)
pred = clusterer.predict(reduced_data)
score = silhouette_score(reduced_data,... | C:\Users\admin\Anaconda2\lib\site-packages\sklearn\utils\deprecation.py:52: DeprecationWarning: Class GMM is deprecated; The class GMM is deprecated in 0.18 and will be removed in 0.20. Use class GaussianMixture instead.
warnings.warn(msg, category=DeprecationWarning)
C:\Users\admin\Anaconda2\lib\site-packages\sklea... | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Question 7*Report the silhouette score for several cluster numbers you tried. Of these, which number of clusters has the best silhouette score?* **Answer:**{2: 0.41181886438624482, 3: 0.37616616509083634, 4: 0.34168407828470648, 5: 0.28001985722335737, 6: 0.26923051036000389, 7: 0.32398601556485884, 8: 0.304106857662... | # Display the results of the clustering from implementation
vs.cluster_results(reduced_data, preds, centers, pca_samples) | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Implementation: Data RecoveryEach cluster present in the visualization above has a central point. These centers (or means) are not specifically data points from the data, but rather the *averages* of all the data points predicted in the respective clusters. For the problem of creating customer segments, a cluster's ce... | # TODO: Inverse transform the centers
log_centers = pca.inverse_transform(centers)
# TODO: Exponentiate the centers
true_centers = np.exp(log_centers)
# Display the true centers
segments = ['Segment {}'.format(i) for i in range(0,len(centers))]
true_centers = pd.DataFrame(np.round(true_centers), columns = data.keys()... | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
Question 8Consider the total purchase cost of each product category for the representative data points above, and reference the statistical description of the dataset at the beginning of this project. *What set of establishments could each of the customer segments represent?* **Hint:** A customer who is assigned to `... | # Display the predictions
for i, pred in enumerate(sample_preds):
print "Sample point", i, "predicted to be in Cluster", pred
display(samples) | Sample point 0 predicted to be in Cluster 0
Sample point 1 predicted to be in Cluster 0
Sample point 2 predicted to be in Cluster 1
| MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
**Answer:** The third datapoint is correctly classified by both me and gmm. The first two aren't exactly misclassfied since I have put them into two categories Supermarket and Convenience Store which can be also called as Retailer Conclusion In this final section, you will investigate ways that you can make use of the... | # Display the clustering results based on 'Channel' data
vs.channel_results(reduced_data, outliers, pca_samples) | _____no_output_____ | MIT | customer_segments/customer_segments.ipynb | nk101/Machine-Learning-ND |
SSD Evaluation TutorialThis is a brief tutorial that explains how compute the average precisions for any trained SSD model using the `Evaluator` class. The `Evaluator` computes the average precisions according to the Pascal VOC pre-2010 or post-2010 detection evaluation algorithms. You can find details about these com... | from keras import backend as K
from tensorflow.keras.models import load_model
from tensorflow.keras.optimizers import Adam
from matplotlib.pyplot import imread
import numpy as np
from matplotlib import pyplot as plt
from models.keras_ssd300 import ssd_300
from keras_loss_function.keras_ssd_loss import SSDLoss
from ker... | _____no_output_____ | Apache-2.0 | ssd300_evaluation.ipynb | rogeryan/ssd_keras |
1. Load a trained SSDEither load a trained model or build a model and load trained weights into it. Since the HDF5 files I'm providing contain only the weights for the various SSD versions, not the complete models, you'll have to go with the latter option when using this implementation for the first time. You can then... | # 1: Build the Keras model
K.clear_session() # Clear previous models from memory.
model = ssd_300(image_size=(img_height, img_width, 3),
n_classes=n_classes,
mode=model_mode,
l2_regularization=0.0005,
scales=[0.1, 0.2, 0.37, 0.54, 0.71, 0.88, 1.05], # Th... | _____no_output_____ | Apache-2.0 | ssd300_evaluation.ipynb | rogeryan/ssd_keras |
Or 1.2. Load a trained modelWe set `model_mode` to 'inference' above, so the evaluator expects that you load a model that was built in 'inference' mode. If you're loading a model that was built in 'training' mode, change the `model_mode` parameter accordingly. | # TODO: Set the path to the `.h5` file of the model to be loaded.
model_path = 'path/to/trained/model.h5'
# We need to create an SSDLoss object in order to pass that to the model loader.
ssd_loss = SSDLoss(neg_pos_ratio=3, alpha=1.0)
K.clear_session() # Clear previous models from memory.
model = load_model(model_pat... | _____no_output_____ | Apache-2.0 | ssd300_evaluation.ipynb | rogeryan/ssd_keras |
2. Create a data generator for the evaluation datasetInstantiate a `DataGenerator` that will serve the evaluation dataset during the prediction phase. | dataset = DataGenerator()
# TODO: Set the paths to the dataset here.
Pascal_VOC_dataset_images_dir = '../../datasets/VOCdevkit/VOC2007/JPEGImages/'
Pascal_VOC_dataset_annotations_dir = '../../datasets/VOCdevkit/VOC2007/Annotations/'
Pascal_VOC_dataset_image_set_filename = '../../datasets/VOCdevkit/VOC2007/ImageSets/Ma... | test.txt: 100%|โโโโโโโโโโ| 4952/4952 [00:13<00:00, 373.84it/s]
| Apache-2.0 | ssd300_evaluation.ipynb | rogeryan/ssd_keras |
3. Run the evaluationNow that we have instantiated a model and a data generator to serve the dataset, we can set up the evaluator and run the evaluation.The evaluator is quite flexible: It can compute the average precisions according to the Pascal VOC pre-2010 algorithm, which samples 11 equidistant points of the prec... | evaluator = Evaluator(model=model,
n_classes=n_classes,
data_generator=dataset,
model_mode=model_mode)
results = evaluator(img_height=img_height,
img_width=img_width,
batch_size=8,
data_generat... | Number of images in the evaluation dataset: 4952
Producing predictions batch-wise: 100%|โโโโโโโโโโ| 619/619 [02:17<00:00, 4.50it/s]
Matching predictions to ground truth, class 1/20.: 100%|โโโโโโโโโโ| 7902/7902 [00:00<00:00, 19253.00it/s]
Matching predictions to ground truth, class 2/20.: 100%|โโโโโโโโโโ| 4276/4276 [0... | Apache-2.0 | ssd300_evaluation.ipynb | rogeryan/ssd_keras |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.