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|---|---|---|---|
34127932/cell_54 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape) | code |
34127932/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
train_data.describe() | code |
34127932/cell_52 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
dataTrain.head() | code |
34127932/cell_49 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.countplot(x='embarked', hue='survived', data=dataTrain) | code |
34127932/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
sns.countplot(x='pclass', hue='survived', data=dataTrain) | code |
34127932/cell_62 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import numpy as np #for mathematical manipulation of the data
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape)
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
stdscale = MinMaxScaler()
x_new = stdscale.fit_transform(x)
testd = stdscale.transform(dataTest)
(x_new.shape, testd.shape)
X = pd.DataFrame(x_new, columns=x.columns)
testData = pd.DataFrame(testd, columns=dataTest.columns)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
(x_train.shape, y_train.shape)
np.unique(y_train) | code |
34127932/cell_58 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
y = dataTrain['survived']
x = dataTrain.drop('survived', axis=1)
(x.shape, y.shape)
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler
stdscale = MinMaxScaler()
x_new = stdscale.fit_transform(x)
testd = stdscale.transform(dataTest)
(x_new.shape, testd.shape)
X = pd.DataFrame(x_new, columns=x.columns)
testData = pd.DataFrame(testd, columns=dataTest.columns)
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
(x_train.shape, y_train.shape) | code |
34127932/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
train_data.info() | code |
34127932/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTest.head() | code |
34127932/cell_3 | [
"text_plain_output_1.png"
] | import os
import os
os.getcwd()
os.chdir('/kaggle/input')
os.listdir() | code |
34127932/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain['pclass'].value_counts() | code |
34127932/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.distplot(dataTrain['fare']) | code |
34127932/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain.head() | code |
34127932/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTrain.head() | code |
34127932/cell_53 | [
"text_html_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTest = pd.get_dummies(dataTest, columns=['sex'])
dataTrain = pd.get_dummies(dataTrain, columns=['embarked'])
dataTest = pd.get_dummies(dataTest, columns=['embarked'])
dataTest.head() | code |
34127932/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
train_data.head() | code |
34127932/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd #for structuring the data
import seaborn as sns #for visualization
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
sns.countplot(x='sibsp', hue='survived', data=dataTrain) | code |
34127932/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #for structuring the data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')
dataTrain = train_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
passengerid = test_data['passenger_ID']
dataTest = test_data.drop(['passenger_ID', 'name', 'ticket', 'cabin'], axis=1)
dataTrain = pd.get_dummies(dataTrain, columns=['sex'])
dataTrain['sibsp'].value_counts() | code |
16114195/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
train['SalePrice'].hist(bins=50)
y = train['SalePrice'].reset_index(drop=True) | code |
16114195/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
test.describe() | code |
16114195/cell_11 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(drop=True)
train = train.drop(['Id', 'SalePrice'], axis=1)
test = test.drop(['Id'], axis=1)
x = pd.concat([train, test]).reset_index(drop=True)
x.info() | code |
16114195/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train['SalePrice'].hist(bins=50) | code |
16114195/cell_10 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(drop=True)
train = train.drop(['Id', 'SalePrice'], axis=1)
test = test.drop(['Id'], axis=1)
x = pd.concat([train, test]).reset_index(drop=True)
x.describe() | code |
16114195/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
train.reset_index(drop=True, inplace=True)
train['SalePrice'] = np.log1p(train['SalePrice'])
y = train['SalePrice'].reset_index(drop=True)
train = train.drop(['Id', 'SalePrice'], axis=1)
test = test.drop(['Id'], axis=1)
x = pd.concat([train, test]).reset_index(drop=True)
x['MSSubClass'] = x['MSSubClass'].apply(str)
x['YrSold'] = x['YrSold'].astype(str)
x['MoSold'] = x['MoSold'].astype(str)
x['Functional'] = x['Functional'].fillna('Typ')
x['Electrical'] = x['Electrical'].fillna('SBrkr')
x['KitchenQual'] = x['KitchenQual'].fillna('TA')
x['Exterior1st'] = x['Exterior1st'].fillna(x['Exterior1st'].mode()[0])
x['Exterior2nd'] = x['Exterior2nd'].fillna(x['Exterior2nd'].mode()[0])
x['SaleType'] = x['SaleType'].fillna(x['SaleType'].mode()[0])
for col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):
x[col] = x[col].fillna(0)
for col in ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']:
x[col] = x[col].fillna('None')
for col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):
x[col] = x[col].fillna(0)
for col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):
x[col] = x[col].fillna('None')
objects = []
for i in x.columns:
if x[i].dtype == object:
objects.append(i)
x.update(x[objects].fillna('None'))
numeric_dtypes = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
numerics = []
for i in x.columns:
if x[i].dtype in numeric_dtypes:
numerics.append(i)
x.update(x[numerics].fillna(0))
x.info() | code |
16114195/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/train.csv')
test = pd.read_csv('../input/test.csv')
train.describe() | code |
129020867/cell_2 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from tensorflow.keras.models import Model
import numpy as np
import numpy as np
import tensorflow as tf
import tensorflow as tf
import numpy as np
import tensorflow as tf
def seed_everything(SEED):
np.random.seed(SEED)
tf.random.set_seed(SEED)
seed = 42
seed_everything(seed)
'\nResUNet++ architecture in Keras TensorFlow\n'
import os
import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
def squeeze_excite_block(inputs, ratio=8):
init = inputs
channel_axis = -1
filters = init.shape[channel_axis]
se_shape = (1, 1, filters)
se = GlobalAveragePooling2D()(init)
se = Reshape(se_shape)(se)
se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
x = Multiply()([init, se])
return x
def stem_block(x, n_filter, strides):
x_init = x
x = Conv2D(n_filter, (3, 3), padding='same', strides=strides)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same')(x)
s = Conv2D(n_filter, (1, 1), padding='same', strides=strides)(x_init)
s = BatchNormalization()(s)
x = Add()([x, s])
x = squeeze_excite_block(x)
return x
def resnet_block(x, n_filter, strides=1):
x_init = x
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same', strides=strides)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same', strides=1)(x)
s = Conv2D(n_filter, (1, 1), padding='same', strides=strides)(x_init)
s = BatchNormalization()(s)
x = Add()([x, s])
x = squeeze_excite_block(x)
return x
def aspp_block(x, num_filters, rate_scale=1):
x1 = Conv2D(num_filters, (3, 3), dilation_rate=(6 * rate_scale, 6 * rate_scale), padding='same')(x)
x1 = BatchNormalization()(x1)
x2 = Conv2D(num_filters, (3, 3), dilation_rate=(12 * rate_scale, 12 * rate_scale), padding='same')(x)
x2 = BatchNormalization()(x2)
x3 = Conv2D(num_filters, (3, 3), dilation_rate=(18 * rate_scale, 18 * rate_scale), padding='same')(x)
x3 = BatchNormalization()(x3)
x4 = Conv2D(num_filters, (3, 3), padding='same')(x)
x4 = BatchNormalization()(x4)
y = Add()([x1, x2, x3, x4])
y = Conv2D(num_filters, (1, 1), padding='same')(y)
return y
def attetion_block(g, x):
"""
g: Output of Parallel Encoder block
x: Output of Previous Decoder block
"""
filters = x.shape[-1]
g_conv = BatchNormalization()(g)
g_conv = Activation('relu')(g_conv)
g_conv = Conv2D(filters, (3, 3), padding='same')(g_conv)
g_pool = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(g_conv)
x_conv = BatchNormalization()(x)
x_conv = Activation('relu')(x_conv)
x_conv = Conv2D(filters, (3, 3), padding='same')(x_conv)
gc_sum = Add()([g_pool, x_conv])
gc_conv = BatchNormalization()(gc_sum)
gc_conv = Activation('relu')(gc_conv)
gc_conv = Conv2D(filters, (3, 3), padding='same')(gc_conv)
gc_mul = Multiply()([gc_conv, x])
return gc_mul
def build_model(input_size=512):
n_filters = [16, 32, 64, 128, 256]
inputs = Input((input_size, input_size, 3))
c0 = inputs
