model = PReLU()(model) model = Conv2D(56, (1, 1), padding='same', kernel_initializer='he_normal')(model) model = PReLU()(model) model = Conv2DTranspose(1, (9, 9), strides=(4, 4), padding='same')(model) output_img = model model = Model(input_img, output_img) # model.load_weights('./checkpoints/weights-improvement-20-26.93.hdf5') model.compile(optimizer='adam', lr=0.0001, loss='mse', metrics=[PSNR, "accuracy"]) model.summary() filepath = "./checkpoints/weights-improvement-{epoch:02d}-{PSNR:.2f}.hdf5" checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max') callbacks_list = [checkpoint] model.fit_generator(image_gen(train_list, scale=INPUT_SCALE), steps_per_epoch=len(train_list) // BATCH_SIZE, \ validation_data=image_gen(test_list, scale=INPUT_SCALE), validation_steps=len(train_list) // BATCH_SIZE, \ epochs=EPOCHS, workers=8, callbacks=callbacks_list) print("Done training!!!") print("Saving the final model ...")
model = Conv2D(56, (1, 1), padding='same', kernel_initializer='he_normal')(model) model = PReLU()(model) model = Conv2DTranspose(1, (9, 9), strides=(1, 1), padding='same')(model) output_img = model model = Model(input_img, output_img) # model.load_weights('/checkpoints/weights-improvement-20-26.93.hdf5') # model_path = os.path.join(base_dir, "Results") # model.load_weights(os.path.join(model_path,'fsrcnn_L7_Epoch64_VIS2NIR.h5')) adam = optimizers.Adam(lr=1e-3) model.compile(optimizer=adam, loss='mse', metrics=[PSNR]) model.summary() filepath = os.path.join(data_output, "checkpoints", "weights-improvement-{epoch:02d}-{PSNR:.2f}.hdf5") checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max') callbacks_list = [checkpoint] model.fit(X, y, epochs=EPOCHS, validation_split=0.20, batch_size=BATCH_SIZE, callbacks=callbacks_list, shuffle="batch") print("Done training!!!") print("Saving the final model ...")