path
stringlengths 13
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sequencelengths 1
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|---|---|---|---|
16148029/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import warnings
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.xticks([])
plt.yticks([])
train_images = train_images / 255.0
test_images = test_images / 255.0
for i in range(25):
plt.xticks([])
plt.yticks([])
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(64, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_images = train_images.values
train_labels = train_labels.values
test_images = test_images.values
test_labels = test_labels.values
model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images, test_labels)
predictions = model.predict(test_images)
np.argmax(predictions[0])
x = np.argmax(predictions[999])
print(x)
print('\n')
print(test_labels[999]) | code |
16148029/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.figure()
plt.imshow(train_images.iloc[4].as_matrix().reshape(28, 28))
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.show()
print(class_names[3]) | code |
16148029/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
train.shape
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.shape
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
train_images = train.iloc[:, 1:785]
train_labels = train.iloc[:, 0]
test_images = test.iloc[:, 1:785]
test_labels = test.iloc[:, 0]
plt.colorbar()
plt.xticks([])
plt.yticks([])
plt.colorbar()
plt.xticks([])
plt.yticks([])
train_images = train_images / 255.0
test_images = test_images / 255.0
plt.figure(figsize=(10, 10))
for i in range(25):
plt.subplot(5, 5, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images.iloc[i].as_matrix().reshape(28, 28))
plt.xlabel(class_names[train_labels[i]])
plt.show() | code |
16148029/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/fashion-mnist_train.csv')
test = pd.read_csv('../input/fashion-mnist_test.csv')
test.head() | code |
72077843/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.groupby('Sex')[['Survived']].mean()
df.pivot_table('Survived', index='Sex', columns='Pclass')
age = pd.cut(df['Age'], [0, 50, 80])
df.pivot_table('Survived', index=['Sex', age], columns='Pclass')
plt.scatter(df['Fare'], df['Pclass'], color='Orange', label='Passeneger Paid')
plt.ylabel('Pclass')
plt.xlabel('Price')
plt.legend() | code |
72077843/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.groupby('Sex')[['Survived']].mean()
df.pivot_table('Survived', index='Sex', columns='Pclass') | code |
72077843/cell_4 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.describe() | code |
72077843/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
import seaborn as sns
sns.countplot(df['Survived']) | code |
72077843/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df | code |
72077843/cell_11 | [
"text_plain_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
import seaborn as sns
df.groupby('Sex')[['Survived']].mean()
df.pivot_table('Survived', index='Sex', columns='Pclass')
sns.barplot(x='Pclass', y='Survived', data=df) | code |
72077843/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72077843/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
cols = ['Sex', 'Pclass', 'Age', 'SibSp']
n_rows = 2
n_cols = 2
fig, axs = plt.subplots(n_rows, n_cols, figsize=(n_cols * 8, n_rows * 8))
for r in range(0, n_rows):
for c in range(0, n_cols):
i = r * n_cols + c
ax = axs[r][c]
sns.countplot(df[cols[i]], hue=df['Survived'], ax=ax)
ax.set_title(cols[i])
ax.legend(title='survived', loc='upper right')
plt.tight_layout() | code |
72077843/cell_8 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.groupby('Sex')[['Survived']].mean() | code |
72077843/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape | code |
72077843/cell_10 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.groupby('Sex')[['Survived']].mean()
df.pivot_table('Survived', index='Sex', columns='Pclass')
df.pivot_table('Survived', index='Sex', columns='Pclass').plot() | code |
72077843/cell_12 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df.groupby('Sex')[['Survived']].mean()
df.pivot_table('Survived', index='Sex', columns='Pclass')
age = pd.cut(df['Age'], [0, 50, 80])
df.pivot_table('Survived', index=['Sex', age], columns='Pclass') | code |
72077843/cell_5 | [
"text_html_output_1.png"
] | import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
import os
import pandas as pd
os.getcwd()
os.chdir('/kaggle/')
os.listdir('/kaggle/input')
df = pd.read_csv('input/titanic/train.csv')
df
df.shape
df['Survived'].value_counts() | code |
122251959/cell_21 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(4)
math.factorial(5) | code |
122251959/cell_13 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x) | code |
122251959/cell_9 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a) | code |
122251959/cell_4 | [
"text_plain_output_1.png"
] | import math
math.e | code |
122251959/cell_23 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(4)
math.factorial(5)
l = [1.2, 2.3, 3.4, 4.5]
sum(l)
math.fsum(l) | code |
122251959/cell_20 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree))
math.sqrt(4) | code |
122251959/cell_18 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2)
degree = 90
math.sin(math.radians(degree)) | code |
122251959/cell_8 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a) | code |
122251959/cell_15 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10) | code |
122251959/cell_17 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000)
math.log(1000, 10)
math.sin(math.pi / 2) | code |
122251959/cell_14 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b)
x = 3
math.exp(x)
math.log(1000) | code |
122251959/cell_22 | [
"text_plain_output_1.png"
] | l = [1.2, 2.3, 3.4, 4.5]
sum(l) | code |
122251959/cell_10 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi
a = 3.4
math.ceil(a)
math.floor(a)
b = 5.3145
math.trunc(b) | code |
122251959/cell_5 | [
"text_plain_output_1.png"
] | import math
math.e
math.pi | code |
33100906/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.head() | code |
33100906/cell_2 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import seaborn as sns
from collections import Counter
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33100906/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns
train.describe() | code |
33100906/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test_PassengerId = test['PassengerId']
train.columns | code |
49119627/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(C=10)
logreg.fit(X_train, Y_train)
Y_predict1 = logreg.predict(X_test)
score_logreg = logreg.score(X_test, Y_test)
print(score_logreg) | code |
49119627/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/breast-cancer-prediction-dataset/Breast_cancer_data.csv')
print('Dataset :', data.shape)
x = data.iloc[:, [0, 1, 2, 3]].values
data.info()
data[0:10] | code |
49119627/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(C=10)
logreg.fit(X_train, Y_train)
Y_predict1 = logreg.predict(X_test)
score_logreg = logreg.score(X_test, Y_test)
from sklearn.feature_selection import RFE
logreg_2 = LogisticRegression()
rfe = RFE(estimator=logreg_2, n_features_to_select=5, step=1)
rfe = rfe.fit(X_train, Y_train)
print('Chosen best 5 feature by rfe:', X_train.columns[rfe.support_])
X_train_3 = rfe.transform(X_train)
X_test_3 = rfe.transform(X_test)
logreg_2 = LogisticRegression()
logreg_2 = logreg_2.fit(X_train_3, Y_train)
Y_predict2 = logreg.predict(X_test_3)
score_logreg = logreg_2.score(X_test_3, Y_test)
print(score_logreg) | code |
49119627/cell_5 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import seaborn as sns
