for fashion mnist blog

model.py

model = Sequential()
# Tier one 
model.add(Conv2D(32, kernel_size=5, input_shape = (28, 28, 1), activation='relu', padding = 'Same' ))
model.add(Conv2D(64, kernel_size=5, activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size = (2, 2)))
model.add(Dropout(0.33))

model.add(Conv2D(128, kernel_size=3, activation='relu'))
model.add(Conv2D(256, kernel_size=3, activation = 'relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size= (2,2)))
model.add(Dropout(0.2))

model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.33))

model.add(Dense(512, activation='relu'))
model.add(Dropout(0.33))

model.add(Dense(units = 10, activation ='softmax'))

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