train.py

import numpy as np
from numpy import genfromtxt
x_train = genfromtxt('train_data.csv', delimiter=',')
y_train = genfromtxt('train_labels.csv', delimiter=',')
x_test = genfromtxt('test_data.csv', delimiter=',')
y_test = genfromtxt('test_labels.csv', delimiter=',')
print('\nShape train CSV:',x_train.shape)

#converting to one hot
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)


#reshaping to 2D 
x_train=np.reshape(x_train,(x_train.shape[0], 40,5))
x_test=np.reshape(x_test,(x_test.shape[0], 40,5))
print('\nShape train2D:',x_train.shape)

#reshaping to shape required by CNN
x_train=np.reshape(x_train,(x_train.shape[0], 40,5,1))
x_test=np.reshape(x_test,(x_test.shape[0], 40,5,1))
print('\nShape train_CNN:',x_train.shape)

from keras import Sequential
from keras.layers import Dense,Conv2D,MaxPooling2D,Flatten,Dropout

#forming model
model=Sequential()

#adding layers and forming the model
model.add(Conv2D(64,kernel_size=5,strides=1,padding="Same",activation="relu",input_shape=(40,5,1)))
model.add(MaxPooling2D(padding="same"))
model.add(Conv2D(128,kernel_size=5,strides=1,padding="same",activation="relu"))
model.add(MaxPooling2D(padding="same"))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(256,activation="relu"))
model.add(Dropout(0.3))
model.add(Dense(512,activation="relu"))
model.add(Dropout(0.3))
model.add(Dense(10,activation="softmax"))



#compiling
model.compile(optimizer="adam",loss="categorical_crossentropy",metrics=["accuracy"])


#training the model
model.fit(x_train,y_train,batch_size=50,epochs=40,validation_data=(x_test,y_test))
from keras.models import load_model
model.save('model.h5')
print('\n\n Model Saved. \n\n')
train_loss_score=model.evaluate(x_train,y_train)
test_loss_score=model.evaluate(x_test,y_test)
print(train_loss_score)
print(test_loss_score)