import seaborn as sns
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression(C=10)
logreg.fit(X_train, Y_train)
Y_predict1 = logreg.predict(X_test)
from sklearn.metrics import confusion_matrix
import seaborn as sns
logreg_cm = confusion_matrix(Y_test, Y_predict1)
f, ax = plt.subplots(figsize=(5, 5))
sns.heatmap(logreg_cm, annot=True, linewidth=0.7, linecolor='red', fmt='g', ax=ax, cmap='BuPu')
plt.title('Logistic Regression Classification Confusion Matrix')
plt.xlabel('Y predict')
plt.ylabel('Y test')
plt.show() | code |
105199751/cell_21 | [
"text_plain_output_1.png"
] | from datetime import datetime
import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
Category_info = pd.json_normalize(IN_JSON['items'])
Category_info
IN_CSV = pd.read_csv(os.path.join(BASE_DIR, 'INvideos.csv'))
IN_CSV
Category_info.id = Category_info.id.astype('int64')
IN_CSV = IN_CSV.merge(Category_info, left_on='category_id', right_on='id')
IN_CSV.drop(columns=['kind', 'etag', 'id', 'snippet.channelId', 'snippet.assignable'], axis=1, inplace=True)
IN_CSV.rename(columns={'snippet.title': 'cat_title'}, inplace=True)
IN_CSV.columns
IN_CSV.publish_time
'2020-01-06T00:00:00.000Z'[:-1]
def change_to_datetime(time):
time = datetime.fromisoformat(time[:-1])
time.strftime('%Y-%m-%d %H:%M:%S')
return time
IN_CSV.publish_time = IN_CSV.publish_time.apply(lambda val: change_to_datetime(val))
IN_CSV.publish_time
IN_CSV.trending_date = pd.to_datetime(IN_CSV.trending_date, format='%y.%d.%m')
IN_CSV.trending_date | code |
105199751/cell_13 | [
"text_plain_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
Category_info = pd.json_normalize(IN_JSON['items'])
Category_info
IN_CSV = pd.read_csv(os.path.join(BASE_DIR, 'INvideos.csv'))
IN_CSV
Category_info.id = Category_info.id.astype('int64')
IN_CSV = IN_CSV.merge(Category_info, left_on='category_id', right_on='id')
IN_CSV.drop(columns=['kind', 'etag', 'id', 'snippet.channelId', 'snippet.assignable'], axis=1, inplace=True)
IN_CSV.rename(columns={'snippet.title': 'cat_title'}, inplace=True)
IN_CSV.columns | code |
105199751/cell_4 | [
"text_plain_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title'] | code |
105199751/cell_6 | [
"text_plain_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
Category_info = pd.json_normalize(IN_JSON['items'])
Category_info | code |
105199751/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
print(LIST_JSON)
print(LIST_CSV) | code |
105199751/cell_19 | [
"text_plain_output_1.png"
] | from datetime import datetime
import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
Category_info = pd.json_normalize(IN_JSON['items'])
Category_info
IN_CSV = pd.read_csv(os.path.join(BASE_DIR, 'INvideos.csv'))
IN_CSV
Category_info.id = Category_info.id.astype('int64')
IN_CSV = IN_CSV.merge(Category_info, left_on='category_id', right_on='id')
IN_CSV.drop(columns=['kind', 'etag', 'id', 'snippet.channelId', 'snippet.assignable'], axis=1, inplace=True)
IN_CSV.rename(columns={'snippet.title': 'cat_title'}, inplace=True)
IN_CSV.columns
IN_CSV.publish_time
'2020-01-06T00:00:00.000Z'[:-1]
def change_to_datetime(time):
time = datetime.fromisoformat(time[:-1])
time.strftime('%Y-%m-%d %H:%M:%S')
return time
IN_CSV.publish_time = IN_CSV.publish_time.apply(lambda val: change_to_datetime(val))
IN_CSV.publish_time | code |
105199751/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
105199751/cell_7 | [
"text_plain_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
IN_JSON | code |
105199751/cell_8 | [
"text_html_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
Category_info = pd.json_normalize(IN_JSON['items'])
Category_info
IN_CSV = pd.read_csv(os.path.join(BASE_DIR, 'INvideos.csv'))
IN_CSV | code |
105199751/cell_3 | [
"text_plain_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())) | code |
105199751/cell_14 | [
"text_html_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
Category_info = pd.json_normalize(IN_JSON['items'])
Category_info
IN_CSV = pd.read_csv(os.path.join(BASE_DIR, 'INvideos.csv'))
IN_CSV
Category_info.id = Category_info.id.astype('int64')
IN_CSV = IN_CSV.merge(Category_info, left_on='category_id', right_on='id')
IN_CSV.drop(columns=['kind', 'etag', 'id', 'snippet.channelId', 'snippet.assignable'], axis=1, inplace=True)
IN_CSV.rename(columns={'snippet.title': 'cat_title'}, inplace=True)
IN_CSV.columns
IN_CSV.publish_time | code |
105199751/cell_10 | [
"text_plain_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
Category_info = pd.json_normalize(IN_JSON['items'])
Category_info
IN_CSV = pd.read_csv(os.path.join(BASE_DIR, 'INvideos.csv'))
IN_CSV
Category_info.id = Category_info.id.astype('int64')
IN_CSV = IN_CSV.merge(Category_info, left_on='category_id', right_on='id')
print(IN_CSV.columns)
IN_CSV.drop(columns=['kind', 'etag', 'id', 'snippet.channelId', 'snippet.assignable'], axis=1, inplace=True) | code |
105199751/cell_12 | [
"text_html_output_1.png"
] | import json
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import os
BASE_DIR = '/kaggle/input/youtube-dataset-of-countries/Youtube_data/Countries_data'
LIST_CSV = []
LIST_JSON = []
for file in os.listdir(BASE_DIR):
if file.endswith('.json'):
LIST_JSON.append(file)
else:
LIST_CSV.append(file)
import json
pd.DataFrame.from_dict(json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read()))
IN_JSON = json.loads(open(os.path.join(BASE_DIR, 'IN_category_id.json')).read())
IN_JSON['items'][0]['snippet']['title']
Category_info = pd.json_normalize(IN_JSON['items'])
Category_info
IN_CSV = pd.read_csv(os.path.join(BASE_DIR, 'INvideos.csv'))
IN_CSV
Category_info.id = Category_info.id.astype('int64')
IN_CSV = IN_CSV.merge(Category_info, left_on='category_id', right_on='id')
IN_CSV.drop(columns=['kind', 'etag', 'id', 'snippet.channelId', 'snippet.assignable'], axis=1, inplace=True)
IN_CSV | code |
129022538/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/Parkinson_disease.csv')
df.info() | code |
129022538/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import warnings
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import random
import warnings
warnings.filterwarnings('ignore')
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.metrics import r2_score, mean_squared_error, mean_squared_log_error
from sklearn.model_selection import train_test_split, cross_val_score, KFold, GridSearchCV
from sklearn.metrics import confusion_matrix, precision_recall_curve, auc, roc_auc_score, roc_curve, recall_score, classification_report
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.ensemble import GradientBoostingRegressor
from imblearn.over_sampling import SMOTE | code |
90111237/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
from pathlib import Path
from pytorch_lightning import LightningModule
from sklearn.manifold import TSNE
from tokenizers import Tokenizer
from torchvision import models
from tqdm import tqdm
import albumentations as A
import cv2
import math
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import numpy as np
import pandas as pd
from pathlib import Path
import os
BASE_PATH = Path('/kaggle/input/h-and-m-personalized-fashion-recommendations/')
MODEL_PATH = Path('/kaggle/input/image-text-embeddings/ssl_resnet18_1337.ckpt')
TOKENIZER_PATH = Path('/kaggle/input/image-text-embeddings/tokenizer.json')
from tokenizers import Tokenizer
TOKENIZER = Tokenizer.from_file(str(TOKENIZER_PATH))
CLS_IDX = TOKENIZER.token_to_id('[CLS]')
PAD_IDX = TOKENIZER.token_to_id('[PAD]')
SEP_IDX = TOKENIZER.token_to_id('[SEP]')
def tokenize(text: str):
raw_tokens = TOKENIZER.encode(text)
return raw_tokens.ids
def pad_list(list_integers, context_size: int=90, pad_val: int=PAD_IDX, mode='right'):
"""
:param list_integers:
:param context_size:
:param pad_val:
:param mode:
:return:
"""
list_integers = list_integers[:context_size]
if len(list_integers) < context_size:
if mode == 'left':
list_integers = [pad_val] * (context_size - len(list_integers)) + list_integers
else:
list_integers = list_integers + [pad_val] * (context_size - len(list_integers))
return list_integers
import random
from pathlib import Path
import cv2
import numpy as np
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import albumentations as A
SIZE = 128
SCALE = 255.0
RESIZE = A.Compose([A.LongestMaxSize(max_size=SIZE, p=1.0), A.PadIfNeeded(min_height=SIZE, min_width=SIZE, p=1.0)])
NORMALIZE = A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=SCALE)
def read_image(image_path: Path) -> np.ndarray:
bgr_image = cv2.imread(str(image_path))
rgb_image = bgr_image[:, :, ::-1]
return rgb_image
def resize(image: np.ndarray) -> np.ndarray:
reshaped = RESIZE(image=image)['image']
return reshaped
def normalize(image: np.ndarray) -> np.ndarray:
normalized = NORMALIZE(image=image)['image']
return normalized
def preprocess(image: np.ndarray) -> np.ndarray:
return normalize(resize(image))
import math
import torch
import torch.nn.functional as F
from pytorch_lightning import LightningModule
from torchvision import models
from transformers import get_cosine_schedule_with_warmup
class PositionalEncoding(torch.nn.Module):
def __init__(self, d_model: int, dropout: float=0.1, max_len: int=5000):
super().__init__()
self.dropout = torch.nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model)
pe[0:, :, 0::2] = torch.sin(position * div_term)
pe[0:, :, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
self.d_model = d_model
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: Tensor, shape [seq_len, batch_size, embedding_dim]
"""
x = x + self.pe[:, :x.size(1)] / math.sqrt(self.d_model)
return self.dropout(x)
class Cola(LightningModule):
def __init__(self, lr=0.001, use_pretrained=False, dropout=0.2, d_model=128, n_vocab=30000, smoothing=0.1):
super().__init__()
self.dropout = dropout
self.lr = lr
self.d_model = d_model
self.n_vocab = n_vocab
self.smoothing = smoothing
self.model = models.resnet18(pretrained=use_pretrained)
self.model.fc = torch.nn.Linear(self.model.fc.in_features, self.d_model)
self.item_embeddings = torch.nn.Embedding(self.n_vocab, self.d_model)
self.pos_encoder = PositionalEncoding(d_model=self.d_model, dropout=self.dropout)
encoder_layer = torch.nn.TransformerEncoderLayer(d_model=self.d_model, nhead=4, dropout=self.dropout, batch_first=True)
self.encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers=4)
self.layer_norm = torch.nn.LayerNorm(normalized_shape=self.d_model)
self.linear = torch.nn.Linear(self.d_model, self.d_model, bias=False)
self.do = torch.nn.Dropout(p=self.dropout)
self.save_hyperparameters()
def encode_image(self, x):
x = x.permute(0, 3, 1, 2)
x = self.do(self.model(x))
x = torch.tanh(self.layer_norm(x))
return x
def encode_text(self, x):
x = self.item_embeddings(x)
x = self.pos_encoder(x)
x = self.encoder(x)
return x[:, 0, :]
def forward(self, x):
image, text = x
encoded_image = self.encode_image(image)
encoded_image_w = self.linear(encoded_image)
encoded_text = self.encode_text(text)
return (encoded_image_w, encoded_text)
df = pd.read_csv(BASE_PATH / 'articles.csv', nrows=None, dtype={'article_id': str})
df['text'] = df.apply(lambda x: ' '.join([str(x['prod_name']), str(x['product_type_name']), str(x['product_group_name']), str(x['graphical_appearance_name']), str(x['colour_group_name']), str(x['perceived_colour_value_name']), str(x['index_name']), str(x['section_name']), str(x['detail_desc'])]), axis=1)
df['image_path'] = df.article_id.apply(lambda x: BASE_PATH / 'images' / x[:3] / f'{x}.jpg')
df = df.sample(n=5000)
model = Cola(lr=0.0001)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.load_state_dict(torch.load(MODEL_PATH, map_location=device)['state_dict'])
model.to(device)
model.eval()
text_embeddings = []
image_embeddings = []
for image_path, text in tqdm(zip(df.image_path.values, df.text.values), total=len(df)):
if image_path.is_file():
image = read_image(image_path)
else:
image = np.zeros((128, 128, 3))
image = preprocess(image)
image_t = torch.from_numpy(image).unsqueeze(0)
image_t = image_t.to(device)
text_t = tokenize(text)
text_t = torch.tensor(pad_list(text_t), dtype=torch.long, device=device).unsqueeze(0)
with torch.no_grad():
text_embed = model.encode_text(text_t)
image_embed = model.encode_image(image_t)
text_embed = text_embed.squeeze().cpu().tolist()
image_embed = image_embed.squeeze().cpu().tolist()
text_embeddings.append(text_embed)
image_embeddings.append(image_embed)
text_embeddings = np.array(text_embeddings)
image_embeddings = np.array(image_embeddings)
tsne = TSNE(n_components=2, init='random', random_state=0, learning_rate='auto', n_iter=300)
Y = tsne.fit_transform(image_embeddings)
fig = plt.figure(figsize=(12, 12))
for index_name in df.index_name.unique():
plt.scatter(Y[df.index_name == index_name, 0], Y[df.index_name == index_name, 1], label=index_name, s=3)
plt.title('Cola Image embeddings by index_name')
plt.legend()
plt.show() | code |
90111237/cell_2 | [
"image_output_1.png"
] | from pathlib import Path
import os
import numpy as np
import pandas as pd
from pathlib import Path
import os
for dirname, _, filenames in os.walk('../input/image-text-embeddings'):
for filename in filenames:
print(os.path.join(dirname, filename))
BASE_PATH = Path('/kaggle/input/h-and-m-personalized-fashion-recommendations/')
MODEL_PATH = Path('/kaggle/input/image-text-embeddings/ssl_resnet18_1337.ckpt')
TOKENIZER_PATH = Path('/kaggle/input/image-text-embeddings/tokenizer.json') | code |
90111237/cell_11 | [
"text_plain_output_1.png"
] | from pathlib import Path
from pathlib import Path
from pytorch_lightning import LightningModule
from tokenizers import Tokenizer
from torchvision import models
from tqdm import tqdm
import albumentations as A
import cv2
import math
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import numpy as np
import pandas as pd
from pathlib import Path
import os
BASE_PATH = Path('/kaggle/input/h-and-m-personalized-fashion-recommendations/')
MODEL_PATH = Path('/kaggle/input/image-text-embeddings/ssl_resnet18_1337.ckpt')
TOKENIZER_PATH = Path('/kaggle/input/image-text-embeddings/tokenizer.json')
from tokenizers import Tokenizer
TOKENIZER = Tokenizer.from_file(str(TOKENIZER_PATH))
CLS_IDX = TOKENIZER.token_to_id('[CLS]')
PAD_IDX = TOKENIZER.token_to_id('[PAD]')
SEP_IDX = TOKENIZER.token_to_id('[SEP]')
def tokenize(text: str):
raw_tokens = TOKENIZER.encode(text)
return raw_tokens.ids
def pad_list(list_integers, context_size: int=90, pad_val: int=PAD_IDX, mode='right'):
"""
:param list_integers:
:param context_size:
:param pad_val:
:param mode:
:return:
"""
list_integers = list_integers[:context_size]
if len(list_integers) < context_size:
if mode == 'left':
list_integers = [pad_val] * (context_size - len(list_integers)) + list_integers
else:
list_integers = list_integers + [pad_val] * (context_size - len(list_integers))
return list_integers
import random
from pathlib import Path
import cv2
import numpy as np
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import albumentations as A
SIZE = 128
SCALE = 255.0
RESIZE = A.Compose([A.LongestMaxSize(max_size=SIZE, p=1.0), A.PadIfNeeded(min_height=SIZE, min_width=SIZE, p=1.0)])
NORMALIZE = A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=SCALE)
def read_image(image_path: Path) -> np.ndarray:
bgr_image = cv2.imread(str(image_path))
rgb_image = bgr_image[:, :, ::-1]
return rgb_image
def resize(image: np.ndarray) -> np.ndarray:
reshaped = RESIZE(image=image)['image']
return reshaped
