Jupyter import
ML project imports
import os, sys, glob, time, datetime
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from tqdm.notebook import tqdm
import IPython #IPython.display.clear_output()
import tensorflow as tf
from sklearn.model_selection import train_test_split
from scipy.ndimage.filters import gaussian_filter
from cycler import cycler
colors = ['#0c6575', '#bbcbcb', '#23a98c', '#fc7a70','#a07060',
'#003847', '#FFF7D6', '#5CA4B5', '#eeeeee']
plt.rcParams['axes.prop_cycle'] = cycler(color = colors)
Simple imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
Colab
try:
from google.colab import drive
drive.mount('/content/gdrive', force_remount=True)
root_path = 'gdrive/My Drive/Colab Notebooks/WORKING_FOLDER_NAME/'
print('Working on google colab')
except:
root_path = '../'
print('Working locally')
# to dowload files
from google.colab import files
files.download('my_file.txt')
On MacOS
%config InlineBackend.figure_format = 'retina'
Keras reproducibility
Use this to ensure seproducibility when using keras: notice the restriction to a single core
import os, sys
os.environ['PYTHONHASHSEED']=str(0)
import random
random.seed(0)
import numpy as np
np.random.seed(0)
import tensorflow as tf
tf.set_random_seed(0)
from keras import backend as K
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)