c1 = stem_block(c0, n_filters[0], strides=1)
c2 = resnet_block(c1, n_filters[1], strides=2)
c3 = resnet_block(c2, n_filters[2], strides=2)
c4 = resnet_block(c3, n_filters[3], strides=2)
b1 = aspp_block(c4, n_filters[4])
d1 = attetion_block(c3, b1)
d1 = UpSampling2D((2, 2))(d1)
d1 = Concatenate()([d1, c3])
d1 = resnet_block(d1, n_filters[3])
d2 = attetion_block(c2, d1)
d2 = UpSampling2D((2, 2))(d2)
d2 = Concatenate()([d2, c2])
d2 = resnet_block(d2, n_filters[2])
d3 = attetion_block(c1, d2)
d3 = UpSampling2D((2, 2))(d3)
d3 = Concatenate()([d3, c1])
d3 = resnet_block(d3, n_filters[1])
outputs = aspp_block(d3, n_filters[0])
outputs = Conv2D(1, (1, 1), padding='same')(outputs)
outputs = Activation('sigmoid')(outputs)
model = Model(inputs, outputs)
return model
unet = build_model()
unet.summary() | code |
129020867/cell_7 | [
"text_plain_output_1.png"
] | from skimage.io import imshow
from matplotlib import pyplot as plt
imshow(x_train.next()[0].astype(np.float32))
plt.show()
imshow(np.squeeze(y_train.next()[0].astype(np.float32)))
plt.show()
imshow(x_val.next()[0].astype(np.float32))
plt.show()
imshow(np.squeeze(y_val.next()[0].astype(np.float32)))
plt.show() | code |
129020867/cell_15 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from skimage.io import imshow
from matplotlib import pyplot as plt
imshow(x_test.next()[0].astype(np.float32))
plt.show()
imshow(np.squeeze(y_pred[0].astype(np.float32)))
plt.show()
imshow(y_test.next()[0].astype(np.float32))
plt.show() | code |
129020867/cell_17 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from keras.preprocessing import image
from keras.preprocessing import image
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam, Nadam, SGD
from tensorflow.keras.optimizers import Adam, Nadam, SGD
import csv
import csv
import numpy as np
import numpy as np
import numpy as np
import numpy as np
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
import numpy as np
import tensorflow as tf
def seed_everything(SEED):
np.random.seed(SEED)
tf.random.set_seed(SEED)
seed = 42
seed_everything(seed)
'\nResUNet++ architecture in Keras TensorFlow\n'
import os
import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
def squeeze_excite_block(inputs, ratio=8):
init = inputs
channel_axis = -1
filters = init.shape[channel_axis]
se_shape = (1, 1, filters)
se = GlobalAveragePooling2D()(init)
se = Reshape(se_shape)(se)
se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
x = Multiply()([init, se])
return x
def stem_block(x, n_filter, strides):
x_init = x
x = Conv2D(n_filter, (3, 3), padding='same', strides=strides)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same')(x)
s = Conv2D(n_filter, (1, 1), padding='same', strides=strides)(x_init)
s = BatchNormalization()(s)
x = Add()([x, s])
x = squeeze_excite_block(x)
return x
def resnet_block(x, n_filter, strides=1):
x_init = x
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same', strides=strides)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same', strides=1)(x)
s = Conv2D(n_filter, (1, 1), padding='same', strides=strides)(x_init)
s = BatchNormalization()(s)
x = Add()([x, s])
x = squeeze_excite_block(x)
return x
def aspp_block(x, num_filters, rate_scale=1):
x1 = Conv2D(num_filters, (3, 3), dilation_rate=(6 * rate_scale, 6 * rate_scale), padding='same')(x)
x1 = BatchNormalization()(x1)
x2 = Conv2D(num_filters, (3, 3), dilation_rate=(12 * rate_scale, 12 * rate_scale), padding='same')(x)
x2 = BatchNormalization()(x2)
x3 = Conv2D(num_filters, (3, 3), dilation_rate=(18 * rate_scale, 18 * rate_scale), padding='same')(x)
x3 = BatchNormalization()(x3)
x4 = Conv2D(num_filters, (3, 3), padding='same')(x)
x4 = BatchNormalization()(x4)
y = Add()([x1, x2, x3, x4])
y = Conv2D(num_filters, (1, 1), padding='same')(y)
return y
def attetion_block(g, x):
"""
g: Output of Parallel Encoder block
x: Output of Previous Decoder block
"""
filters = x.shape[-1]
g_conv = BatchNormalization()(g)
g_conv = Activation('relu')(g_conv)
g_conv = Conv2D(filters, (3, 3), padding='same')(g_conv)
g_pool = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(g_conv)
x_conv = BatchNormalization()(x)
x_conv = Activation('relu')(x_conv)
x_conv = Conv2D(filters, (3, 3), padding='same')(x_conv)
gc_sum = Add()([g_pool, x_conv])
gc_conv = BatchNormalization()(gc_sum)
gc_conv = Activation('relu')(gc_conv)
gc_conv = Conv2D(filters, (3, 3), padding='same')(gc_conv)
gc_mul = Multiply()([gc_conv, x])
return gc_mul
def build_model(input_size=512):
n_filters = [16, 32, 64, 128, 256]
inputs = Input((input_size, input_size, 3))
c0 = inputs
c1 = stem_block(c0, n_filters[0], strides=1)
c2 = resnet_block(c1, n_filters[1], strides=2)
c3 = resnet_block(c2, n_filters[2], strides=2)
c4 = resnet_block(c3, n_filters[3], strides=2)
b1 = aspp_block(c4, n_filters[4])
d1 = attetion_block(c3, b1)
d1 = UpSampling2D((2, 2))(d1)
d1 = Concatenate()([d1, c3])
d1 = resnet_block(d1, n_filters[3])
d2 = attetion_block(c2, d1)
d2 = UpSampling2D((2, 2))(d2)
d2 = Concatenate()([d2, c2])
d2 = resnet_block(d2, n_filters[2])
d3 = attetion_block(c1, d2)
d3 = UpSampling2D((2, 2))(d3)
d3 = Concatenate()([d3, c1])
d3 = resnet_block(d3, n_filters[1])
outputs = aspp_block(d3, n_filters[0])
outputs = Conv2D(1, (1, 1), padding='same')(outputs)
outputs = Activation('sigmoid')(outputs)
model = Model(inputs, outputs)
return model
unet = build_model()
unet.summary()
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.optimizers import Adam, Nadam, SGD
import tensorflow as tf
smooth = 1.0
def dice_coef(y_true, y_pred):
y_true_f = tf.keras.layers.Flatten()(y_true)
y_pred_f = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return (2.0 * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
def dice_loss(y_true, y_pred):
y_true_f = tf.keras.layers.Flatten()(y_true)
y_pred_f = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return 1.0 - (2.0 * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
def unet_loss(y_true, y_pred):
bce = tf.keras.losses.BinaryCrossentropy()(y_true, y_pred)
dice = dice_loss(y_true, y_pred)
loss = bce + dice
return loss
import numpy as np
trainImages = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/train_images.npy')
trainMasks = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/train_masks.npy')
valImages = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/val_images.npy')
valMasks = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/val_masks.npy')
from keras.preprocessing import image
BATCH_SIZE = 8
image_datagen_train = image.ImageDataGenerator()
mask_datagen_train = image.ImageDataGenerator()
image_datagen_train.fit(trainImages, augment=False, seed=seed)
mask_datagen_train.fit(trainMasks, augment=False, seed=seed)
x_train = image_datagen_train.flow(trainImages, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
y_train = mask_datagen_train.flow(trainMasks, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
from keras.preprocessing import image
BATCH_SIZE = 8
image_datagen_val = image.ImageDataGenerator()
mask_datagen_val = image.ImageDataGenerator()
image_datagen_val.fit(valImages, augment=False, seed=seed)
mask_datagen_val.fit(valMasks, augment=False, seed=seed)
x_val = image_datagen_val.flow(valImages, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
y_val = mask_datagen_val.flow(valMasks, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
train_generator = zip(x_train, y_train)
val_generator = zip(x_val, y_val)
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.optimizers import Adam, Nadam, SGD
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
lr = 0.0001
optimizer = Nadam(lr)
metrics = [Recall(), Precision(), dice_coef, MeanIoU(num_classes=2)]
unet.compile(loss=dice_loss, optimizer=optimizer, metrics=metrics)
checkpoint1 = ModelCheckpoint('/kaggle/working/MDLChkP_Everything/unet.h5', verbose=1, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=False)
checkpoint2 = ModelCheckpoint('/kaggle/working/MDLChkP_WeightsOnly/', verbose=1, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, min_lr=1e-06, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)
callbacks = [checkpoint1, checkpoint2, reduce_lr, early_stopping]
train_steps = len(x_train)
val_steps = len(x_val)
history = unet.fit_generator(train_generator, validation_data=val_generator, validation_steps=val_steps, steps_per_epoch=train_steps, epochs=120, callbacks=callbacks)
import numpy as np
testImages = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/test_images.npy')
testMasks = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/test_masks.npy')
from keras.preprocessing import image
BATCH_SIZE = 8
image_datagen_test = image.ImageDataGenerator()
mask_datagen_test = image.ImageDataGenerator()
image_datagen_test.fit(testImages, augment=False, seed=seed)