def normalize(image: np.ndarray) -> np.ndarray:
normalized = NORMALIZE(image=image)['image']
return normalized
def preprocess(image: np.ndarray) -> np.ndarray:
return normalize(resize(image))
import math
import torch
import torch.nn.functional as F
from pytorch_lightning import LightningModule
from torchvision import models
from transformers import get_cosine_schedule_with_warmup
class PositionalEncoding(torch.nn.Module):
def __init__(self, d_model: int, dropout: float=0.1, max_len: int=5000):
super().__init__()
self.dropout = torch.nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model)
pe[0:, :, 0::2] = torch.sin(position * div_term)
pe[0:, :, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
self.d_model = d_model
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: Tensor, shape [seq_len, batch_size, embedding_dim]
"""
x = x + self.pe[:, :x.size(1)] / math.sqrt(self.d_model)
return self.dropout(x)
class Cola(LightningModule):
def __init__(self, lr=0.001, use_pretrained=False, dropout=0.2, d_model=128, n_vocab=30000, smoothing=0.1):
super().__init__()
self.dropout = dropout
self.lr = lr
self.d_model = d_model
self.n_vocab = n_vocab
self.smoothing = smoothing
self.model = models.resnet18(pretrained=use_pretrained)
self.model.fc = torch.nn.Linear(self.model.fc.in_features, self.d_model)
self.item_embeddings = torch.nn.Embedding(self.n_vocab, self.d_model)
self.pos_encoder = PositionalEncoding(d_model=self.d_model, dropout=self.dropout)
encoder_layer = torch.nn.TransformerEncoderLayer(d_model=self.d_model, nhead=4, dropout=self.dropout, batch_first=True)
self.encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers=4)
self.layer_norm = torch.nn.LayerNorm(normalized_shape=self.d_model)
self.linear = torch.nn.Linear(self.d_model, self.d_model, bias=False)
self.do = torch.nn.Dropout(p=self.dropout)
self.save_hyperparameters()
def encode_image(self, x):
x = x.permute(0, 3, 1, 2)
x = self.do(self.model(x))
x = torch.tanh(self.layer_norm(x))
return x
def encode_text(self, x):
x = self.item_embeddings(x)
x = self.pos_encoder(x)
x = self.encoder(x)
return x[:, 0, :]
def forward(self, x):
image, text = x
encoded_image = self.encode_image(image)
encoded_image_w = self.linear(encoded_image)
encoded_text = self.encode_text(text)
return (encoded_image_w, encoded_text)
df = pd.read_csv(BASE_PATH / 'articles.csv', nrows=None, dtype={'article_id': str})
df['text'] = df.apply(lambda x: ' '.join([str(x['prod_name']), str(x['product_type_name']), str(x['product_group_name']), str(x['graphical_appearance_name']), str(x['colour_group_name']), str(x['perceived_colour_value_name']), str(x['index_name']), str(x['section_name']), str(x['detail_desc'])]), axis=1)
df['image_path'] = df.article_id.apply(lambda x: BASE_PATH / 'images' / x[:3] / f'{x}.jpg')
df = df.sample(n=5000)
model = Cola(lr=0.0001)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.load_state_dict(torch.load(MODEL_PATH, map_location=device)['state_dict'])
model.to(device)
model.eval()
text_embeddings = []
image_embeddings = []
for image_path, text in tqdm(zip(df.image_path.values, df.text.values), total=len(df)):
if image_path.is_file():
image = read_image(image_path)
else:
image = np.zeros((128, 128, 3))
image = preprocess(image)
image_t = torch.from_numpy(image).unsqueeze(0)
image_t = image_t.to(device)
text_t = tokenize(text)
text_t = torch.tensor(pad_list(text_t), dtype=torch.long, device=device).unsqueeze(0)
with torch.no_grad():
text_embed = model.encode_text(text_t)
image_embed = model.encode_image(image_t)
text_embed = text_embed.squeeze().cpu().tolist()
image_embed = image_embed.squeeze().cpu().tolist()
text_embeddings.append(text_embed)
image_embeddings.append(image_embed)
text_embeddings = np.array(text_embeddings)
image_embeddings = np.array(image_embeddings) | code |
90111237/cell_16 | [
"image_output_1.png"
] | from pathlib import Path
from pathlib import Path
from pytorch_lightning import LightningModule
from sklearn.manifold import TSNE
from tokenizers import Tokenizer
from torchvision import models
from tqdm import tqdm
import albumentations as A
import cv2
import math
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import numpy as np
import pandas as pd
from pathlib import Path
import os
BASE_PATH = Path('/kaggle/input/h-and-m-personalized-fashion-recommendations/')
MODEL_PATH = Path('/kaggle/input/image-text-embeddings/ssl_resnet18_1337.ckpt')
TOKENIZER_PATH = Path('/kaggle/input/image-text-embeddings/tokenizer.json')
from tokenizers import Tokenizer
TOKENIZER = Tokenizer.from_file(str(TOKENIZER_PATH))
CLS_IDX = TOKENIZER.token_to_id('[CLS]')
PAD_IDX = TOKENIZER.token_to_id('[PAD]')
SEP_IDX = TOKENIZER.token_to_id('[SEP]')
def tokenize(text: str):
raw_tokens = TOKENIZER.encode(text)
return raw_tokens.ids
def pad_list(list_integers, context_size: int=90, pad_val: int=PAD_IDX, mode='right'):
"""
:param list_integers:
:param context_size:
:param pad_val:
:param mode:
:return:
"""
list_integers = list_integers[:context_size]
if len(list_integers) < context_size:
if mode == 'left':
list_integers = [pad_val] * (context_size - len(list_integers)) + list_integers
else:
list_integers = list_integers + [pad_val] * (context_size - len(list_integers))
return list_integers
import random
from pathlib import Path
import cv2
import numpy as np
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import albumentations as A
SIZE = 128
SCALE = 255.0
RESIZE = A.Compose([A.LongestMaxSize(max_size=SIZE, p=1.0), A.PadIfNeeded(min_height=SIZE, min_width=SIZE, p=1.0)])
NORMALIZE = A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=SCALE)
def read_image(image_path: Path) -> np.ndarray:
bgr_image = cv2.imread(str(image_path))
rgb_image = bgr_image[:, :, ::-1]
return rgb_image
def resize(image: np.ndarray) -> np.ndarray:
reshaped = RESIZE(image=image)['image']
return reshaped
def normalize(image: np.ndarray) -> np.ndarray:
normalized = NORMALIZE(image=image)['image']
return normalized
def preprocess(image: np.ndarray) -> np.ndarray:
return normalize(resize(image))
import math
import torch
import torch.nn.functional as F
from pytorch_lightning import LightningModule
from torchvision import models
from transformers import get_cosine_schedule_with_warmup
class PositionalEncoding(torch.nn.Module):
def __init__(self, d_model: int, dropout: float=0.1, max_len: int=5000):
super().__init__()
self.dropout = torch.nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model)
pe[0:, :, 0::2] = torch.sin(position * div_term)
pe[0:, :, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
self.d_model = d_model
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: Tensor, shape [seq_len, batch_size, embedding_dim]
"""
x = x + self.pe[:, :x.size(1)] / math.sqrt(self.d_model)
return self.dropout(x)
class Cola(LightningModule):
def __init__(self, lr=0.001, use_pretrained=False, dropout=0.2, d_model=128, n_vocab=30000, smoothing=0.1):
super().__init__()
self.dropout = dropout
self.lr = lr
self.d_model = d_model
self.n_vocab = n_vocab
self.smoothing = smoothing
self.model = models.resnet18(pretrained=use_pretrained)
self.model.fc = torch.nn.Linear(self.model.fc.in_features, self.d_model)
self.item_embeddings = torch.nn.Embedding(self.n_vocab, self.d_model)
self.pos_encoder = PositionalEncoding(d_model=self.d_model, dropout=self.dropout)
encoder_layer = torch.nn.TransformerEncoderLayer(d_model=self.d_model, nhead=4, dropout=self.dropout, batch_first=True)
self.encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers=4)
self.layer_norm = torch.nn.LayerNorm(normalized_shape=self.d_model)
self.linear = torch.nn.Linear(self.d_model, self.d_model, bias=False)
self.do = torch.nn.Dropout(p=self.dropout)
self.save_hyperparameters()
def encode_image(self, x):
