類型錯誤：fit_generator() 缺少 1 個必需的位置參數：'generator'

Question

我正在嘗試訓練 CNN 來檢測圖像是否為 deepfake，但在運行代碼時，我不斷收到此錯誤：TypeError: fit_generator() missing 1 required positional argument: 'generator' 如何擺脫此錯誤? 我的代碼有問題嗎？ 我也不確定分類器 class 是否是必要的，所以我已將其包含在內，但已將其注釋掉。

我的完整代碼：

import tensorflow as tf 
config = tf.compat.v1.ConfigProto() 
config.gpu_options.allow_growth = True 
sess = tf.compat.v1.Session(config=config)

import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Input, Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Dropout, Reshape, Concatenate, LeakyReLU
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Model

from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.models import Model

# Height and width refer to the size of the image
# Channels refers to the amount of color channels (red, green, blue)

image_dimensions = {'height':256, 'width':256, 'channels':3}

# Create a Classifier class

#class Classifier():
     
       # def __init__():
           # self.model = 0
    
        #def predict(self, x):
           # return self.model.predict(x)
    
       # def fit(self, x, y):
            #return self.model.train_on_batch(x, y)
    
       # def get_accuracy(self, x, y):
            #return self.model.test_on_batch(x, y)
    
        #def load(self, path):
           # self.model.load_weights(path)

class Meso4(Model):
    def __init__(self, learning_rate = 0.0001):
        self.model = self.init_model()
        optimizer = Adam(lr = learning_rate)
        self.model.compile(optimizer = optimizer,
                           loss = 'mean_squared_error',
                           metrics = ['accuracy'])   
    
        
    
    def init_model(self):
        x = Input(shape = (image_dimensions['height'],
                           image_dimensions['width'],
                           image_dimensions['channels']))
        
        x1 = Conv2D(8, (3, 3), padding='same', activation = 'relu')(x)
        x1 = BatchNormalization()(x1)
        x1 = MaxPooling2D(pool_size=(2, 2), padding='same')(x1)
        
        x2 = Conv2D(8, (5, 5), padding='same', activation = 'relu')(x1)
        x2 = BatchNormalization()(x2)
        x2 = MaxPooling2D(pool_size=(2, 2), padding='same')(x2)
        
        x3 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x2)
        x3 = BatchNormalization()(x3)
        x3 = MaxPooling2D(pool_size=(2, 2), padding='same')(x3)
        
        x4 = Conv2D(16, (5, 5), padding='same', activation = 'relu')(x3)
        x4 = BatchNormalization()(x4)
        x4 = MaxPooling2D(pool_size=(4, 4), padding='same')(x4)
        
        y = Flatten()(x4)
        y = Dropout(0.5)
        y = Dense(16)
        y = LeakyReLU(alpha=0.1)
        y = Dropout(0.5)
        y = Dense(1, activation = 'sigmoid')
        
        return Model(inputs = x, outputs = y)

bat_size = 64
input_size = 256

# initializing a train datagenerator
train_datagen = ImageDataGenerator(rescale=1./255)

# initializing a test datagenerator
test_datagen = ImageDataGenerator(rescale=1./255)

# preprocessing for trainig set
train_set = train_datagen.flow_from_directory(
                            'C:\\Users\\Kevin\\Desktop\\Train', # train data directory
                            target_size=(input_size, input_size), 
                            batch_size=bat_size,
                            class_mode='categorical',
                            color_mode= 'rgb'
                                            )

# preprocessing for test set
test_set = test_datagen.flow_from_directory(
                                'C:\\Users\\Kevin\\Desktop\\Test', # test data directory
                            target_size=(input_size, input_size),
                            batch_size=bat_size,
                            shuffle=False,
                            class_mode='categorical',
                            color_mode= 'rgb'
                                            )
filepath = "FYP.hdf5"
checkpoint = ModelCheckpoint(
                            filepath,
                            monitor='val_acc',
                            verbose=1,
                            save_best_only=True,
                            mode='max'
                            )
Meso4.fit_generator(
                                train_set,
                                steps_per_epoch=1400//bat_size + 1,
                                epochs=25,
                                callbacks=[checkpoint],
                                validation_data=test_set,
                                validation_steps=600 //bat_size + 1
                                )

#ERROR
TypeError                                 Traceback (most recent call last)
<ipython-input-9-00d0b295f968> in <module>
      5                                 callbacks=[checkpoint],
      6                                 validation_data=test_set,
----> 7                                 validation_steps=600 //bat_size + 1
      8                                 )

~\Anaconda3\envs\Tf\lib\site-packages\keras\legacy\interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name + '` call to the ' +
     90                               'Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

TypeError: fit_generator() missing 1 required positional argument: 'generator'

Answer 1

我可以看到 3 或 4 個錯誤：

對於 keras 中的子類化：

• 你需要調用super(YourClass, self).__init__()
• 您在call方法中定義 model

檢查此鏈接以了解有關 keras 子類化的更多信息

同樣在您的 y 部分中，您停止使用功能語法

 y = Flatten()(x4)
 y = Dropout(0.5)
 y = Dense(16)

它應該是

y = Dropout(0.5)(y)
y = Dense(16)(y)

而且你不直接調用 class 只是實例化一個新的 object