Я пытаюсь собрать и обучить версию SegNet из здесь в Керасе и получить ValueError во время обучения. Мои данные о тренировках имеют вид:
RGB изображения:
train.shape
(382,200,200,3)
Маски с одноразовыми метками для 3 региональных классов:
label.shape
(382,200,200,3)
Модель выглядит следующим образом:
inshape=(200, 200, 3)
classes=3
# c.f. https://github.com/alexgkendall/SegNet-Tutorial/blob/master/Example_Models/bayesian_segnet_camvid.prototxt
img_input = Input(shape=inshape)
x = img_input
# Encoder
x = Conv2D(64, (3, 3), padding="same")(x)
print(x.shape)
#x = BatchNormalization()(x)
x = Activation("relu")(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
print(x.shape)
x = Conv2D(128, (3, 3), padding="same")(x)
print(x.shape)
#x = BatchNormalization()(x)
x = Activation("relu")(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
print(x.shape)
x = Conv2D(256, (3, 3), padding="same")(x)
print(x.shape)
#x = BatchNormalization()(x)
x = Activation("relu")(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
print(x.shape)
x = Conv2D(512, (3, 3), padding="same")(x)
print(x.shape)
#x = BatchNormalization()(x)
x = Activation("relu")(x)
# Decoder
x = Conv2D(512, (3, 3), padding="same")(x)
print(x.shape)
#x = BatchNormalization()(x)
x = Activation("relu")(x)
x = UpSampling2D(size=(2, 2))(x)
print(x.shape)
x = Conv2D(256, (3, 3), padding="same")(x)
print(x.shape)
#x = BatchNormalization()(x)
x = Activation("relu")(x)
x = UpSampling2D(size=(2, 2))(x)
print(x.shape)
x = Conv2D(128, (3, 3), padding="same")(x)
print(x.shape)
#x = BatchNormalization()(x)
x = Activation("relu")(x)
x = UpSampling2D(size=(2, 2))(x)
print(x.shape)
x = Conv2D(128, (3, 3), padding="same")(x)
print(x.shape)
#x = BatchNormalization()(x)
x = Activation("relu")(x)
x = Convolution2D(classes, (1,1), padding="valid")(x)
print(x.shape)
#x = Dense(classes,activation='softmax')(x)
x = Reshape((inshape[0]*inshape[1],classes), input_shape = (200,200,3))(x)
#x = x.reshape((-1,40000,3))
print(x.shape)
x = Activation("softmax")(x)
model = Model(img_input, x)
В распечатке выписок:
(?, 200, 200, 64)
(?, 100, 100, 64)
(?, 100, 100, 128)
(?, 50, 50, 128)
(?, 50, 50, 256)
(?, 25, 25, 256)
(?, 25, 25, 512)
(?, 25, 25, 512)
(?, 50, 50, 512)
(?, 50, 50, 256)
(?, 100, 100, 256)
(?, 100, 100, 128)
(?, 200, 200, 128)
(?, 200, 200, 128)
(?, 200, 200, 3)
(?, 40000, 3)
Компиляция с:
model.compile(loss = 'categorical_crossentropy', optimizer = 'adadelta', metrics=["accuracy"])
работает нормально.
Установка с:
model.fit(train,label,epochs = 1)
выдает следующую ошибку:
ValueError Traceback (most recent call last)
<ipython-input-28-fd1c2ff82d80> in <module>()
----> 1 model.fit(train,label,epochs = 1)
c:\program files\python36\lib\site-packages\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1628 sample_weight=sample_weight,
1629 class_weight=class_weight,
-> 1630 batch_size=batch_size)
1631 # Prepare validation data.
1632 do_validation = False
c:\program files\python36\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
1478 output_shapes,
1479 check_batch_axis=False,
-> 1480 exception_prefix='target')
1481 sample_weights = _standardize_sample_weights(sample_weight,
1482 self._feed_output_names)
c:\program files\python36\lib\site-packages\keras\engine\training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
111 ': expected ' + names[i] + ' to have ' +
112 str(len(shape)) + ' dimensions, but got array '
--> 113 'with shape ' + str(data_shape))
114 if not check_batch_axis:
115 data_shape = data_shape[1:]
ValueError: Error when checking target: expected activation_60 to have 3 dimensions, but got array with shape (383, 200, 200, 3)
Любой совет будет высоко ценится.