mask_datagen_test.fit(testMasks, augment=False, seed=seed)
x_test = image_datagen_test.flow(testImages, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
y_test = mask_datagen_test.flow(testMasks, batch_size=1, shuffle=True, seed=seed)
preds_test = unet.predict(x_test, verbose=1)
y_pred = (preds_test > 0.5).astype(np.float32)
dice_scores = []
for i in range(len(y_pred)):
k = y_test.next()
arr = np.squeeze(k, axis=0)
dice = dice_coef(arr, y_pred[i])
dice_scores.append(dice)
average_dice = np.mean(dice_scores)
import csv
my_values = [tensor.numpy() for tensor in dice_scores]
with open('dice_scores.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(my_values)
unet.save('lastunetsave.h5')
tf.saved_model.save(unet, 'unet_SMF')
import tensorflow as tf
unet = tf.keras.models.load_model('/kaggle/working/lastunetsave.h5', compile=False)
preds_test = unet.predict(x_test, verbose=1)
y_pred = (preds_test > 0.5).astype(np.float32)
dice_scores = []
for i in range(len(y_pred)):
k = y_test.next()
arr = np.squeeze(k, axis=0)
dice = dice_coef(arr, y_pred[i])
dice_scores.append(dice)
print('\n Dice score: \t \n', dice)
average_dice = np.mean(dice_scores)
print('Average dice coefficient: ', average_dice)
import csv
my_values = [tensor.numpy() for tensor in dice_scores]
with open('dice_scores2.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(my_values) | code |
129020867/cell_14 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from keras.preprocessing import image
from keras.preprocessing import image
from keras.preprocessing import image
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam, Nadam, SGD
from tensorflow.keras.optimizers import Adam, Nadam, SGD
import csv
import numpy as np
import numpy as np
import numpy as np
import numpy as np
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
import numpy as np
import tensorflow as tf
def seed_everything(SEED):
np.random.seed(SEED)
tf.random.set_seed(SEED)
seed = 42
seed_everything(seed)
'\nResUNet++ architecture in Keras TensorFlow\n'
import os
import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
def squeeze_excite_block(inputs, ratio=8):
init = inputs
channel_axis = -1
filters = init.shape[channel_axis]
se_shape = (1, 1, filters)
se = GlobalAveragePooling2D()(init)
se = Reshape(se_shape)(se)
se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
x = Multiply()([init, se])
return x
def stem_block(x, n_filter, strides):
x_init = x
x = Conv2D(n_filter, (3, 3), padding='same', strides=strides)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same')(x)
s = Conv2D(n_filter, (1, 1), padding='same', strides=strides)(x_init)
s = BatchNormalization()(s)
x = Add()([x, s])
x = squeeze_excite_block(x)
return x
def resnet_block(x, n_filter, strides=1):
x_init = x
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same', strides=strides)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same', strides=1)(x)
s = Conv2D(n_filter, (1, 1), padding='same', strides=strides)(x_init)
s = BatchNormalization()(s)
x = Add()([x, s])
x = squeeze_excite_block(x)
return x
def aspp_block(x, num_filters, rate_scale=1):
x1 = Conv2D(num_filters, (3, 3), dilation_rate=(6 * rate_scale, 6 * rate_scale), padding='same')(x)
x1 = BatchNormalization()(x1)
x2 = Conv2D(num_filters, (3, 3), dilation_rate=(12 * rate_scale, 12 * rate_scale), padding='same')(x)
x2 = BatchNormalization()(x2)
x3 = Conv2D(num_filters, (3, 3), dilation_rate=(18 * rate_scale, 18 * rate_scale), padding='same')(x)
x3 = BatchNormalization()(x3)
x4 = Conv2D(num_filters, (3, 3), padding='same')(x)
x4 = BatchNormalization()(x4)
y = Add()([x1, x2, x3, x4])
y = Conv2D(num_filters, (1, 1), padding='same')(y)
return y
def attetion_block(g, x):
"""
g: Output of Parallel Encoder block
x: Output of Previous Decoder block
"""
filters = x.shape[-1]
g_conv = BatchNormalization()(g)
g_conv = Activation('relu')(g_conv)
g_conv = Conv2D(filters, (3, 3), padding='same')(g_conv)
g_pool = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(g_conv)
x_conv = BatchNormalization()(x)
x_conv = Activation('relu')(x_conv)
x_conv = Conv2D(filters, (3, 3), padding='same')(x_conv)
gc_sum = Add()([g_pool, x_conv])
gc_conv = BatchNormalization()(gc_sum)
gc_conv = Activation('relu')(gc_conv)
gc_conv = Conv2D(filters, (3, 3), padding='same')(gc_conv)
gc_mul = Multiply()([gc_conv, x])
return gc_mul
def build_model(input_size=512):
n_filters = [16, 32, 64, 128, 256]
inputs = Input((input_size, input_size, 3))
c0 = inputs
c1 = stem_block(c0, n_filters[0], strides=1)
c2 = resnet_block(c1, n_filters[1], strides=2)
c3 = resnet_block(c2, n_filters[2], strides=2)
c4 = resnet_block(c3, n_filters[3], strides=2)
b1 = aspp_block(c4, n_filters[4])
d1 = attetion_block(c3, b1)
d1 = UpSampling2D((2, 2))(d1)
d1 = Concatenate()([d1, c3])
d1 = resnet_block(d1, n_filters[3])
d2 = attetion_block(c2, d1)
d2 = UpSampling2D((2, 2))(d2)
d2 = Concatenate()([d2, c2])
d2 = resnet_block(d2, n_filters[2])
d3 = attetion_block(c1, d2)
d3 = UpSampling2D((2, 2))(d3)
d3 = Concatenate()([d3, c1])
d3 = resnet_block(d3, n_filters[1])
outputs = aspp_block(d3, n_filters[0])
outputs = Conv2D(1, (1, 1), padding='same')(outputs)
outputs = Activation('sigmoid')(outputs)
model = Model(inputs, outputs)
return model
unet = build_model()
unet.summary()
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.optimizers import Adam, Nadam, SGD
import tensorflow as tf
smooth = 1.0
def dice_coef(y_true, y_pred):
y_true_f = tf.keras.layers.Flatten()(y_true)
y_pred_f = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return (2.0 * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
def dice_loss(y_true, y_pred):
y_true_f = tf.keras.layers.Flatten()(y_true)
y_pred_f = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return 1.0 - (2.0 * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
def unet_loss(y_true, y_pred):
bce = tf.keras.losses.BinaryCrossentropy()(y_true, y_pred)
dice = dice_loss(y_true, y_pred)
loss = bce + dice
return loss
import numpy as np
trainImages = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/train_images.npy')
trainMasks = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/train_masks.npy')
valImages = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/val_images.npy')
valMasks = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/val_masks.npy')
from keras.preprocessing import image
BATCH_SIZE = 8
image_datagen_train = image.ImageDataGenerator()
mask_datagen_train = image.ImageDataGenerator()
image_datagen_train.fit(trainImages, augment=False, seed=seed)
mask_datagen_train.fit(trainMasks, augment=False, seed=seed)
x_train = image_datagen_train.flow(trainImages, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
y_train = mask_datagen_train.flow(trainMasks, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
from keras.preprocessing import image
BATCH_SIZE = 8
image_datagen_val = image.ImageDataGenerator()
mask_datagen_val = image.ImageDataGenerator()
image_datagen_val.fit(valImages, augment=False, seed=seed)
mask_datagen_val.fit(valMasks, augment=False, seed=seed)
x_val = image_datagen_val.flow(valImages, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
y_val = mask_datagen_val.flow(valMasks, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
train_generator = zip(x_train, y_train)
val_generator = zip(x_val, y_val)
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.optimizers import Adam, Nadam, SGD
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
lr = 0.0001
optimizer = Nadam(lr)
metrics = [Recall(), Precision(), dice_coef, MeanIoU(num_classes=2)]
unet.compile(loss=dice_loss, optimizer=optimizer, metrics=metrics)
checkpoint1 = ModelCheckpoint('/kaggle/working/MDLChkP_Everything/unet.h5', verbose=1, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=False)
checkpoint2 = ModelCheckpoint('/kaggle/working/MDLChkP_WeightsOnly/', verbose=1, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, min_lr=1e-06, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)
callbacks = [checkpoint1, checkpoint2, reduce_lr, early_stopping]
train_steps = len(x_train)
val_steps = len(x_val)
history = unet.fit_generator(train_generator, validation_data=val_generator, validation_steps=val_steps, steps_per_epoch=train_steps, epochs=120, callbacks=callbacks)
import numpy as np
testImages = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/test_images.npy')
testMasks = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/test_masks.npy')
from keras.preprocessing import image
BATCH_SIZE = 8
image_datagen_test = image.ImageDataGenerator()
mask_datagen_test = image.ImageDataGenerator()
image_datagen_test.fit(testImages, augment=False, seed=seed)
mask_datagen_test.fit(testMasks, augment=False, seed=seed)
x_test = image_datagen_test.flow(testImages, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
