# initialize the model
print("[INFO] compiling model...")
model = SmallerVGGNet.build(width=IMAGE_DIMS[1], height=IMAGE_DIMS[0],
	depth=IMAGE_DIMS[2], classes=len(lb.classes_))
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy", optimizer=opt,
	metrics=["accuracy"])

# train the network
print("[INFO] training network...")
H = model.fit(
	x=aug.flow(trainX, trainY, batch_size=BS),
	validation_data=(testX, testY),
	steps_per_epoch=len(trainX) // BS,
	epochs=EPOCHS, verbose=1)