全连接层输出ValueError

Question

我正在做一个青光眼检测CNN，我在最后的致密层得到了以下的错误信息，除了1以外的任何其他数字。由于分类的数量是2，我需要在激活函数之前给出ValueError: Error when checking target: expected activation_1 to have shape (2,) but got array with shape (1,) (2)。但每当我使用Dense(1)运行代码时，我都会获得良好的准确性，但在测试期间，所有内容都被预测为来自同一个类。如何在不将密集层更改回Dense(1)的情况下解决此错误

代码如下：

img_width, img_height = 256, 256
input_shape = (img_width, img_height, 3)

train_data_dir = "data/train"
validation_data_dir = "data/validation"
nb_train_samples = 500
nb_validation_samples = 50
batch_size = 10
epochs = 10

model = Sequential()

model.add(Conv2D(3, (11, 11), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))

model.add(Conv2D(96, (5, 5), activation='relu'))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(192, (3, 3)))
model.add(MaxPooling2D(pool_size=(3, 3), strides=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(192, (3, 3)))

model.add(Flatten())
model.add(Dense(2))
model.add(Activation('softmax'))

model.summary()
model.compile(loss="binary_crossentropy", optimizer=optimizers.Adam(lr=0.001, beta_1=0.9,
                                                                    beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False), metrics=["accuracy"])


# Initiate the train and test generators with data Augumentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    horizontal_flip=True,
    rotation_range=30)

test_datagen = ImageDataGenerator(
    rescale=1./255,
    horizontal_flip=True,
    rotation_range=30)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_height, img_width),
    batch_size=batch_size,
    class_mode="binary")

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_height, img_width),
    class_mode="binary")


model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size

)

model.save('f1.h5')

任何帮助都将不胜感激。

Answer 1

这是因为您在图像生成器中指定了class_mode='binary'，这意味着两个类将被编码为0或1，而不是1,0或0,1。您可以通过将最终层更改为：

model.add(Dense(1, activation='sigmoid'))
# No need for softmax activation
model.compile(loss='binary_crossentropy', ...)

0-1上的二进制交叉熵在数学上等同于具有交叉熵的2类softmax，因此您可以实现相同的事情。