def train(trainX, trainY):
m = SRCNN()
m.compile(Adam(lr=0.0003), 'mse')
count = 1
while True:
m.fit(trainX, trainY, batch_size=128, nb_epoch=5)
print("Saving model " + str(count * 5))
m.save(join('./model_' + str(count * 5) + '.h5'))
count += 1
def train_batch(aug, trainX, trainY, load_weight=True):
print("[INFO] compiling model...")
model = SRCNN()
if load_weight and os.path.exists('my_model_weights.h5'):
print("[INFO] import model weights...")
model.load_weights('my_model_weights.h5')
try:
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
# opt = Adam(lr=INIT_LR)
model.compile(loss="mean_squared_error",
optimizer=opt,
metrics=[psnr1])
current_time = datetime.now().strftime("%Y%m%d-%H:%M")
filepath = 'train_model/model-ep{epoch:03d}-loss{loss:.3f}-psnr{psnr1:.3f}-' + current_time + '.h5'
checkpoint = ModelCheckpoint(filepath,
monitor='psnr1',
verbose=1,
save_best_only=True,
mode='max',
period=2)
print("[INFO] training network...")
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),
steps_per_epoch=len(trainX) // BS,
epochs=EPOCHS,
callbacks=[checkpoint])
print("[INFO] training finish")
except KeyboardInterrupt:
print("[INFO] KeyboardInterrupted...")
except Exception as e:
print(e)
else:
plt.figure()
N = EPOCHS
# plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
# plt.plot(np.arange(0, N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, N), H.history["psnr1"], label="psnr")
plt.xlabel("Epoch #")
plt.ylabel("psnr")
plt.legend(loc="upper left")
plt.savefig("plot.png")
finally:
print("[INFO] serializing network...")
model.save("./model.h5")
model.save_weights('my_model_weights.h5')
