如何修復InvalidArgumentError：logits和標簽必須是可廣播的：logits_size=[32,198]labels_size=[32,3]

我實際上是在嘗試對影像進行表面缺陷檢測（檢查墻壁上的缺陷，如裂縫……）當我嘗試擬合模型時，它會拋出錯誤日志，標簽必須是 broadcastable: logits_size=[32,198] labels_size=[32,3]

我嘗試了幾種方法，但沒有任何效果。我如何克服錯誤或者我選擇的方法有什么問題？我正在處理的資料是未標記的影像資料（所有影像都在一個檔案夾中）

from keras.preprocessing.image import ImageDataGenerator

train_model = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_model = ImageDataGenerator(rescale = 1./255)

training_data = train_model.flow_from_directory('/Users/nm2/Public/ai-dataset-training-100/5/23_463_DISTACCO_DEL_COPRIFERRO_Q100_training_dataset',
                                                 target_size = (224, 224),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')

testing_data = test_model.flow_from_directory('/Users/nm2/Public/ai-dataset-training-100/5/23_463_DISTACCO_DEL_COPRIFERRO_Q100_training_dataset',
                                            target_size = (224, 224),
                                            batch_size = 32,
                                            class_mode = 'categorical')

IMAGE_SIZE = [224, 224]

#Import the Vgg 16 and add the preprocessing layer to front of the VGG16 Here we will use ImageNet  PreTrained Weights

vgg_model = VGG16(input_shape=IMAGE_SIZE   [3], weights='imagenet', include_top=False)


for layer in vgg_model.layers:
    layer.trainable = False

x = Flatten()(vgg_model.output)

#We use glob function to find out how many files are there in the working directory and count the number of classes they belong to.

folder_count = glob('/Users/nm2/Public/ai-dataset-training-`100/5/23_493_PANORAMICA_LIVELLO_BASE_ISPEZIONE_Q100_training_dataset/*')`

prediction = Dense(len(folder_count), activation='softmax')(x)

#Create a Model 
model = Model(inputs=vgg_model.input, outputs=prediction)

model.summary()

model.compile(
  loss='categorical_crossentropy',
  optimizer='adam',
  metrics=['accuracy']
)


post_run = model.fit(training_data,
  validation_data=testing_data,
  epochs=10,
  steps_per_epoch=len(training_data),
  validation_steps=len(testing_data))


InvalidArgumentError:  logits and labels must be broadcastable: logits_size=[32,198] labels_size=[32,3]
     [[node categorical_crossentropy/softmax_cross_entropy_with_logits (defined at var/folders/3b/tfwxbsyd41j64kbrjghzrvcm0000gq/T/ipykernel_1068/3441923959.py:5) ]] [Op:__inference_train_function_1205]

Function call stack:
train_function

uj5u.com熱心網友回復：

你有這個代碼作為你的模型頂層

prediction = Dense(len(folder_count), activation='softmax')(x)

該層中的神經元數量應與您擁有的類別數量相同。同樣在 model.fit 你有

steps_per_epoch=len(training_data), validation_steps=len(testing_data))

這應該是

batch_size=32
steps_per_epoch=len(training_data)/batch_size
validation_steps=len(testing_data)/batch_size

或者不指定這些值，model.fit 將在內部確定正確的值。你也有代碼

vgg_model = VGG16(input_shape=IMAGE_SIZE   [3]

將此更改為

vgg_model = VGG16(input_shape=[224,224,3]

uj5u.com熱心網友回復：

這是應該適合您的完整代碼。

import tensorflow as tf
import tensorflow as tf
from tensorflow import keras
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.metrics import categorical_crossentropy
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Activation,Dropout

data_dir=r'C:\Temp\DATA' # directory where the image files are stored change to your directory
vsplit=.2 #percentage of data to be used for validation
IMAGE_SIZE = [224, 224]
IMAGE_SHAPE=[224,224,3]

train_model = ImageDataGenerator(rescale = 1./255, shear_range = 0.2, zoom_range = 0.2,
                                   horizontal_flip = True, validation_split=vsplit)
test_model = ImageDataGenerator(rescale = 1./255, validation_split=vsplit)
training_data = train_model.flow_from_directory(data_dir, target_size = IMAGE_SIZE,batch_size = 32,
                                                 class_mode = 'categorical',  subset='training',
                                                 shuffle = True, seed=123)
testing_data =  test_model.flow_from_directory(data_dir, target_size = IMAGE_SIZE, batch_size = 32,
                                                class_mode = 'categorical', subset='validation',
                                                shuffle=True, seed=123)
class_dict=training_data.class_indices
classes=list(class_dict.keys())
print ('LIST OF CLASSES ', classes)
print ('CLASS DICTIONARY ',class_dict)
number_of_classes=len(classes) # this is the number of neurons in your top layer of the model
print ('Number of classes = ', number_of_classes)

base_model=tf.keras.applications.VGG19(include_top=False, weights="imagenet",input_shape=IMAGE_SHAPE, pooling='max') 
# Note setting pooling='max' eliminates the need for a flatten layer
# I do not recommend using VGG it is a very large model I recommend using EfficientNetB3- note do not rescale
# the pixel for Efficient net. 
x=base_model.output
base_model.trainable=False
x = Dense(256, activation='relu')(x)
x=Dropout(rate=.45, seed=123)(x)        
output=Dense(number_of_classes, activation='softmax')(x)
model=Model(inputs=base_model.input, outputs=output)
model.compile(Adam(learning_rate=.001), loss='categorical_crossentropy', metrics=['accuracy']) 

epochs=5
# I recommend the use of callbacks to control the learning rate and early stopping
rlronp=tf.keras.callbacks.ReduceLROnPlateau( monitor="val_loss", factor=0.5,  patience=1, verbose=1)
estop=tf.keras.callbacks.EarlyStopping( monitor="val_loss", patience=3, verbose=1, restore_best_weights=True)
history=model.fit(x=training_data,  epochs=epochs, verbose=1,  validation_data=testing_data,
                  callbacks=[rlronp, estop], validation_steps=None,  shuffle=True,  initial_epoch=0)

# Fine Tune the model
base_model.trainable=True # make the base model trainable
epochs=5
history=model.fit(x=training_data,  epochs=epochs, verbose=1,  validation_data=testing_data,
                  callbacks=[rlronp, estop], validation_steps=None,  shuffle=True,  initial_epoch=0)

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