y_test = mask_datagen_test.flow(testMasks, batch_size=1, shuffle=True, seed=seed)
preds_test = unet.predict(x_test, verbose=1)
y_pred = (preds_test > 0.5).astype(np.float32)
dice_scores = []
for i in range(len(y_pred)):
k = y_test.next()
arr = np.squeeze(k, axis=0)
dice = dice_coef(arr, y_pred[i])
dice_scores.append(dice)
print('\n Dice score: \t \n', dice)
average_dice = np.mean(dice_scores)
print('Average dice coefficient: ', average_dice)
import csv
my_values = [tensor.numpy() for tensor in dice_scores]
with open('dice_scores.csv', 'w', newline='') as f:
writer = csv.writer(f)
writer.writerow(my_values) | code |
129020867/cell_10 | [
"text_plain_output_1.png"
] | from keras.preprocessing import image
from keras.preprocessing import image
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam, Nadam, SGD
from tensorflow.keras.optimizers import Adam, Nadam, SGD
import numpy as np
import numpy as np
import numpy as np
import tensorflow as tf
import tensorflow as tf
import tensorflow as tf
import numpy as np
import tensorflow as tf
def seed_everything(SEED):
np.random.seed(SEED)
tf.random.set_seed(SEED)
seed = 42
seed_everything(seed)
'\nResUNet++ architecture in Keras TensorFlow\n'
import os
import numpy as np
import cv2
import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import Model
def squeeze_excite_block(inputs, ratio=8):
init = inputs
channel_axis = -1
filters = init.shape[channel_axis]
se_shape = (1, 1, filters)
se = GlobalAveragePooling2D()(init)
se = Reshape(se_shape)(se)
se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
x = Multiply()([init, se])
return x
def stem_block(x, n_filter, strides):
x_init = x
x = Conv2D(n_filter, (3, 3), padding='same', strides=strides)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same')(x)
s = Conv2D(n_filter, (1, 1), padding='same', strides=strides)(x_init)
s = BatchNormalization()(s)
x = Add()([x, s])
x = squeeze_excite_block(x)
return x
def resnet_block(x, n_filter, strides=1):
x_init = x
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same', strides=strides)(x)
x = BatchNormalization()(x)
x = Activation('relu')(x)
x = Conv2D(n_filter, (3, 3), padding='same', strides=1)(x)
s = Conv2D(n_filter, (1, 1), padding='same', strides=strides)(x_init)
s = BatchNormalization()(s)
x = Add()([x, s])
x = squeeze_excite_block(x)
return x
def aspp_block(x, num_filters, rate_scale=1):
x1 = Conv2D(num_filters, (3, 3), dilation_rate=(6 * rate_scale, 6 * rate_scale), padding='same')(x)
x1 = BatchNormalization()(x1)
x2 = Conv2D(num_filters, (3, 3), dilation_rate=(12 * rate_scale, 12 * rate_scale), padding='same')(x)
x2 = BatchNormalization()(x2)
x3 = Conv2D(num_filters, (3, 3), dilation_rate=(18 * rate_scale, 18 * rate_scale), padding='same')(x)
x3 = BatchNormalization()(x3)
x4 = Conv2D(num_filters, (3, 3), padding='same')(x)
x4 = BatchNormalization()(x4)
y = Add()([x1, x2, x3, x4])
y = Conv2D(num_filters, (1, 1), padding='same')(y)
return y
def attetion_block(g, x):
"""
g: Output of Parallel Encoder block
x: Output of Previous Decoder block
"""
filters = x.shape[-1]
g_conv = BatchNormalization()(g)
g_conv = Activation('relu')(g_conv)
g_conv = Conv2D(filters, (3, 3), padding='same')(g_conv)
g_pool = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(g_conv)
x_conv = BatchNormalization()(x)
x_conv = Activation('relu')(x_conv)
x_conv = Conv2D(filters, (3, 3), padding='same')(x_conv)
gc_sum = Add()([g_pool, x_conv])
gc_conv = BatchNormalization()(gc_sum)
gc_conv = Activation('relu')(gc_conv)
gc_conv = Conv2D(filters, (3, 3), padding='same')(gc_conv)
gc_mul = Multiply()([gc_conv, x])
return gc_mul
def build_model(input_size=512):
n_filters = [16, 32, 64, 128, 256]
inputs = Input((input_size, input_size, 3))
c0 = inputs
c1 = stem_block(c0, n_filters[0], strides=1)
c2 = resnet_block(c1, n_filters[1], strides=2)
c3 = resnet_block(c2, n_filters[2], strides=2)
c4 = resnet_block(c3, n_filters[3], strides=2)
b1 = aspp_block(c4, n_filters[4])
d1 = attetion_block(c3, b1)
d1 = UpSampling2D((2, 2))(d1)
d1 = Concatenate()([d1, c3])
d1 = resnet_block(d1, n_filters[3])
d2 = attetion_block(c2, d1)
d2 = UpSampling2D((2, 2))(d2)
d2 = Concatenate()([d2, c2])
d2 = resnet_block(d2, n_filters[2])
d3 = attetion_block(c1, d2)
d3 = UpSampling2D((2, 2))(d3)
d3 = Concatenate()([d3, c1])
d3 = resnet_block(d3, n_filters[1])
outputs = aspp_block(d3, n_filters[0])
outputs = Conv2D(1, (1, 1), padding='same')(outputs)
outputs = Activation('sigmoid')(outputs)
model = Model(inputs, outputs)
return model
unet = build_model()
unet.summary()
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.optimizers import Adam, Nadam, SGD
import tensorflow as tf
smooth = 1.0
def dice_coef(y_true, y_pred):
y_true_f = tf.keras.layers.Flatten()(y_true)
y_pred_f = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return (2.0 * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
def dice_loss(y_true, y_pred):
y_true_f = tf.keras.layers.Flatten()(y_true)
y_pred_f = tf.keras.layers.Flatten()(y_pred)
intersection = tf.reduce_sum(y_true_f * y_pred_f)
return 1.0 - (2.0 * intersection + smooth) / (tf.reduce_sum(y_true_f) + tf.reduce_sum(y_pred_f) + smooth)
def unet_loss(y_true, y_pred):
bce = tf.keras.losses.BinaryCrossentropy()(y_true, y_pred)
dice = dice_loss(y_true, y_pred)
loss = bce + dice
return loss
import numpy as np
trainImages = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/train_images.npy')
trainMasks = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/train_masks.npy')
valImages = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/val_images.npy')
valMasks = np.load('/kaggle/input/isib2016-allnpdata-noclahe-noaug/val_masks.npy')
from keras.preprocessing import image
BATCH_SIZE = 8
image_datagen_train = image.ImageDataGenerator()
mask_datagen_train = image.ImageDataGenerator()
image_datagen_train.fit(trainImages, augment=False, seed=seed)
mask_datagen_train.fit(trainMasks, augment=False, seed=seed)
x_train = image_datagen_train.flow(trainImages, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
y_train = mask_datagen_train.flow(trainMasks, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
from keras.preprocessing import image
BATCH_SIZE = 8
image_datagen_val = image.ImageDataGenerator()
mask_datagen_val = image.ImageDataGenerator()
image_datagen_val.fit(valImages, augment=False, seed=seed)
mask_datagen_val.fit(valMasks, augment=False, seed=seed)
x_val = image_datagen_val.flow(valImages, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
y_val = mask_datagen_val.flow(valMasks, batch_size=BATCH_SIZE, shuffle=True, seed=seed)
train_generator = zip(x_train, y_train)
val_generator = zip(x_val, y_val)
from tensorflow.keras.metrics import Precision, Recall, MeanIoU
from tensorflow.keras.optimizers import Adam, Nadam, SGD
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
lr = 0.0001
optimizer = Nadam(lr)
metrics = [Recall(), Precision(), dice_coef, MeanIoU(num_classes=2)]
unet.compile(loss=dice_loss, optimizer=optimizer, metrics=metrics)
checkpoint1 = ModelCheckpoint('/kaggle/working/MDLChkP_Everything/unet.h5', verbose=1, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=False)
checkpoint2 = ModelCheckpoint('/kaggle/working/MDLChkP_WeightsOnly/', verbose=1, monitor='val_loss', mode='min', save_best_only=True, save_weights_only=True)
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, min_lr=1e-06, verbose=1)
early_stopping = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)
callbacks = [checkpoint1, checkpoint2, reduce_lr, early_stopping]
train_steps = len(x_train)
val_steps = len(x_val)
history = unet.fit_generator(train_generator, validation_data=val_generator, validation_steps=val_steps, steps_per_epoch=train_steps, epochs=120, callbacks=callbacks) | code |
90147502/cell_21 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any()
train_df['Sentiment'].value_counts() | code |
90147502/cell_13 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape | code |
90147502/cell_9 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape | code |
90147502/cell_57 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
X_train = train['Phrase']
y_train = train['Sentiment']
tokenize = Tokenizer()
tokenize.fit_on_texts(X_train.values)
X_test = test['Phrase']
X_train = tokenize.texts_to_sequences(X_train)
X_test = tokenize.texts_to_sequences(X_test)
max_sequence_len = max([len(s.split()) for s in train['Phrase']])
X_train = pad_sequences(X_train, max_sequence_len, padding='pre')
X_test = pad_sequences(X_test, max_sequence_len, padding='pre')
import tensorflow as tf
embedding_dimension = 100
input_val = len(tokenize.word_index) + 1
