import numpy
import pandas
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from keras.constraints import maxnorm
from keras.optimizers import SGD
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load dataset
filename='train_numeric'
dataframe = pandas.read_csv("/tmp/codegit/AWS2/train_categorical.csv", header=None,usecols=[1], engine='python')
dataset = dataframe.values
print('Loaded data file {0} with {1} rows and {2} columns').format(filename, len(dataset), len(dataset[0]))
#row0
print(dataset[0])
#colum1
print(dataset[:,1])
plt.plot(dataset)
plt.show()