x = x.permute(0, 3, 1, 2)
x = self.do(self.model(x))
x = torch.tanh(self.layer_norm(x))
return x
def encode_text(self, x):
x = self.item_embeddings(x)
x = self.pos_encoder(x)
x = self.encoder(x)
return x[:, 0, :]
def forward(self, x):
image, text = x
encoded_image = self.encode_image(image)
encoded_image_w = self.linear(encoded_image)
encoded_text = self.encode_text(text)
return (encoded_image_w, encoded_text)
df = pd.read_csv(BASE_PATH / 'articles.csv', nrows=None, dtype={'article_id': str})
df['text'] = df.apply(lambda x: ' '.join([str(x['prod_name']), str(x['product_type_name']), str(x['product_group_name']), str(x['graphical_appearance_name']), str(x['colour_group_name']), str(x['perceived_colour_value_name']), str(x['index_name']), str(x['section_name']), str(x['detail_desc'])]), axis=1)
df['image_path'] = df.article_id.apply(lambda x: BASE_PATH / 'images' / x[:3] / f'{x}.jpg')
df = df.sample(n=5000)
model = Cola(lr=0.0001)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.load_state_dict(torch.load(MODEL_PATH, map_location=device)['state_dict'])
model.to(device)
model.eval()
text_embeddings = []
image_embeddings = []
for image_path, text in tqdm(zip(df.image_path.values, df.text.values), total=len(df)):
if image_path.is_file():
image = read_image(image_path)
else:
image = np.zeros((128, 128, 3))
image = preprocess(image)
image_t = torch.from_numpy(image).unsqueeze(0)
image_t = image_t.to(device)
text_t = tokenize(text)
text_t = torch.tensor(pad_list(text_t), dtype=torch.long, device=device).unsqueeze(0)
with torch.no_grad():
text_embed = model.encode_text(text_t)
image_embed = model.encode_image(image_t)
text_embed = text_embed.squeeze().cpu().tolist()
image_embed = image_embed.squeeze().cpu().tolist()
text_embeddings.append(text_embed)
image_embeddings.append(image_embed)
text_embeddings = np.array(text_embeddings)
image_embeddings = np.array(image_embeddings)
tsne = TSNE(
n_components=2,
init="random",
random_state=0,
learning_rate="auto",
n_iter=300,
)
Y = tsne.fit_transform(image_embeddings)
fig = plt.figure(figsize=(12, 12))
for index_name in df.index_name.unique():
plt.scatter(Y[df.index_name == index_name, 0], Y[df.index_name == index_name, 1], label=index_name, s=3)
plt.title("Cola Image embeddings by index_name")
plt.legend()
plt.show()
tsne = TSNE(
n_components=2,
init="random",
random_state=0,
learning_rate="auto",
n_iter=300,
)
Y = tsne.fit_transform(text_embeddings)
fig = plt.figure(figsize=(12, 12))
for index_name in df.index_name.unique():
plt.scatter(Y[df.index_name == index_name, 0], Y[df.index_name == index_name, 1], label=index_name, s=3)
plt.title("Cola Text embeddings by index_name")
plt.legend()
plt.show()
index = 10
most_similar = np.argsort(-image_embeddings @ image_embeddings[index, :].T)[:9].tolist()
_, axs = plt.subplots(3, 3, figsize=(12, 12))
axs = axs.flatten()
for i, ax in zip(most_similar, axs):
ax.imshow(read_image(df.image_path.values[i]))
ax.axis('off')
if i == index:
ax.title.set_text('Query image')
else:
ax.title.set_text('Result Image')
plt.axis('off')
plt.show() | code |
90111237/cell_14 | [
"image_output_1.png"
] | from pathlib import Path
from pathlib import Path
from pytorch_lightning import LightningModule
from sklearn.manifold import TSNE
from tokenizers import Tokenizer
from torchvision import models
from tqdm import tqdm
import albumentations as A
import cv2
import math
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch
import numpy as np
import pandas as pd
from pathlib import Path
import os
BASE_PATH = Path('/kaggle/input/h-and-m-personalized-fashion-recommendations/')
MODEL_PATH = Path('/kaggle/input/image-text-embeddings/ssl_resnet18_1337.ckpt')
TOKENIZER_PATH = Path('/kaggle/input/image-text-embeddings/tokenizer.json')
from tokenizers import Tokenizer
TOKENIZER = Tokenizer.from_file(str(TOKENIZER_PATH))
CLS_IDX = TOKENIZER.token_to_id('[CLS]')
PAD_IDX = TOKENIZER.token_to_id('[PAD]')
SEP_IDX = TOKENIZER.token_to_id('[SEP]')
def tokenize(text: str):
raw_tokens = TOKENIZER.encode(text)
return raw_tokens.ids
def pad_list(list_integers, context_size: int=90, pad_val: int=PAD_IDX, mode='right'):
"""
:param list_integers:
:param context_size:
:param pad_val:
:param mode:
:return:
"""
list_integers = list_integers[:context_size]
if len(list_integers) < context_size:
if mode == 'left':
list_integers = [pad_val] * (context_size - len(list_integers)) + list_integers
else:
list_integers = list_integers + [pad_val] * (context_size - len(list_integers))
return list_integers
import random
from pathlib import Path
import cv2
import numpy as np
cv2.setNumThreads(0)
cv2.ocl.setUseOpenCL(False)
import albumentations as A
SIZE = 128
SCALE = 255.0
RESIZE = A.Compose([A.LongestMaxSize(max_size=SIZE, p=1.0), A.PadIfNeeded(min_height=SIZE, min_width=SIZE, p=1.0)])
NORMALIZE = A.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225], max_pixel_value=SCALE)
def read_image(image_path: Path) -> np.ndarray:
bgr_image = cv2.imread(str(image_path))
rgb_image = bgr_image[:, :, ::-1]
return rgb_image
def resize(image: np.ndarray) -> np.ndarray:
reshaped = RESIZE(image=image)['image']
return reshaped
def normalize(image: np.ndarray) -> np.ndarray:
normalized = NORMALIZE(image=image)['image']
return normalized
def preprocess(image: np.ndarray) -> np.ndarray:
return normalize(resize(image))
import math
import torch
import torch.nn.functional as F
from pytorch_lightning import LightningModule
from torchvision import models
from transformers import get_cosine_schedule_with_warmup
class PositionalEncoding(torch.nn.Module):
def __init__(self, d_model: int, dropout: float=0.1, max_len: int=5000):
super().__init__()
self.dropout = torch.nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model)
pe[0:, :, 0::2] = torch.sin(position * div_term)
pe[0:, :, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
self.d_model = d_model
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x: Tensor, shape [seq_len, batch_size, embedding_dim]
"""
x = x + self.pe[:, :x.size(1)] / math.sqrt(self.d_model)
return self.dropout(x)
class Cola(LightningModule):
def __init__(self, lr=0.001, use_pretrained=False, dropout=0.2, d_model=128, n_vocab=30000, smoothing=0.1):
super().__init__()
self.dropout = dropout
self.lr = lr
self.d_model = d_model
self.n_vocab = n_vocab
self.smoothing = smoothing
self.model = models.resnet18(pretrained=use_pretrained)
self.model.fc = torch.nn.Linear(self.model.fc.in_features, self.d_model)
self.item_embeddings = torch.nn.Embedding(self.n_vocab, self.d_model)
self.pos_encoder = PositionalEncoding(d_model=self.d_model, dropout=self.dropout)
encoder_layer = torch.nn.TransformerEncoderLayer(d_model=self.d_model, nhead=4, dropout=self.dropout, batch_first=True)
self.encoder = torch.nn.TransformerEncoder(encoder_layer, num_layers=4)
self.layer_norm = torch.nn.LayerNorm(normalized_shape=self.d_model)
self.linear = torch.nn.Linear(self.d_model, self.d_model, bias=False)
self.do = torch.nn.Dropout(p=self.dropout)
self.save_hyperparameters()
def encode_image(self, x):
x = x.permute(0, 3, 1, 2)
x = self.do(self.model(x))
x = torch.tanh(self.layer_norm(x))
return x
def encode_text(self, x):
x = self.item_embeddings(x)
x = self.pos_encoder(x)
x = self.encoder(x)
return x[:, 0, :]
def forward(self, x):
image, text = x
encoded_image = self.encode_image(image)
encoded_image_w = self.linear(encoded_image)
encoded_text = self.encode_text(text)
return (encoded_image_w, encoded_text)
df = pd.read_csv(BASE_PATH / 'articles.csv', nrows=None, dtype={'article_id': str})
df['text'] = df.apply(lambda x: ' '.join([str(x['prod_name']), str(x['product_type_name']), str(x['product_group_name']), str(x['graphical_appearance_name']), str(x['colour_group_name']), str(x['perceived_colour_value_name']), str(x['index_name']), str(x['section_name']), str(x['detail_desc'])]), axis=1)
