model = Sequential()

#1st Conv layer
model.add(Conv2D(filters = 32, kernel_size=(4,4), input_shape = (512,512,3), strides=2, activation=LeakyReLU(), padding='same'))

model.add(Conv2D(filters = 32, kernel_size=(4,4), strides=1, activation=LeakyReLU(), padding='same'))#input_shape = (256,256,32),
model.add(MaxPooling2D(pool_size=(3,3),strides=2))

#2nd Conv layer
model.add(Conv2D(filters=64, kernel_size=(4,4), strides=2, activation=LeakyReLU(), padding='same'))#input_shape = (127,127,32),

model.add(Conv2D(filters = 64, kernel_size=(4,4),  strides=1, activation=LeakyReLU(), padding='same'))#input_shape = (62,62,64),

model.add(MaxPooling2D(pool_size=(3,3),strides=2,padding='valid'))

#3rd Conv layer
model.add(Conv2D(filters=128, kernel_size=(4,4), strides=1, activation=LeakyReLU(), padding='same'))#input_shape = (31,31,64),

model.add(Conv2D(filters = 128, kernel_size=(4,4),  strides=1, activation=LeakyReLU(), padding='same'))#input_shape = (32,32,128),

model.add(MaxPooling2D(pool_size=(3,3),strides=2,padding='valid'))

#4th Conv layer
model.add(Conv2D(filters=256, kernel_size=(4,4), strides=1, activation=LeakyReLU(), padding='same'))#input_shape = (16,16,128),

model.add(MaxPooling2D(pool_size=(3,3),strides=2,padding='valid'))

#5th Conv layer
model.add(Conv2D(filters=384, kernel_size=(4,4), strides=1, activation=LeakyReLU(), padding='same'))#input_shape = (8,8,256),

model.add(MaxPooling2D(pool_size=(3,3),strides=2,padding='valid'))

#6th Conv layer
model.add(Conv2D(filters=512, kernel_size=(4,4), strides=1, activation=LeakyReLU(), padding='same'))#input_shape = (4,4,384),

model.add(MaxPooling2D(pool_size=(3,3),strides=2,padding='valid'))

#FC
model.add(Flatten())
#1st FC
model.add(Dense(1024))
model.add(Activation(LeakyReLU()))
model.add(Dropout(0,5))
#2nd FC
model.add(Dense(1024))
model.add(Activation(LeakyReLU()))
model.add(Dropout(0,5))
# Output Layer
model.add(Dense(5))
model.add(Activation('softmax'))