전이 학습 : 모델이 변경되지 않은 손실 결과를 제공합니다. 훈련이 아닙니까? [닫은]

Question

Inception V3 에서 회귀 모델 을 훈련 시키려고합니다 . 입력은 크기 (96,320,3)의 이미지입니다. 총 16k + 이미지가 있으며 그중 12k +는 훈련 용이고 나머지는 유효성 검사 용입니다. Inception에서 모든 레이어를 고정했지만 고정 해제해도 도움이되지 않습니다 (이미 시도). 아래 코드에 표시된대로 사전 학습 된 모델의 상단을 몇 개의 레이어로 교체했습니다.

X_train = preprocess_input(X_train)
inception = InceptionV3(weights='imagenet', include_top=False, input_shape=(299,299,3))
inception.trainable = False
print(inception.summary())

driving_input = Input(shape=(96,320,3))
resized_input = Lambda(lambda image: tf.image.resize(image,(299,299)))(driving_input)
inp = inception(resized_input)

x = GlobalAveragePooling2D()(inp)

x = Dense(512, activation = 'relu')(x)
x = Dense(256, activation = 'relu')(x)
x = Dropout(0.25)(x)
x = Dense(128, activation = 'relu')(x)
x = Dense(64, activation = 'relu')(x)
x = Dropout(0.25)(x)
result = Dense(1, activation = 'relu')(x)

lr_schedule = ExponentialDecay(initial_learning_rate=0.1, decay_steps=100000, decay_rate=0.95)
optimizer = Adam(learning_rate=lr_schedule)
loss = Huber(delta=0.5, reduction="auto", name="huber_loss")
model = Model(inputs = driving_input, outputs = result)
model.compile(optimizer=optimizer, loss=loss)

checkpoint = ModelCheckpoint(filepath="./ckpts/model.h5", monitor='val_loss', save_best_only=True)
stopper = EarlyStopping(monitor='val_loss', min_delta=0.0003, patience = 10)

batch_size = 32
epochs = 100

model.fit(x=X_train, y=y_train, shuffle=True, validation_split=0.2, epochs=epochs, 
          batch_size=batch_size, verbose=1, callbacks=[checkpoint, stopper])

결과는 다음과 같습니다.

내 모델이 훈련되지 않는 이유는 무엇이며 어떻게 수정해야합니까?

Answer 1

문제가 회귀 문제이므로 마지막 레이어의 활성화 linear는 relu. 또한 학습률이 너무 높으므로 전체 설정에 따라 낮추는 것을 고려해야합니다. MNIST를 사용한 코드 샘플을 보여 드리겠습니다.

# data 
(xtrain, train_target), (xtest, test_target) = tf.keras.datasets.mnist.load_data()
# train_x, MNIST is gray scale, so in order to use it in pretrained weights , extending it to 3 axix
x_train = np.expand_dims(xtrain, axis=-1)
x_train = np.repeat(x_train, 3, axis=-1)
x_train = x_train.astype('float32') / 255
# prepare the label for regression model 
ytrain4 = tf.square(tf.cast(train_target, tf.float32))

# base model 
inception = InceptionV3(weights='imagenet', include_top=False, input_shape=(75,75,3))
inception.trainable = False

# inputs layer
driving_input = tf.keras.layers.Input(shape=(28,28,3))
resized_input = tf.keras.layers.Lambda(lambda image: tf.image.resize(image,(75,75)))(driving_input)
inp = inception(resized_input)

# top model 
x = GlobalAveragePooling2D()(inp)
x = Dense(512, activation = 'relu')(x)
x = Dense(256, activation = 'relu')(x)
x = Dropout(0.25)(x)
x = Dense(128, activation = 'relu')(x)
x = Dense(64, activation = 'relu')(x)
x = Dropout(0.25)(x)
result = Dense(1, activation = 'linear')(x)

# hyper-param
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_learning_rate=0.0001, 
                                                             decay_steps=100000, decay_rate=0.95)
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)
loss = tf.keras.losses.Huber(delta=0.5, reduction="auto", name="huber_loss")

# build models
model = tf.keras.Model(inputs = driving_input, outputs = result)
model.compile(optimizer=optimizer, loss=loss)

# callbacks
checkpoint = tf.keras.callbacks.ModelCheckpoint(filepath="./ckpts/model.h5", monitor='val_loss', save_best_only=True)
stopper = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0.0003, patience = 10)

batch_size = 32
epochs = 10

# fit 
model.fit(x=x_train, y=ytrain4, shuffle=True, validation_split=0.2, epochs=epochs, 
          batch_size=batch_size, verbose=1, callbacks=[checkpoint, stopper])

산출

1500/1500 [==============================] - 27s 18ms/step - loss: 5.2239 - val_loss: 3.6060
Epoch 2/10
1500/1500 [==============================] - 26s 17ms/step - loss: 3.5634 - val_loss: 2.9022
Epoch 3/10
1500/1500 [==============================] - 26s 17ms/step - loss: 3.0629 - val_loss: 2.5063
Epoch 4/10
1500/1500 [==============================] - 26s 17ms/step - loss: 2.7615 - val_loss: 2.3764
Epoch 5/10
1500/1500 [==============================] - 26s 17ms/step - loss: 2.5371 - val_loss: 2.1303
Epoch 6/10
1500/1500 [==============================] - 26s 17ms/step - loss: 2.3848 - val_loss: 2.1373
Epoch 7/10
1500/1500 [==============================] - 26s 17ms/step - loss: 2.2653 - val_loss: 1.9039
Epoch 8/10
1500/1500 [==============================] - 26s 17ms/step - loss: 2.1581 - val_loss: 1.9087
Epoch 9/10
1500/1500 [==============================] - 26s 17ms/step - loss: 2.0518 - val_loss: 1.7193
Epoch 10/10
1500/1500 [==============================] - 26s 17ms/step - loss: 1.9699 - val_loss: 1.8837