import os
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import joblib
from sklearn.externals import joblib
import sklearn.external.joblib as extjoblib
import seaborn as sns
sns.set(color_codes=True)
import matplotlib.pyplot as plt
%matplotlib inline
from numpy.random import seed
from tensorflow import set_random_seed ### tf.set_random_seed(seed) -> tf.random.set_seed(seed)
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from keras.layers import Input, Dropout, Dense, LSTM, TimeDistributed, RepeatVector
from keras.models import Model
from keras import regularizers
import os import pandas as pd import numpy as np from sklearn.preprocessing import MinMaxScaler import joblib
from sklearn.externals import joblib
import sklearn.external.joblib as extjoblib
import seaborn as sns sns.set(color_codes=True) import matplotlib.pyplot as plt %matplotlib inline from numpy.random import seed
from tensorflow import set_random_seed ### tf.set_random_seed(seed) -> tf.random.set_seed(seed)
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR) from keras.layers import Input, Dropout, Dense, LSTM, TimeDistributed, RepeatVector from keras.models import Model from keras import regularizers