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Created 4 years ago
TODO
- ✅ add unblacend class
- ⬜ batch norm
- ⬜ regularizer
- ✅ train/dev/test
- ⬜ lr tuner
- ✅ add normalizing
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import matplotlib.pyplot as plt
import sklearn as sk
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory
# import os
# for dirname, _, filenames in os.walk('/kaggle/input'):
# for filename in filenames:
# print(os.path.join(dirname, filename))
# Any results you write to the current directory are saved as output.
%matplotlib inline
import seaborn as sns
import warnings
warnings.simplefilter('ignore')
sns.set(rc={'figure.figsize' : (10, 5)})
sns.set_style("darkgrid", {'axes.grid' : True})
Reading csv files and showing first and last 5 records.