我正在尝试使用迁移学习在一组新颖的图像上训练 InceptionV3。我遇到了这个问题 - 这显然与输入和输出维度的不匹配有关（我认为），但我似乎无法确定问题）。之前关于 SO 的所有相关帖子都与 VGG16（我已经开始工作）有关。这是我的代码：
from keras.applications.inception_v3 import InceptionV3
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.callbacks import ModelCheckpoint, TensorBoard, CSVLogger, Callback
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(3, activation='softmax')(x)
model = Model(inputs=base_model.input, output=predictions)
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer=SGD(lr=0.001, momentum=0.9), loss='sparse_categorical_crossentropy')
train_dir = 'hrct_data/ExtractedHRCTs/Train'
validation_dir = 'hrct_data/ExtractedHRCTs/Validation'
nb_train_samples = 21903
nb_validation_samples = 6000
epochs = 30
batch_size = 256
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
validation_datagen = ImageDataGenerator(
rescale=1./255)
train_generator = train_datagen.flow_from_directory(
train_dir,
target_size=(512, 512),
batch_size=batch_size,
class_mode="categorical")
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(512, 512),
batch_size=batch_size,
class_mode="categorical")
model.fit_generator(
train_generator,
steps_per_epoch=21903 // batch_size,
epochs=30,
validation_data=validation_generator,
validation_steps=6000 // batch_size)
model.save_weights('hrct_inception.h5')
这是错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-89-f79a107413cd> in <module>()
4 epochs=30,
5 validation_data=validation_generator,
6 validation_steps=6000 // batch_size)
7 model.save_weights('hrct_inception.h5')
/Users/simonalice/anaconda/lib/python3.5/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
86 warnings.warn('Update your `' + object_name +
87 '` call to the Keras 2 API: ' + signature, stacklevel=2)
88 return func(*args, **kwargs)
89 wrapper._legacy_support_signature = inspect.getargspec(func)
90 return wrapper
/Users/simonalice/anaconda/lib/python3.5/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_q_size, workers, pickle_safe, initial_epoch)
1888 outs = self.train_on_batch(x, y,
1889
sample_weight=sample_weight,
1890 class_weight=class_weight)
1891
1892 if not isinstance(outs, list):
/Users/simonalice/anaconda/lib/python3.5/site-packages/keras/engine/training.py in train_on_batch(self, x, y, sample_weight, class_weight)
1625 sample_weight=sample_weight,
1626 class_weight=class_weight,
1627 check_batch_axis=True)
1628 if self.uses_learning_phase and not
isinstance(K.learning_phase(), int):
1629 ins = x + y + sample_weights + [1.]
/Users/simonalice/anaconda/lib/python3.5/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_axis, batch_size)
1307 output_shapes,
1308 check_batch_axis=False,
1309 exception_prefix='target')
1310 sample_weights = _standardize_sample_weights(sample_weight,
1311 self._feed_output_names)
/Users/simonalice/anaconda/lib/python3.5/site-packages/keras/engine/training.py in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
137 ' to have shape ' + str(shapes[i]) +
138 ' but got array with shape ' +
139 str(array.shape))
140 return arrays
141
ValueError: Error when checking target: expected dense_12 to have shape (None, 1) but got array with shape (256, 3)
任何帮助——即使是让我朝着正确的方向前进，都会有所帮助。