model_ANN = tf.keras.Sequential([tf.keras.layers.Embedding(input_val, embedding_dimension, input_length=max_sequence_len), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(5, activation='softmax')]) | code |
90147502/cell_23 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any()
train = train_df.to_pandas()
sns.countplot(x='Sentiment', data=train) | code |
90147502/cell_6 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.head() | code |
90147502/cell_48 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
X_train = train['Phrase']
y_train = train['Sentiment']
tokenize = Tokenizer()
tokenize.fit_on_texts(X_train.values)
X_test = test['Phrase']
X_train = tokenize.texts_to_sequences(X_train)
X_test = tokenize.texts_to_sequences(X_test)
max_sequence_len = max([len(s.split()) for s in train['Phrase']])
X_train = pad_sequences(X_train, max_sequence_len, padding='pre')
X_test = pad_sequences(X_test, max_sequence_len, padding='pre')
print(X_train.shape)
print(X_test.shape) | code |
90147502/cell_11 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.info() | code |
90147502/cell_60 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
X_train = train['Phrase']
y_train = train['Sentiment']
tokenize = Tokenizer()
tokenize.fit_on_texts(X_train.values)
X_test = test['Phrase']
X_train = tokenize.texts_to_sequences(X_train)
X_test = tokenize.texts_to_sequences(X_test)
max_sequence_len = max([len(s.split()) for s in train['Phrase']])
X_train = pad_sequences(X_train, max_sequence_len, padding='pre')
X_test = pad_sequences(X_test, max_sequence_len, padding='pre')
early_stopping = EarlyStopping(min_delta=0.001, mode='max', monitor='val_acc', patience=2)
callback = [early_stopping]
import tensorflow as tf
embedding_dimension = 100
input_val = len(tokenize.word_index) + 1
model_ANN = tf.keras.Sequential([tf.keras.layers.Embedding(input_val, embedding_dimension, input_length=max_sequence_len), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(5, activation='softmax')])
model_ANN.summary()
model_ANN.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model_ANN.fit(X_train, y_train, batch_size=512, epochs=50, verbose=1, callbacks=callback) | code |
90147502/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 |
90147502/cell_7 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.info() | code |
90147502/cell_18 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull().sum()
test_df.isnull().any().any() | code |
90147502/cell_62 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.callbacks import EarlyStopping
from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
X_train = train['Phrase']
y_train = train['Sentiment']
tokenize = Tokenizer()
tokenize.fit_on_texts(X_train.values)
X_test = test['Phrase']
X_train = tokenize.texts_to_sequences(X_train)
X_test = tokenize.texts_to_sequences(X_test)
max_sequence_len = max([len(s.split()) for s in train['Phrase']])
X_train = pad_sequences(X_train, max_sequence_len, padding='pre')
X_test = pad_sequences(X_test, max_sequence_len, padding='pre')
early_stopping = EarlyStopping(min_delta=0.001, mode='max', monitor='val_acc', patience=2)
callback = [early_stopping]
import tensorflow as tf
embedding_dimension = 100
input_val = len(tokenize.word_index) + 1
model_ANN = tf.keras.Sequential([tf.keras.layers.Embedding(input_val, embedding_dimension, input_length=max_sequence_len), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(5, activation='softmax')])
model_ANN.summary()
model_ANN.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model_ANN.fit(X_train, y_train, batch_size=512, epochs=50, verbose=1, callbacks=callback)
predict_x = model_ANN.predict(X_test)
classes_x_ANN = np.argmax(predict_x, axis=1)
classes_x_ANN | code |
90147502/cell_58 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import tensorflow as tf
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
X_train = train['Phrase']
y_train = train['Sentiment']
tokenize = Tokenizer()
tokenize.fit_on_texts(X_train.values)
X_test = test['Phrase']
X_train = tokenize.texts_to_sequences(X_train)
X_test = tokenize.texts_to_sequences(X_test)
max_sequence_len = max([len(s.split()) for s in train['Phrase']])
X_train = pad_sequences(X_train, max_sequence_len, padding='pre')
X_test = pad_sequences(X_test, max_sequence_len, padding='pre')
import tensorflow as tf
embedding_dimension = 100
input_val = len(tokenize.word_index) + 1
model_ANN = tf.keras.Sequential([tf.keras.layers.Embedding(input_val, embedding_dimension, input_length=max_sequence_len), tf.keras.layers.Flatten(), tf.keras.layers.Dense(256, activation='relu'), tf.keras.layers.Dense(5, activation='softmax')])
model_ANN.summary() | code |
90147502/cell_28 | [
"text_plain_output_1.png"
] | import string
import string
import string
import re
string.punctuation | code |
90147502/cell_8 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.describe() | code |
90147502/cell_15 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum() | code |
90147502/cell_16 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull().sum() | code |
90147502/cell_47 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
X_train = train['Phrase']
y_train = train['Sentiment']
tokenize = Tokenizer()
tokenize.fit_on_texts(X_train.values)
X_test = test['Phrase']
X_train = tokenize.texts_to_sequences(X_train)
X_test = tokenize.texts_to_sequences(X_test)
max_sequence_len = max([len(s.split()) for s in train['Phrase']])
X_train = pad_sequences(X_train, max_sequence_len, padding='pre')
X_test = pad_sequences(X_test, max_sequence_len, padding='pre')
X_train | code |
90147502/cell_17 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any() | code |
90147502/cell_24 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any()
train = train_df.to_pandas()
train_df['Phrase'][0] | code |
90147502/cell_10 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.head() | code |
90147502/cell_12 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.describe() | code |
90147502/cell_36 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
stopwords.words('english') | code |
72105010/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
features = train.drop(columns=['target', 'id'], axis=1)
test = test.drop(columns=['id'])
y = train['target']
ordinal_enc = OrdinalEncoder()
categorical = list(features.select_dtypes(include=[object]))
X = features.copy()
X_test = test.copy()
X[categorical] = ordinal_enc.fit_transform(features[categorical])
X_test[categorical] = ordinal_enc.transform(test[categorical])
ordinal_enc = OrdinalEncoder()
categorical = list(features.select_dtypes(include=[object]))
X_scale = features.copy()
X_test_scale = test.copy()
X_scale[categorical] = ordinal_enc.fit_transform(features[categorical])
X_test_scale[categorical] = ordinal_enc.transform(test[categorical])
numerical = list(features.select_dtypes(exclude=[object]))
scaler = MinMaxScaler()
X_scale[numerical] = scaler.fit_transform(features[numerical])
X_test_scale[numerical] = scaler.transform(test[numerical])
X_scale | code |
72105010/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
print(train.shape)
print(test.shape) | code |
72105010/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
sns.heatmap(train.isnull()) | code |
72105010/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import OrdinalEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from xgboost import XGBRegressor
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error, r2_score | code |
72105010/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
features = train.drop(columns=['target', 'id'], axis=1)
test = test.drop(columns=['id'])
y = train['target']
ordinal_enc = OrdinalEncoder()
categorical = list(features.select_dtypes(include=[object]))
X = features.copy()
X_test = test.copy()
X[categorical] = ordinal_enc.fit_transform(features[categorical])
X_test[categorical] = ordinal_enc.transform(test[categorical])
X | code |
72105010/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 |
72105010/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
sns.pairplot(train) | code |
72105010/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
model_base = LinearRegression()
model_base.fit(X_train, y_train)
preds_valid_base = model_base.predict(X_valid)
print('MAE', mean_squared_error(y_valid, preds_valid_base, squared=False))
print('r2', r2_score(y_valid, preds_valid_base)) | code |
72105010/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv')
train.info() | code |
33110459/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py
!python pytorch-xla-env-setup.py --apt-packages libomp5 libopenblas-dev | code |
33110459/cell_7 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'train'
train_df = train_df.append(tmp_df, ignore_index=True)