df['image_path'] = df.article_id.apply(lambda x: BASE_PATH / 'images' / x[:3] / f'{x}.jpg')
df = df.sample(n=5000)
model = Cola(lr=0.0001)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.load_state_dict(torch.load(MODEL_PATH, map_location=device)['state_dict'])
model.to(device)
model.eval()
text_embeddings = []
image_embeddings = []
for image_path, text in tqdm(zip(df.image_path.values, df.text.values), total=len(df)):
if image_path.is_file():
image = read_image(image_path)
else:
image = np.zeros((128, 128, 3))
image = preprocess(image)
image_t = torch.from_numpy(image).unsqueeze(0)
image_t = image_t.to(device)
text_t = tokenize(text)
text_t = torch.tensor(pad_list(text_t), dtype=torch.long, device=device).unsqueeze(0)
with torch.no_grad():
text_embed = model.encode_text(text_t)
image_embed = model.encode_image(image_t)
text_embed = text_embed.squeeze().cpu().tolist()
image_embed = image_embed.squeeze().cpu().tolist()
text_embeddings.append(text_embed)
image_embeddings.append(image_embed)
text_embeddings = np.array(text_embeddings)
image_embeddings = np.array(image_embeddings)
tsne = TSNE(
n_components=2,
init="random",
random_state=0,
learning_rate="auto",
n_iter=300,
)
Y = tsne.fit_transform(image_embeddings)
fig = plt.figure(figsize=(12, 12))
for index_name in df.index_name.unique():
plt.scatter(Y[df.index_name == index_name, 0], Y[df.index_name == index_name, 1], label=index_name, s=3)
plt.title("Cola Image embeddings by index_name")
plt.legend()
plt.show()
tsne = TSNE(n_components=2, init='random', random_state=0, learning_rate='auto', n_iter=300)
Y = tsne.fit_transform(text_embeddings)
fig = plt.figure(figsize=(12, 12))
for index_name in df.index_name.unique():
plt.scatter(Y[df.index_name == index_name, 0], Y[df.index_name == index_name, 1], label=index_name, s=3)
plt.title('Cola Text embeddings by index_name')
plt.legend()
plt.show() | code |
88083134/cell_4 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns | code |
88083134/cell_6 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.info() | code |
88083134/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum() | code |
88083134/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import SMOTE
plt.style.use('fivethirtyeight')
import warnings
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text, export_graphviz
import graphviz
from sklearn.metrics import confusion_matrix, plot_confusion_matrix, classification_report, RocCurveDisplay
from sklearn.model_selection import train_test_split, GridSearchCV
warnings.filterwarnings('ignore')
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum()
sns.kdeplot(drug['Age'], hue=drug['Drug']) | code |
88083134/cell_8 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum()
drug.describe() | code |
88083134/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import SMOTE
plt.style.use('fivethirtyeight')
import warnings
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text, export_graphviz
import graphviz
from sklearn.metrics import confusion_matrix, plot_confusion_matrix, classification_report, RocCurveDisplay
from sklearn.model_selection import train_test_split, GridSearchCV
warnings.filterwarnings('ignore')
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum()
plt.figure(figsize=(12, 5))
plt.subplot(121)
sns.distplot(drug['Na_to_K'], kde=False, color='violet')
plt.subplot(122)
sns.kdeplot(drug['Na_to_K'], shade=True, color='teal')
plt.axvline(drug['Na_to_K'].mean(), color='red', label='mean->' + str(drug['Na_to_K'].mean()))
plt.axvline(drug['Na_to_K'].median(), color='black', label='median->' + str(drug['Na_to_K'].median()))
plt.legend(bbox_to_anchor=(1.3, 1))
plt.suptitle('Distribution of the Sodium to Potassium Ratio Variable', fontsize=20, color='Orangered', fontstyle='italic')
plt.show() | code |
88083134/cell_3 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.head() | code |
88083134/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import SMOTE
plt.style.use('fivethirtyeight')
import warnings
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text, export_graphviz
import graphviz
from sklearn.metrics import confusion_matrix, plot_confusion_matrix, classification_report, RocCurveDisplay
from sklearn.model_selection import train_test_split, GridSearchCV
warnings.filterwarnings('ignore')
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum()
plt.figure(figsize=(12, 5))
plt.subplot(121)
sns.distplot(drug['Age'], kde=False, color='green')
plt.subplot(122)
sns.kdeplot(drug['Age'], shade=True, color='orangered')
plt.axvline(drug['Age'].mean(), color='red', label='mean->' + str(drug['Age'].mean()))
plt.axvline(drug['Age'].median(), color='black', label='median->' + str(drug['Age'].median()))
plt.legend(bbox_to_anchor=(1.2, 1))
plt.suptitle('Distribution of the Age Variable', fontsize=20, color='DarkSlateBlue', fontstyle='italic')
plt.show() | code |
88083134/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import SMOTE
plt.style.use('fivethirtyeight')
import warnings
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text, export_graphviz
import graphviz
from sklearn.metrics import confusion_matrix, plot_confusion_matrix, classification_report, RocCurveDisplay
from sklearn.model_selection import train_test_split, GridSearchCV
warnings.filterwarnings('ignore')
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum()
sns.countplot(drug['Drug'], orient='v')
plt.show() | code |
88083134/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import OneHotEncoder, LabelBinarizer, StandardScaler
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import SMOTE
plt.style.use('fivethirtyeight')
import warnings
from sklearn.tree import DecisionTreeClassifier, plot_tree, export_text, export_graphviz
import graphviz
from sklearn.metrics import confusion_matrix, plot_confusion_matrix, classification_report, RocCurveDisplay
from sklearn.model_selection import train_test_split, GridSearchCV
warnings.filterwarnings('ignore')
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape)
drug.isnull().sum().sum()
plt.figure(figsize=(14, 5))
plt.subplot(131)
plt.pie(drug['Sex'].value_counts(), labels=drug['Sex'].value_counts().index, autopct='%0.2f%%', wedgeprops={'width': 0.2}, shadow=True)
plt.title('Gender Composition')
plt.subplot(132)
plt.pie(drug['BP'].value_counts(), labels=drug['BP'].value_counts().index, autopct='%0.2f%%', wedgeprops={'width': 0.3}, shadow=True, colors=['red', 'green', 'orange'], pctdistance=0.4)
plt.title('Blood Pressure Composition')
plt.subplot(133)
plt.pie(drug['Cholesterol'].value_counts(), labels=drug['Cholesterol'].value_counts().index, autopct='%0.2f%%', wedgeprops={'width': 0.2}, shadow=True)
plt.title('Cholesterol Composition')
plt.show() | code |
88083134/cell_5 | [
"image_output_1.png"
] | import pandas as pd
drug = pd.read_csv('../input/drug-classification/drug200.csv')
drug.columns
(drug.size, drug.shape) | code |
122246772/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name', 'Cabin'], axis=1, inplace=True)
df.groupby('Sex').size()
df.drop(['Ticket'], axis=1, inplace=True)
df.isnull().sum()
df.isna().sum()
df_train = df[df['Survived'].isna() == False]
y = df_train.loc[:, 'Survived']
y
X = df_train.drop(['Survived'], axis=1)
X | code |
122246772/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name', 'Cabin'], axis=1, inplace=True)
df.info() | code |
122246772/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
df_test.head() | code |
122246772/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
model2 = rfc.fit(X_train, y_train)
y_hat2 = model2.predict(X_test)
f1 = f1_score(y_test, y_hat2)
