Kaggle competition: Digit recognition
import os
import time
import numpy as np
import pandas as pd
from tqdm import tqdm
import math
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.model_selection import GridSearchCV, StratifiedKFold
from sklearn.metrics import f1_score, roc_auc_score
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.layers import Dense, Input, CuDNNLSTM, Embedding, Dropout, Activation, CuDNNGRU, Conv1D
from keras.layers import Bidirectional, GlobalMaxPool1D, GlobalMaxPooling1D, GlobalAveragePooling1D
from keras.layers import Input, Embedding, Dense, Conv2D, MaxPool2D, concatenate
from keras.layers import Reshape, Flatten, Concatenate, Dropout, SpatialDropout1D
from keras.optimizers import Adam
from keras.models import Model
from keras import backend as K
from keras.engine.topology import Layer
from keras import initializers, regularizers, constraints, optimizers, layers
from keras.layers import concatenate
from keras.callbacks import *<load_from_csv>
X_train = np.load(input_dir + 'X_train.npy')
 X_test = np.load(input_dir + 'X_test.npy')
 y_train = np.load(input_dir + 'y_train.npy')
 print("X_train shape :",X_train.shape)
 print("X_test shape :",X_test.shape)
 print("y_train shape :",y_train.shape )
<statistical_test><EOS> 
pred = pd.DataFrame()
pred['Id'] = np.arange(X_test.shape[0])
 pred['Prediction'] = np.zeros(X_test.shape[0],dtype = np.int)
 pred.to_csv('pred.csv',index=False)
 pred.head()