val_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'val'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'val', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'val'
val_df = val_df.append(tmp_df, ignore_index=True)
CLASSES = ['pink primrose', 'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea', 'wild geranium', 'tiger lily', 'moon orchid', 'bird of paradise', 'monkshood', 'globe thistle', 'snapdragon', "colt's foot", 'king protea', 'spear thistle', 'yellow iris', 'globe-flower', 'purple coneflower', 'peruvian lily', 'balloon flower', 'giant white arum lily', 'fire lily', 'pincushion flower', 'fritillary', 'red ginger', 'grape hyacinth', 'corn poppy', 'prince of wales feathers', 'stemless gentian', 'artichoke', 'sweet william', 'carnation', 'garden phlox', 'love in the mist', 'cosmos', 'alpine sea holly', 'ruby-lipped cattleya', 'cape flower', 'great masterwort', 'siam tulip', 'lenten rose', 'barberton daisy', 'daffodil', 'sword lily', 'poinsettia', 'bolero deep blue', 'wallflower', 'marigold', 'buttercup', 'daisy', 'common dandelion', 'petunia', 'wild pansy', 'primula', 'sunflower', 'lilac hibiscus', 'bishop of llandaff', 'gaura', 'geranium', 'orange dahlia', 'pink-yellow dahlia', 'cautleya spicata', 'japanese anemone', 'black-eyed susan', 'silverbush', 'californian poppy', 'osteospermum', 'spring crocus', 'iris', 'windflower', 'tree poppy', 'gazania', 'azalea', 'water lily', 'rose', 'thorn apple', 'morning glory', 'passion flower', 'lotus', 'toad lily', 'anthurium', 'frangipani', 'clematis', 'hibiscus', 'columbine', 'desert-rose', 'tree mallow', 'magnolia', 'cyclamen ', 'watercress', 'canna lily', 'hippeastrum ', 'bee balm', 'pink quill', 'foxglove', 'bougainvillea', 'camellia', 'mallow', 'mexican petunia', 'bromelia', 'blanket flower', 'trumpet creeper', 'blackberry lily', 'common tulip', 'wild rose']
print('num class:', len(CLASSES))
train_df['label'] = train_df['class'].apply(lambda x: CLASSES.index(x))
val_df['label'] = val_df['class'].apply(lambda x: CLASSES.index(x)) | code |
33110459/cell_18 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as pl
import torchvision.models as models
import torchvision.transforms as T
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'train'
train_df = train_df.append(tmp_df, ignore_index=True)
val_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'val'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'val', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'val'
val_df = val_df.append(tmp_df, ignore_index=True)
test_df = pd.DataFrame()
f = 'jpeg-224x224'
images = os.listdir(os.path.join(root, f, 'test'))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = 'unknown'
tmp_df['folder'] = f
tmp_df['type'] = 'test'
test_df = test_df.append(tmp_df, ignore_index=True)
class flowerDataset(Dataset):
def __init__(self, df, root='../input/104-flowers-garden-of-eden'):
self.df = df
self.root = root
self.transforms = T.Compose([T.Resize((224, 224)), T.ToTensor()])
def __getitem__(self, idx):
img_path = os.path.join(self.root, self.df.iloc[idx]['folder'], self.df.iloc[idx]['type'], self.df.iloc[idx]['class'], self.df.iloc[idx]['image_name'])
img = Image.open(img_path)
img_tensor = self.transforms(img)
target_tensor = torch.tensor(self.df.iloc[idx]['label'], dtype=torch.long)
return (img_tensor, target_tensor)
def __len__(self):
return len(self.df)
class testDataset(Dataset):
def __init__(self, df, root='../input/104-flowers-garden-of-eden'):
self.df = df
self.root = root
self.transforms = T.Compose([T.ToTensor()])
def __getitem__(self, idx):
img_path = os.path.join(self.root, self.df.iloc[idx]['folder'], self.df.iloc[idx]['type'], self.df.iloc[idx]['image_name'])
img = Image.open(img_path)
img_tensor = self.transforms(img)
return (img_tensor, self.df.iloc[idx]['image_name'][:-5])
def __len__(self):
return len(self.df)
train_dataset = flowerDataset(train_df)
print(train_dataset.__len__())
train_loader = DataLoader(train_dataset, batch_size = 32, shuffle = True, drop_last = True)
train_iter = iter(train_loader)
images, labels = next(train_iter)
print(images.size())
print(labels.size())
plot_size = 32
fig = plt.figure(figsize=(25, 10))
for idx in np.arange(plot_size):
ax = fig.add_subplot(4, plot_size/4, idx+1, xticks=[], yticks=[])
ax.imshow(np.transpose(images[idx], (1, 2, 0)))
ax.set_title(classes[labels[idx].item()])
def train_net():
torch.manual_seed(FLAGS['seed'])
device = xm.xla_device()
world_size = xm.xrt_world_size()
train_dataset = flowerDataset(train_df)
train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=xm.get_ordinal(), shuffle=True)
train_loader = DataLoader(train_dataset, batch_size=FLAGS['batch_size'], sampler=train_sampler, num_workers=FLAGS['num_workers'], drop_last=True)
val_dataset = flowerDataset(val_df)
val_sampler = DistributedSampler(val_dataset, num_replicas=world_size, rank=xm.get_ordinal(), shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=FLAGS['batch_size'], sampler=val_sampler, num_workers=FLAGS['num_workers'], drop_last=True)
model = models.resnet18()
model.load_state_dict(torch.load('/kaggle/input/resnet18/resnet18.pth'))
model.fc = nn.Linear(512, 104)
model.to(device)
optimizer = optim.SGD(model.parameters(), lr=FLAGS['learning_rate'] * world_size, momentum=FLAGS['momentum'], weight_decay=0.0005)
loss_fn = torch.nn.CrossEntropyLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
loss_window = deque(maxlen=FLAGS['log_steps'])
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss_window.append(loss.item())
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS['batch_size'])
def val_loop_fn(loader):
total_samples, correct = (0, 0)
model.eval()
for data, target in loader:
with torch.no_grad():
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
return accuracy
for epoch in range(1, FLAGS['num_epochs'] + 1):
para_loader = pl.ParallelLoader(train_loader, [device])
train_loop_fn(para_loader.per_device_loader(device))
para_loader = pl.ParallelLoader(val_loader, [device])
accuracy = val_loop_fn(para_loader.per_device_loader(device))
best_accuracy = 0.0
if accuracy > best_accuracy:
xm.save(model.state_dict(), 'trained_resnet18_model.pth')
best_accuracy = accuracy
def _mp_fn(rank, flags):
global FLAGS
FLAGS = flags
torch.set_default_tensor_type('torch.FloatTensor')
train_start = time.time()
train_net()
elapsed_train_time = time.time() - train_start
model = models.resnet18()
model.fc = nn.Linear(512, 104)
model.load_state_dict(torch.load('trained_resnet18_model.pth'))
device = xm.xla_device()
model.to(device)
model.eval()
batch_size = 128
test_dataset = testDataset(test_df)
test_loader = DataLoader(test_dataset, batch_size=batch_size)
n = test_dataset.__len__()
label = []
id = []
for x, (images, names) in enumerate(test_loader):
images = images.to(device)
with torch.no_grad():
output = model(images)
preds = list(output.max(1)[1].cpu().numpy())
label.extend(preds)
id.extend(names)
print('\rProcess {} %'.format(round(100 * x * batch_size / n)), end='')
print('\rProcess 100 %')
predictions = pd.DataFrame(data={'id': id, 'label': label}) | code |
33110459/cell_8 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'train'
train_df = train_df.append(tmp_df, ignore_index=True)
val_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'val'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'val', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'val'
val_df = val_df.append(tmp_df, ignore_index=True)
test_df = pd.DataFrame()
f = 'jpeg-224x224'
images = os.listdir(os.path.join(root, f, 'test'))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = 'unknown'
tmp_df['folder'] = f
tmp_df['type'] = 'test'
test_df = test_df.append(tmp_df, ignore_index=True)
print('test:', test_df.shape) | code |
33110459/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as pl
import torchvision.models as models
import torchvision.transforms as T
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'train'
train_df = train_df.append(tmp_df, ignore_index=True)
val_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'val'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'val', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'val'
val_df = val_df.append(tmp_df, ignore_index=True)
test_df = pd.DataFrame()
f = 'jpeg-224x224'
images = os.listdir(os.path.join(root, f, 'test'))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = 'unknown'
tmp_df['folder'] = f
tmp_df['type'] = 'test'
test_df = test_df.append(tmp_df, ignore_index=True)
class flowerDataset(Dataset):
def __init__(self, df, root='../input/104-flowers-garden-of-eden'):
self.df = df
self.root = root
self.transforms = T.Compose([T.Resize((224, 224)), T.ToTensor()])
def __getitem__(self, idx):
img_path = os.path.join(self.root, self.df.iloc[idx]['folder'], self.df.iloc[idx]['type'], self.df.iloc[idx]['class'], self.df.iloc[idx]['image_name'])