acc = accuracy_score(y_test, y_hat2)
(f1, acc)
parameters = {'booster': ['gbtree', 'gblinear', 'dart'], 'learning_rate': [0.1, 0.3, 0.6, 0.9, 1], 'n_estimators': [0.1, 1, 10, 15]}
grid = GridSearchCV(xgb, param_grid=parameters, cv=5, verbose=0)
grid.fit(X_train, y_train)
xgb = XGBClassifier(booster='gbtree', learning_rate=0.3, n_estimators=10)
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.model_selection import GridSearchCV
rfc = RandomForestClassifier()
parameters = {'criterion': ['gini', 'entropy'], 'max_features': ['auto', 'sqrt', 'log2'], 'n_estimators': [15, 50, 100, 200]}
grid = GridSearchCV(rfc, param_grid=parameters, cv=5, verbose=1)
grid.fit(X_train, y_train)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(criterion='entropy', max_features='auto', n_estimators=50, random_state=123)
model3 = rfc.fit(X_train, y_train)
y_hat3 = model3.predict(X_test)
f1 = f1_score(y_test, y_hat3)
acc = accuracy_score(y_test, y_hat3)
(f1, acc)
from sklearn.ensemble import GradientBoostingClassifier
gb = GradientBoostingClassifier()
parameters = {'loss': ['deviance', 'exponential'], 'criterion': ['friedman_mse', 'squared_error'], 'learning_rate': [0.1, 0.3, 10], 'n_estimators': [10, 100, 200]}
grid = GridSearchCV(gb, param_grid=parameters, cv=5, verbose=1)
grid.fit(X_train, y_train)
gb = GradientBoostingClassifier(criterion='friedman_mse', learning_rate=0.1, loss='exponential', n_estimators=100, random_state=123)
model = gb.fit(X_train, y_train)
y_hat = model.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc) | code |
122246772/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import Normalizer
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name', 'Cabin'], axis=1, inplace=True)
df.groupby('Sex').size()
df.drop(['Ticket'], axis=1, inplace=True)
df.isnull().sum()
df.isna().sum()
df_train = df[df['Survived'].isna() == False]
y = df_train.loc[:, 'Survived']
y
X = df_train.drop(['Survived'], axis=1)
X
norm = Normalizer()
X = norm.fit_transform(X)
X | code |
122246772/cell_30 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
model2 = rfc.fit(X_train, y_train)
y_hat2 = model2.predict(X_test)
f1 = f1_score(y_test, y_hat2)
acc = accuracy_score(y_test, y_hat2)
(f1, acc)
xgb = XGBClassifier(booster='gbtree', learning_rate=0.3, n_estimators=10)
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc) | code |
122246772/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
model2 = rfc.fit(X_train, y_train)
y_hat2 = model2.predict(X_test)
f1 = f1_score(y_test, y_hat2)
acc = accuracy_score(y_test, y_hat2)
(f1, acc)
parameters = {'booster': ['gbtree', 'gblinear', 'dart'], 'learning_rate': [0.1, 0.3, 0.6, 0.9, 1], 'n_estimators': [0.1, 1, 10, 15]}
grid = GridSearchCV(xgb, param_grid=parameters, cv=5, verbose=0)
grid.fit(X_train, y_train)
from sklearn.model_selection import GridSearchCV
rfc = RandomForestClassifier()
parameters = {'criterion': ['gini', 'entropy'], 'max_features': ['auto', 'sqrt', 'log2'], 'n_estimators': [15, 50, 100, 200]}
grid = GridSearchCV(rfc, param_grid=parameters, cv=5, verbose=1)
grid.fit(X_train, y_train)
from sklearn.ensemble import GradientBoostingClassifier
gb = GradientBoostingClassifier()
parameters = {'loss': ['deviance', 'exponential'], 'criterion': ['friedman_mse', 'squared_error'], 'learning_rate': [0.1, 0.3, 10], 'n_estimators': [10, 100, 200]}
grid = GridSearchCV(gb, param_grid=parameters, cv=5, verbose=1)
grid.fit(X_train, y_train)
print(grid.best_params_) | code |
122246772/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name', 'Cabin'], axis=1, inplace=True)
df.groupby('Sex').size()
df.drop(['Ticket'], axis=1, inplace=True)
df.isnull().sum()
df.isna().sum()
df_train = df[df['Survived'].isna() == False]
y = df_train.loc[:, 'Survived']
y | code |
122246772/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.tail() | code |
122246772/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
parameters = {'booster': ['gbtree', 'gblinear', 'dart'], 'learning_rate': [0.1, 0.3, 0.6, 0.9, 1], 'n_estimators': [0.1, 1, 10, 15]}
grid = GridSearchCV(xgb, param_grid=parameters, cv=5, verbose=0)
grid.fit(X_train, y_train)
print(grid.best_params_) | code |
122246772/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import f1_score ,accuracy_score
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc) | code |
122246772/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122246772/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum() | code |
122246772/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
model2 = rfc.fit(X_train, y_train)
y_hat2 = model2.predict(X_test)
f1 = f1_score(y_test, y_hat2)
acc = accuracy_score(y_test, y_hat2)
(f1, acc)
parameters = {'booster': ['gbtree', 'gblinear', 'dart'], 'learning_rate': [0.1, 0.3, 0.6, 0.9, 1], 'n_estimators': [0.1, 1, 10, 15]}
grid = GridSearchCV(xgb, param_grid=parameters, cv=5, verbose=0)
grid.fit(X_train, y_train)
xgb = XGBClassifier(booster='gbtree', learning_rate=0.3, n_estimators=10)
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.model_selection import GridSearchCV
rfc = RandomForestClassifier()
parameters = {'criterion': ['gini', 'entropy'], 'max_features': ['auto', 'sqrt', 'log2'], 'n_estimators': [15, 50, 100, 200]}
grid = GridSearchCV(rfc, param_grid=parameters, cv=5, verbose=1)
grid.fit(X_train, y_train)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(criterion='entropy', max_features='auto', n_estimators=50, random_state=123)
model3 = rfc.fit(X_train, y_train)
y_hat3 = model3.predict(X_test)
f1 = f1_score(y_test, y_hat3)
acc = accuracy_score(y_test, y_hat3)
(f1, acc) | code |
122246772/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.info() | code |
122246772/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_train.head() | code |
122246772/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name', 'Cabin'], axis=1, inplace=True)
df.groupby('Sex').size()
df.drop(['Ticket'], axis=1, inplace=True)
df.isnull().sum()
df.isna().sum() | code |
122246772/cell_31 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
model2 = rfc.fit(X_train, y_train)
y_hat2 = model2.predict(X_test)
f1 = f1_score(y_test, y_hat2)
acc = accuracy_score(y_test, y_hat2)
(f1, acc)
parameters = {'booster': ['gbtree', 'gblinear', 'dart'], 'learning_rate': [0.1, 0.3, 0.6, 0.9, 1], 'n_estimators': [0.1, 1, 10, 15]}
grid = GridSearchCV(xgb, param_grid=parameters, cv=5, verbose=0)
grid.fit(X_train, y_train)
from sklearn.model_selection import GridSearchCV
rfc = RandomForestClassifier()
parameters = {'criterion': ['gini', 'entropy'], 'max_features': ['auto', 'sqrt', 'log2'], 'n_estimators': [15, 50, 100, 200]}
grid = GridSearchCV(rfc, param_grid=parameters, cv=5, verbose=1)
grid.fit(X_train, y_train)
print(grid.best_params_) | code |
122246772/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name', 'Cabin'], axis=1, inplace=True)
df.groupby('Sex').size()
df.drop(['Ticket'], axis=1, inplace=True)
df.isnull().sum() | code |
122246772/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape)
df = pd.concat([df_train, df_test], axis=0)
df.isnull().sum()
df.drop(['Name', 'Cabin'], axis=1, inplace=True)
df.groupby('Sex').size() | code |
122246772/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score ,accuracy_score
from xgboost import XGBClassifier
xgb = XGBClassifier()
model1 = xgb.fit(X_train, y_train)
y_hat = model1.predict(X_test)
f1 = f1_score(y_test, y_hat)
acc = accuracy_score(y_test, y_hat)
(f1, acc)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
model2 = rfc.fit(X_train, y_train)
y_hat2 = model2.predict(X_test)
f1 = f1_score(y_test, y_hat2)
acc = accuracy_score(y_test, y_hat2)
(f1, acc) | code |
122246772/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/titanic/train.csv')
