model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal')(model)
model = Activation('relu')(model)
model = Conv2D(1, (3, 3), padding='same', kernel_initializer='he_normal')(model)
res_img = model
output_img = add([res_img, input_img])
model = Model(input_img, output_img)
# model.load_weights('./checkpoints/weights-improvement-20-26.93.hdf5')
adam = Adam(lr=0.001, decay=1e-5, clipvalue=0.1, epsilon=1e-8)
sgd = SGD(lr=1e-2, momentum=0.9, decay=1e-4, nesterov=False)
model.compile(adam, loss='mse', metrics=[PSNR, "accuracy"])
model.summary()
# 每个epoch保存一次模型
filepath = "./checkpoints2/vdsr-{epoch:02d}-{PSNR:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor=PSNR, verbose=1, mode='max')
callbacks_list = [checkpoint]
# 记录每次训练的loss，PSNR值
train_log = CSVLogger(filename="train.log")
model.fit(x=train_input, y=train_label, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=callbacks_list, shuffle=True)
print("Done training!!!")
hyper_net = LatentHyperNet(n_comp=19, model=model, layers=layers, dm_method='pls')
hyper_net.fit(X_train, y[train_idx])
X_train = hyper_net.transform(X_train)
X_test = hyper_net.transform(X_test)
inp = Input((X_train.shape[1],))
fc = Dense(n_class)(inp)
model = Activation('softmax')(fc)
model = Model(inp, model)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='Adadelta')
callbacks = [cm.custom_stopping(value=cm.loss, verbose=2)]
model.fit(X_train, y[train_idx], batch_size=len(X_train),
epochs=4*cm.n_ep,#The drawback of the method is that it requires more iterations to converge (loss <= cm.loss)
verbose=0, callbacks=callbacks, validation_data=(X_train, y[train_idx]))
y_pred = model.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
y_true = np.argmax(y[test_idx], axis=1)
acc_fold = accuracy_score(y_true, y_pred)
avg_acc.append(acc_fold)
recall_fold = recall_score(y_true, y_pred, average='macro')
avg_recall.append(recall_fold)
f1_fold = f1_score(y_true, y_pred, average='macro')
avg_f1.append(f1_fold)