img = Image.open(img_path)
img_tensor = self.transforms(img)
target_tensor = torch.tensor(self.df.iloc[idx]['label'], dtype=torch.long)
return (img_tensor, target_tensor)
def __len__(self):
return len(self.df)
class testDataset(Dataset):
def __init__(self, df, root='../input/104-flowers-garden-of-eden'):
self.df = df
self.root = root
self.transforms = T.Compose([T.ToTensor()])
def __getitem__(self, idx):
img_path = os.path.join(self.root, self.df.iloc[idx]['folder'], self.df.iloc[idx]['type'], self.df.iloc[idx]['image_name'])
img = Image.open(img_path)
img_tensor = self.transforms(img)
return (img_tensor, self.df.iloc[idx]['image_name'][:-5])
def __len__(self):
return len(self.df)
train_dataset = flowerDataset(train_df)
print(train_dataset.__len__())
train_loader = DataLoader(train_dataset, batch_size = 32, shuffle = True, drop_last = True)
train_iter = iter(train_loader)
images, labels = next(train_iter)
print(images.size())
print(labels.size())
plot_size = 32
fig = plt.figure(figsize=(25, 10))
for idx in np.arange(plot_size):
ax = fig.add_subplot(4, plot_size/4, idx+1, xticks=[], yticks=[])
ax.imshow(np.transpose(images[idx], (1, 2, 0)))
ax.set_title(classes[labels[idx].item()])
def train_net():
torch.manual_seed(FLAGS['seed'])
device = xm.xla_device()
world_size = xm.xrt_world_size()
train_dataset = flowerDataset(train_df)
train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=xm.get_ordinal(), shuffle=True)
train_loader = DataLoader(train_dataset, batch_size=FLAGS['batch_size'], sampler=train_sampler, num_workers=FLAGS['num_workers'], drop_last=True)
val_dataset = flowerDataset(val_df)
val_sampler = DistributedSampler(val_dataset, num_replicas=world_size, rank=xm.get_ordinal(), shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=FLAGS['batch_size'], sampler=val_sampler, num_workers=FLAGS['num_workers'], drop_last=True)
model = models.resnet18()
model.load_state_dict(torch.load('/kaggle/input/resnet18/resnet18.pth'))
model.fc = nn.Linear(512, 104)
model.to(device)
optimizer = optim.SGD(model.parameters(), lr=FLAGS['learning_rate'] * world_size, momentum=FLAGS['momentum'], weight_decay=0.0005)
loss_fn = torch.nn.CrossEntropyLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
loss_window = deque(maxlen=FLAGS['log_steps'])
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss_window.append(loss.item())
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS['batch_size'])
def val_loop_fn(loader):
total_samples, correct = (0, 0)
model.eval()
for data, target in loader:
with torch.no_grad():
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
return accuracy
for epoch in range(1, FLAGS['num_epochs'] + 1):
para_loader = pl.ParallelLoader(train_loader, [device])
train_loop_fn(para_loader.per_device_loader(device))
para_loader = pl.ParallelLoader(val_loader, [device])
accuracy = val_loop_fn(para_loader.per_device_loader(device))
best_accuracy = 0.0
if accuracy > best_accuracy:
xm.save(model.state_dict(), 'trained_resnet18_model.pth')
best_accuracy = accuracy
def _mp_fn(rank, flags):
global FLAGS
FLAGS = flags
torch.set_default_tensor_type('torch.FloatTensor')
train_start = time.time()
train_net()
elapsed_train_time = time.time() - train_start
model = models.resnet18()
model.fc = nn.Linear(512, 104)
model.load_state_dict(torch.load('trained_resnet18_model.pth'))
device = xm.xla_device()
model.to(device)
model.eval()
print(device) | code |
33110459/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
from collections import deque
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.distributed import DistributedSampler
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import torch
import torch.nn as nn
import torch.optim as optim
import torch_xla.core.xla_model as xm
import torch_xla.distributed.parallel_loader as pl
import torch_xla.distributed.xla_multiprocessing as xmp
import torchvision.models as models
import torchvision.transforms as T
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'train'
train_df = train_df.append(tmp_df, ignore_index=True)
val_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'val'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'val', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'val'
val_df = val_df.append(tmp_df, ignore_index=True)
test_df = pd.DataFrame()
f = 'jpeg-224x224'
images = os.listdir(os.path.join(root, f, 'test'))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = 'unknown'
tmp_df['folder'] = f
tmp_df['type'] = 'test'
test_df = test_df.append(tmp_df, ignore_index=True)
class flowerDataset(Dataset):
def __init__(self, df, root='../input/104-flowers-garden-of-eden'):
self.df = df
self.root = root
self.transforms = T.Compose([T.Resize((224, 224)), T.ToTensor()])
def __getitem__(self, idx):
img_path = os.path.join(self.root, self.df.iloc[idx]['folder'], self.df.iloc[idx]['type'], self.df.iloc[idx]['class'], self.df.iloc[idx]['image_name'])
img = Image.open(img_path)
img_tensor = self.transforms(img)
target_tensor = torch.tensor(self.df.iloc[idx]['label'], dtype=torch.long)
return (img_tensor, target_tensor)
def __len__(self):
return len(self.df)
class testDataset(Dataset):
def __init__(self, df, root='../input/104-flowers-garden-of-eden'):
self.df = df
self.root = root
self.transforms = T.Compose([T.ToTensor()])
def __getitem__(self, idx):
img_path = os.path.join(self.root, self.df.iloc[idx]['folder'], self.df.iloc[idx]['type'], self.df.iloc[idx]['image_name'])
img = Image.open(img_path)
img_tensor = self.transforms(img)
return (img_tensor, self.df.iloc[idx]['image_name'][:-5])
def __len__(self):
return len(self.df)
train_dataset = flowerDataset(train_df)
print(train_dataset.__len__())
train_loader = DataLoader(train_dataset, batch_size = 32, shuffle = True, drop_last = True)
train_iter = iter(train_loader)
images, labels = next(train_iter)
print(images.size())
print(labels.size())
plot_size = 32
fig = plt.figure(figsize=(25, 10))
for idx in np.arange(plot_size):
ax = fig.add_subplot(4, plot_size/4, idx+1, xticks=[], yticks=[])
ax.imshow(np.transpose(images[idx], (1, 2, 0)))
ax.set_title(classes[labels[idx].item()])
def train_net():
torch.manual_seed(FLAGS['seed'])
device = xm.xla_device()
world_size = xm.xrt_world_size()
train_dataset = flowerDataset(train_df)
train_sampler = DistributedSampler(train_dataset, num_replicas=world_size, rank=xm.get_ordinal(), shuffle=True)
train_loader = DataLoader(train_dataset, batch_size=FLAGS['batch_size'], sampler=train_sampler, num_workers=FLAGS['num_workers'], drop_last=True)
val_dataset = flowerDataset(val_df)
val_sampler = DistributedSampler(val_dataset, num_replicas=world_size, rank=xm.get_ordinal(), shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=FLAGS['batch_size'], sampler=val_sampler, num_workers=FLAGS['num_workers'], drop_last=True)
model = models.resnet18()
model.load_state_dict(torch.load('/kaggle/input/resnet18/resnet18.pth'))
model.fc = nn.Linear(512, 104)
model.to(device)
optimizer = optim.SGD(model.parameters(), lr=FLAGS['learning_rate'] * world_size, momentum=FLAGS['momentum'], weight_decay=0.0005)
loss_fn = torch.nn.CrossEntropyLoss()
def train_loop_fn(loader):
tracker = xm.RateTracker()
model.train()
loss_window = deque(maxlen=FLAGS['log_steps'])
for x, (data, target) in enumerate(loader):
optimizer.zero_grad()
output = model(data)
loss = loss_fn(output, target)
loss_window.append(loss.item())
loss.backward()
xm.optimizer_step(optimizer)
tracker.add(FLAGS['batch_size'])
def val_loop_fn(loader):
total_samples, correct = (0, 0)
model.eval()
for data, target in loader:
with torch.no_grad():
output = model(data)
pred = output.max(1, keepdim=True)[1]
correct += pred.eq(target.view_as(pred)).sum().item()
total_samples += data.size()[0]
accuracy = 100.0 * correct / total_samples
return accuracy
for epoch in range(1, FLAGS['num_epochs'] + 1):
para_loader = pl.ParallelLoader(train_loader, [device])
train_loop_fn(para_loader.per_device_loader(device))
para_loader = pl.ParallelLoader(val_loader, [device])
accuracy = val_loop_fn(para_loader.per_device_loader(device))
best_accuracy = 0.0
if accuracy > best_accuracy:
xm.save(model.state_dict(), 'trained_resnet18_model.pth')
best_accuracy = accuracy
def _mp_fn(rank, flags):
global FLAGS
FLAGS = flags
torch.set_default_tensor_type('torch.FloatTensor')
train_start = time.time()
train_net()
elapsed_train_time = time.time() - train_start
FLAGS = {}
FLAGS['seed'] = 1
FLAGS['num_workers'] = 4
FLAGS['num_cores'] = 8
FLAGS['num_epochs'] = 10
FLAGS['log_steps'] = 50
FLAGS['batch_size'] = 16
FLAGS['learning_rate'] = 0.0001
FLAGS['momentum'] = 0.9
xmp.spawn(_mp_fn, args=(FLAGS,), nprocs=FLAGS['num_cores'], start_method='fork') | code |
33110459/cell_10 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'train'
