from keras.models import Sequential
from keras.layers import Input, Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation
from keras.datasets import mnist

def buildModel(inputShape, classCnt):
    model = Sequential()
    model.add(Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=inputShape))
    model.add(Conv2D(filters=32, kernel_size=3, activation='relu'))
    model.add(Conv2D(filters=64, kernel_size=3, activation='relu'))
    model.add(MaxPooling2D(pool_size=4))
    model.add(Flatten())
    model.add(Dense(256, activation='relu'))
    model.add(Dropout(0.5))

    model.add(Dense(classCnt, activation='softmax'))

    model.compile(optimizer='adagrad', loss='categorical_crossentropy', metrics=['accuracy'])