錯誤的 label 形狀與 ImageDataGenerator

Question

我正在嘗試訓練具有不同圖像尺寸的 model，通常我會使用 flatten，但 flatten() 期望所有圖像的尺寸都是固定的，而我沒有。

在這里，我試圖用 GlobalMaxPool2D() 替換 flatten 但最后我遇到了預期尺寸的問題。 我是 TensorFlow 的新手，我很難理解在哪里可以調整我的 model 以避免出現預期形狀的這個問題？

代碼：（一些導入是不必要的，但它會被進一步使用，我添加它們以防假定的不兼容）

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])

model總結如下：

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

錯誤如下：

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

看起來這個值肯定是“減半”，但我試圖刪除一些圖層，但我無法讓它工作。

此外，如果您可以推薦一個教程來更好地理解這些概念，我會全力以赴。

非常感謝++

Answer 1

我認為您不應該放n_classes=1 （正如您的評論所說），因為它不是True ，並且可能會帶來混亂。 您可以使用適用於所有情況的方法。

使用class_mode='categorical'將適用於所有情況，無論類的數量是多少。

然后，在你的最后一層，你甚至不必手動設置類別的數量，你可以這樣做：

Dense(units=len(train_data_gen.class_indices))

然后，您將始終在最終神經元和類別數量之間進行匹配。 然后，始終確保您有一個損失 function 允許單熱編碼 output 並且您對 go 很好（例如， categorical_crossentropy ）