train_df = train_df.append(tmp_df, ignore_index=True)
val_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'val'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'val', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'val'
val_df = val_df.append(tmp_df, ignore_index=True)
test_df = pd.DataFrame()
f = 'jpeg-224x224'
images = os.listdir(os.path.join(root, f, 'test'))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = 'unknown'
tmp_df['folder'] = f
tmp_df['type'] = 'test'
test_df = test_df.append(tmp_df, ignore_index=True)
train_dataset = flowerDataset(train_df)
print(train_dataset.__len__())
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, drop_last=True)
train_iter = iter(train_loader)
images, labels = next(train_iter)
print(images.size())
print(labels.size())
plot_size = 32
fig = plt.figure(figsize=(25, 10))
for idx in np.arange(plot_size):
ax = fig.add_subplot(4, plot_size / 4, idx + 1, xticks=[], yticks=[])
ax.imshow(np.transpose(images[idx], (1, 2, 0)))
ax.set_title(classes[labels[idx].item()]) | code |
33110459/cell_5 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
root = '../input/104-flowers-garden-of-eden'
train_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'train'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'train', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'train'
train_df = train_df.append(tmp_df, ignore_index=True)
print('train:', train_df.shape)
val_df = pd.DataFrame()
folder = os.listdir(root)
for f in folder:
classes = os.listdir(os.path.join(root, f, 'val'))
for c in classes:
images = os.listdir(os.path.join(root, f, 'val', c))
tmp_df = pd.DataFrame(images, columns=['image_name'])
tmp_df['class'] = c
tmp_df['folder'] = f
tmp_df['type'] = 'val'
val_df = val_df.append(tmp_df, ignore_index=True)
print('val:', val_df.shape) | code |
105188182/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df.plot(kind='box', subplots=True, figsize=(18, 15), layout=(5, 5))
plt.show() | code |
105188182/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
plt.figure(figsize=(15, 8))
sns.scatterplot(x='Arrival Delay', y='Satisfaction', data=df) | code |
105188182/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns | code |
105188182/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
plt.figure(figsize=(15, 8))
sns.scatterplot(x='Departure Delay', y='Satisfaction', data=df) | code |
105188182/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df = df.drop(df.loc[df['Arrival Delay'] > 700].index)
df.Satisfaction.value_counts()
df.Gender.value_counts() | code |
105188182/cell_20 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
plt.figure(figsize=(15, 8))
sns.scatterplot(x='Departure Delay', y='Satisfaction', data=df) | code |
105188182/cell_40 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df = df.drop(df.loc[df['Arrival Delay'] > 700].index)
df.Satisfaction.value_counts()
df.Gender.value_counts()
df.info() | code |
105188182/cell_39 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df = df.drop(df.loc[df['Arrival Delay'] > 700].index)
df.Satisfaction.value_counts()
df.Gender.value_counts()
df['Class'].value_counts() | code |
105188182/cell_41 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df = df.drop(df.loc[df['Arrival Delay'] > 700].index)
df.Satisfaction.value_counts()
df.Gender.value_counts()
df.plot(kind='box', subplots=True, figsize=(18, 15), layout=(5, 5))
plt.show() | code |
105188182/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
plt.figure(figsize=(20, 10))
sns.heatmap(df.isnull()) | code |
105188182/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum() | code |
105188182/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
plt.figure(figsize=(15, 8))
sns.scatterplot(x='Flight Distance', y='Satisfaction', data=df) | code |
105188182/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
plt.figure(figsize=(20, 10))
sns.heatmap(df.isnull()) | code |
105188182/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
plt.figure(figsize=(15, 8))
sns.scatterplot(x='Flight Distance', y='Satisfaction', data=df) | code |
105188182/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum() | code |
105188182/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df = df.drop(df.loc[df['Arrival Delay'] > 700].index)
df.Satisfaction.value_counts()
df.Gender.value_counts()
df['Customer Type'].value_counts() | code |
105188182/cell_31 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df = df.drop(df.loc[df['Arrival Delay'] > 700].index)
df.Satisfaction.value_counts() | code |
105188182/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df[df['Departure Delay'] > 600].shape | code |
105188182/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum() | code |
105188182/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df = df.drop(df.loc[df['Arrival Delay'] > 700].index)
plt.figure(figsize=(15, 8))
sns.scatterplot(x='Arrival Delay', y='Satisfaction', data=df) | code |
105188182/cell_37 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape
df.isnull().sum()
df.isnull().sum()
df = df.drop(df.loc[df['Flight Distance'] > 4200].index)
df.isnull().sum()
df = df.drop(df.loc[df['Departure Delay'] > 800].index)
df = df.drop(df.loc[df['Arrival Delay'] > 700].index)
df.Satisfaction.value_counts()
df.Gender.value_counts()
df['Type of Travel'].value_counts() | code |
105188182/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/airline-passenger-satisfaction/airline_passenger_satisfaction.csv')
df.columns
df.shape | code |
74048227/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
pd.set_option('display.max_rows', None)
train.describe().T
train2 = train.dropna(axis='rows')
train3 = train.dropna(axis='columns')
training_missing_val_count_by_column = train.isnull().sum()
plt.bar(np.arange(0, len(training_missing_val_count_by_column), 1), training_missing_val_count_by_column)
plt.xlabel('column')
plt.ylabel('Number of missing values')
plt.show()
print(training_missing_val_count_by_column.describe())
print('\n', time.time()) | code |
74048227/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
train.head() | code |
74048227/cell_20 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
pd.set_option('display.max_rows', None)
train.describe().T
train2 = train.dropna(axis='rows')
train3 = train.dropna(axis='columns')
training_missing_val_count_by_column = train.isnull().sum()
imputer = SimpleImputer(strategy='mean')
train_imputed = pd.DataFrame(imputer.fit_transform(train))
train_imputed.columns = train.columns
train = train_imputed
corr = train.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
corr_matrix = train.corr().abs()
high_corr = np.where(corr_matrix > 0.02)
high_corr = [(corr_matrix.columns[x], corr_matrix.columns[y]) for x, y in zip(*high_corr) if x != y and x < y]
print('high correlation', high_corr)
featuresofinterest = ['f6', 'f15', 'f32', 'f34', 'f36', 'f45', 'f46', 'f51', 'f57', 'f86', 'f90', 'f97', 'f111']
print('\n', time.time()) | code |
74048227/cell_19 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
pd.set_option('display.max_rows', None)
train.describe().T
train2 = train.dropna(axis='rows')
train3 = train.dropna(axis='columns')
training_missing_val_count_by_column = train.isnull().sum()
imputer = SimpleImputer(strategy='mean')
train_imputed = pd.DataFrame(imputer.fit_transform(train))
train_imputed.columns = train.columns
train = train_imputed
corr = train.corr()
mask = np.triu(np.ones_like(corr, dtype=bool))
plt.figure(figsize=(15, 15))
plt.title('Correlation matrix for Train data')
sns.heatmap(corr, mask=mask, annot=False, linewidths=0.5, square=True, cbar_kws={'shrink': 0.6})
plt.show()
print('\n', time.time()) | code |
74048227/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
print('How much data was imported?')
print('training data shape ;', train.shape)
print('test data shape ;', test.shape)
print('\nHow much data is missing?')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
print('\nmissing training data :\n', training_missing_val_count_by_column)
print('\nmissing test data :\n', test_missing_val_count_by_column)
print('\noverview complete : ', time.time()) | code |
74048227/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import time
train = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
test = pd.read_csv('../input/tabular-playground-series-sep-2021/test.csv', index_col='id')
train_df = pd.read_csv('../input/tabular-playground-series-sep-2021/train.csv', index_col='id')
training_missing_val_count_by_column = train.isnull().values.sum()
test_missing_val_count_by_column = test.isnull().values.sum()
print('train info ;\n', train.info())
print('test info ;\n', test.info())
print(time.time()) | code |
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