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 ...")
