ImageDataGenerator的标签形状错误

Question

我试图用不同尺寸的图像训练一个模型，通常我会使用flatte，但是flatte（）要求所有图像都有一个固定的尺寸，而我没有

在这里，我试图用globalMapool2D（）替换flatte，但最后我遇到了预期尺寸的问题。我是TensorFlow的新手，我很难理解在哪里可以调整我的模型以避免出现预期形状的问题

代码：（一些导入是不必要的，但它将被进一步使用，我添加了它们以防假定的不兼容）

from __future__ import print_function
import keras

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D, GlobalMaxPool2D
import os
from random import shuffle

train_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our training data
validation_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our validation data
batch_size = 128

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=f"/kaggle/working",
                                                           shuffle=True,
                                                           class_mode='binary')
val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=f"/kaggle/working/",
                                                           shuffle=True,
                                                           class_mode='binary')

model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same', input_shape=(None,None,3))) #We change the input shape because the images have different shapes but always 3 chan.
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

# model.add(Flatten()) #as all the pictures have different size, flatten does not work. Possibly other solutions found there :
model.add(GlobalMaxPool2D())
# https://stackoverflow.com/questions/47795697/how-to-give-variable-size-images-as-input-in-keras
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)

# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
              optimizer=opt,
              metrics=['accuracy'])

# X_train_i = X_train_i.astype('float32')
# X_test_i = X_test_i.astype('float32')
X_train_i /= 255
X_test_i /= 255
model.summary()
model.fit_generator(train_data_gen,
        steps_per_epoch=2000,
        epochs=10,
        validation_data=val_data_gen,
        validation_steps=800)
#             batch_size=batch_size,
#             epochs=epochs,
#             validation_data=(X_test_i, y_test),
#             shuffle=True)


# Score trained model.
scores = model.evaluate(X_test_i, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])

模型总结如下：

Model: "sequential_11"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_20 (Conv2D)           (None, None, None, 32)    896       
_________________________________________________________________
activation_38 (Activation)   (None, None, None, 32)    0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, None, None, 32)    9248      
_________________________________________________________________
activation_39 (Activation)   (None, None, None, 32)    0         
_________________________________________________________________
dropout_20 (Dropout)         (None, None, None, 32)    0         
_________________________________________________________________
global_max_pooling2d_9 (Glob (None, 32)                0         
_________________________________________________________________
dense_19 (Dense)             (None, 512)               16896     
_________________________________________________________________
activation_40 (Activation)   (None, 512)               0         
_________________________________________________________________
dropout_21 (Dropout)         (None, 512)               0         
_________________________________________________________________
dense_20 (Dense)             (None, 2)                 1026      
_________________________________________________________________
activation_41 (Activation)   (None, 2)                 0         
=================================================================
Total params: 28,066
Trainable params: 28,066
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10

错误如下：

ValueError: Error when checking target: expected activation_41 to have shape (2,) but got array with shape (1,)

看起来这个值肯定是“减半”了，但我试着去掉了一些图层，我无法让它工作

此外，如果你能推荐一个教程来更好地理解这些概念，我洗耳恭听

非常感谢++

Answer 1

我认为你不应该把n_classes=1（正如你的评论所说）放进去，因为它不是True，可能会带来混乱。您可以使用在所有情况下都有效的方法

无论类的数量是多少，使用class_mode='categorical'在所有情况下都有效

然后，在最后一层中，您甚至不必手动设置类别的数量，您可以执行以下操作：

Dense(units=len(train_data_gen.class_indices))

然后，最终的神经元和类别的数量总是匹配的。然后，始终确保您有一个loss函数，该函数允许一个热编码输出，并且您可以继续（例如，categorical_crossentropy）