df_test = pd.read_csv('/kaggle/input/titanic/test.csv')
(df_train.shape, df_test.shape) | code |
128030195/cell_13 | [
"text_plain_output_1.png"
] | from keras.models import Model, load_model
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout
from tensorflow.keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten ,Activation,Dropout,BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Sequential
import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='training', interpolation='bilinear')
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images_validation = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='validation', interpolation='bilinear')
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2, restore_best_weights=True)
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import Model
import tensorflow.keras as keras
resnet = ResNet50(include_top=False, weights='imagenet', input_shape=(256, 256, 3), pooling='max')
output = resnet.layers[-1].output
output = tf.keras.layers.Flatten()(output)
resnet = Model(resnet.input, output)
res_name = []
for layer in resnet.layers:
res_name.append(layer.name)
set_trainable = False
for layer in resnet.layers:
if layer.name in res_name[-22:]:
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout
model6 = Sequential()
model6.add(resnet)
model6.add(BatchNormalization())
model6.add(Dense(2048, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(1024, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(512, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(256, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(128, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(64, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(31, activation='softmax'))
model6.summary() | code |
128030195/cell_4 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_11.png",
"text_plain_output_12.png"
] | import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='training', interpolation='bilinear')
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images_validation = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='validation', interpolation='bilinear') | code |
128030195/cell_2 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import os
import cv2
import pickle
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tqdm import tqdm
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
from keras.models import Model, load_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.keras import activations
from tensorflow.keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten, Activation, Dropout, BatchNormalization
import glob
import skimage
plt.rc('font', size=14)
plt.rc('axes', labelsize=14, titlesize=14)
plt.rc('legend', fontsize=14)
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10) | code |
128030195/cell_1 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128030195/cell_7 | [
"image_output_1.png"
] | import plotly.express as px
import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='training', interpolation='bilinear')
class_names = images.class_names
class_names
px.pie(names=class_names, title='Train').show() | code |
128030195/cell_8 | [
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"text_plain_output_285.png",
"text_plain_output_306.png",
"text_plain_output_493.png",
"text_plain_output_46.png"
] | import matplotlib.pyplot as plt
import tensorflow as tf
import os
import cv2
import pickle
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tqdm import tqdm
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
from keras.models import Model, load_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.keras import activations
from tensorflow.keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten, Activation, Dropout, BatchNormalization
import glob
import skimage
plt.rc('font', size=14)
plt.rc('axes', labelsize=14, titlesize=14)
plt.rc('legend', fontsize=14)
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='training', interpolation='bilinear')
class_names = images.class_names
class_names
plt.figure(figsize=(10, 10))
for image, label in images.take(1):
for i in range(25):
ax = plt.subplot(5, 5, i + 1)
plt.imshow(image[i].numpy().astype('uint8'))
plt.title(class_names[label[i]])
plt.axis('off') | code |
128030195/cell_16 | [
"text_plain_output_1.png"
] | from keras.models import Model, load_model
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout
from tensorflow.keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten ,Activation,Dropout,BatchNormalization
from tensorflow.keras.models import Model
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
import tensorflow as tf
import os
import cv2
import pickle
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tqdm import tqdm
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import confusion_matrix
from keras.models import Model, load_model
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.keras import activations
from tensorflow.keras.layers import Dense, Input, Conv2D, MaxPool2D, Flatten, Activation, Dropout, BatchNormalization
import glob
import skimage
plt.rc('font', size=14)
plt.rc('axes', labelsize=14, titlesize=14)
plt.rc('legend', fontsize=14)
plt.rc('xtick', labelsize=10)
plt.rc('ytick', labelsize=10)
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='training', interpolation='bilinear')
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images_validation = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='validation', interpolation='bilinear')
class_names = images.class_names
class_names
plt.figure(figsize=(10, 10))
for image, label in images.take(1):
for i in range(25):
ax = plt.subplot(5, 5, i +1)
plt.imshow(image[i].numpy().astype("uint8"))
plt.title(class_names[label[i]])
plt.axis("off")
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2, restore_best_weights=True)
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import Model
import tensorflow.keras as keras
resnet = ResNet50(include_top=False, weights='imagenet', input_shape=(256, 256, 3), pooling='max')
output = resnet.layers[-1].output
output = tf.keras.layers.Flatten()(output)
resnet = Model(resnet.input, output)
res_name = []
for layer in resnet.layers:
res_name.append(layer.name)
set_trainable = False
for layer in resnet.layers:
if layer.name in res_name[-22:]:
set_trainable = True
if set_trainable:
layer.trainable = True
else:
layer.trainable = False
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout
model6 = Sequential()
model6.add(resnet)
model6.add(BatchNormalization())
model6.add(Dense(2048, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(1024, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(512, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(256, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(128, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(64, activation='relu'))
model6.add(Dropout(0.2))
model6.add(Dense(31, activation='softmax'))
model6.summary()
model6.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False), metrics=['accuracy'])
EPOCHS = 25
BATCH_SIZE = 64
history6 = model6.fit(images, epochs=EPOCHS, batch_size=BATCH_SIZE, verbose=1, validation_data=images_validation, callbacks=[early_stop]) | code |
128030195/cell_3 | [
"image_output_1.png"
] | import tensorflow as tf
data_dir = '/kaggle/input/rgb-arabic-alphabets-sign-language-dataset-jpg/images_after'
images = tf.keras.utils.image_dataset_from_directory(data_dir, labels='inferred', label_mode='int', class_names=None, color_mode='rgb', batch_size=32, image_size=(256, 256), shuffle=True, seed=123, validation_split=0.2, subset='training', interpolation='bilinear') | code |
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