类型错误：fit_generator() 缺少 1 个必需的位置参数：'generator'

Question

I am trying to train a CNN to detect if an image is deepfake or not, but upon running the code I keep getting this error: TypeError: fit_generator() missing 1 required positional argument: 'generator' How do I get rid of this error? Is there an issue with my code? Im also not sure if the classifier class is necessary so i've included it but commented it out.

My code in full:

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

3 or 4 Mistakes i can see:

For subclassing in keras:

• You need to call super(YourClass, self).__init__()
• You define your model inside a call method

Check thislink to learn more about keras subclassing

Also in your y part you stop using functional syntax

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

it should be

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

and you dont call class directly just instantiate a new object