python - AttributeError：“str”对象没有属性“_keras_mask”

Question

以下代码用于深度卷积 GAN：

class Dcgan:
def __init__(self,latent_space,gen_rate,disc_rate,images_source,resized_path,result_path,checkpoint_path,batch_size,optimizer,number_of_images_to_display,epoches):
    self.latent_space = latent_space
    self.gen_rate = gen_rate
    self.disc_rate = disc_rate
    self.images_source = images_source
    self.training_path = os.listdir(self.images_source)
    self.resized_path = resized_path
    self.result_path = result_path
    self.checkpoint_path = checkpoint_path
    self.batch_size = batch_size
    self.optimizer = optimizer
    self.number_of_images_to_display = number_of_images_to_display
    self.epoches = epoches
    self.noise = tf.random.normal([self.batch_size,self.latent_space])
    self.noise_gen_n_save = tf.random.normal([self.number_of_images_to_display,self.latent_space])

def resize_images(self):
    if not os.path.exists(self.resized_path):
        os.mkdir('images')

    for i,image_path in enumerate(os.listdir(self.images_source)):
        img = cv2.imread(os.path.join(self.images_source,image_path), cv2.IMREAD_UNCHANGED) 
        resized = cv2.resize(img, (28,28)) 
        path_new = path_save+'/'+image_path
        status = cv2.imwrite(path_new,resized)

    images=[]
    image_paths = os.listdir('images')
    for image_path in image_paths:
        images.append(imread(os.path.join(path_save,image_path)))

    images = np.array(images).astype('float32')
    self.training_images = (images-127.5)/127.5

def Generator(self):

    generator = tf.keras.Sequential()
    generator.add(Dense(units=8*8*256,input_shape=(self.latent_space,),use_bias=False))
    generator.add(BatchNormalization())
    generator.add(LeakyReLU()) 

    
    generator.add(Reshape((8,8,256)))
    assert generator.output_shape == (None,8,8,256)

    generator.add(Conv2DTranspose(filters = 64 ,kernel_size = (5,5),strides = (2,2),padding="same",use_bias=False)) # 8 to 16
    assert generator.output_shape == (None,16,16,64)
    generator.add(BatchNormalization())
    generator.add(LeakyReLU())

    generator.add(Conv2DTranspose(filters = 16,kernel_size = (5,5),strides = (2,2),padding="same",use_bias=False))  # 16 to 32      
    assert generator.output_shape == (None,32,32,16)
    generator.add(BatchNormalization())
    generator.add(LeakyReLU())
    
    generator.add(Conv2DTranspose(filters = 3,kernel_size = (5,5),strides = (2,2),padding="same",activation='tanh',use_bias=False))  # 32 to 64
    assert generator.output_shape == (None,64,64,3)        
    return generator

def Discriminator(self):
    discriminator = tf.keras.Sequential()
    discriminator.add(Conv2D(filters = 64,kernel_size = (5,5) ,strides = (2,2),padding = "same",activation='relu',input_shape=(64,64, 3)))
    discriminator.add(Dropout(0.3))
    
    discriminator.add(Conv2D(filters = 128,kernel_size = (5,5) ,strides = (2,2),padding = "same",activation='relu'))
    discriminator.add(Dropout(0.3))
    
    discriminator.add(Conv2D(filters = 256,kernel_size = (5,5) ,strides = (2,2),padding = "same",activation='relu'))
    discriminator.add(Dropout(0.3))
    
    discriminator.add(Conv2D(filters = 512,kernel_size = (5,5) ,strides = (2,2),padding = "same",activation='relu'))
    discriminator.add(Dropout(0.3))
    
    discriminator.add(Flatten())
    discriminator.add(Dense(1))
    
    return discriminator

def generator_loss(self):
    self.gen_loss = BinaryCrossentropy(tf.ones_like(self.fake_output),self.fake_output)
    return self.gen_loss

def discriminator_loss(self):
    self.disc_loss_real = BinaryCrossentropy(tf.ones_like(self.real_output),self.real_output)        
    self.disc_loss_fake = BinaryCrossentropy(tf.zeros_like(self.fake_output),self.fake_output)
    self.total_disc_loss = self.disc_loss_real+self.disc_loss_fake
    return self.total_disc_loss
    
@tf.function
def train_step(self):
    generator,discriminator = self.Generator(),self.Discriminator()
    with tf.GradientTape() as grad_gen, tf.GradientTape() as grad_disc:
        self.generated_images = generator(self.noise,training=True)
        self.real_output = discriminator(self.training_path,training=True)            
        self.fake_output = discriminator(self.generated_images,training=True)            
        self.discriminator_loss()
        self.generator_loss()
        self.gen_gradients = grad_gen.gradient(self.gen_loss,generator.trainable_variables)
        self.disc_gradients = grad_disc.gradient(self.total_disc_loss,discriminator.trainable_variables)
        
    Adam(learning_rate=self.gen_rate).apply_gradients(zip(self.gen_gradients,generator.trainable_variables))
    Adam(learning_rate=self.disc_rate).apply_gradients(zip(self.disc_gradients,discriminator.trainable_variables))
    
    return self.gen_loss,self.total_disc_loss


def display_and_save(self,epoch):
    
    output_images = self.generator(self.noise_gen_n_save,training = False)
    
    plt.figure(figsize=(13,13))

    for i in range(self.number_of_images_to_display):
        plt.subplot(8,8,i+1)
        plt.imshow(output_images[i,:,:,0])
        plt.axis('off')
    
    plt.savefig(fname='Traning images\Generated images after epoch : {}'.format(epoch),)
    
def training(self):
    gen_losses,total_disc_losses = [],[]
    for epoch in range(self.epoches):
        start_time = time.time()
        self.train_step()
        gen_loss.append(self.gen_loss.numpy(),self.total_disc_loss.numpy())
        display.clear_output(wait=True)
        display_and_save(epoch)
    return gen_losses,total_disc_losses
        

当我运行时：

dcgan = Dcgan(

    latent_space = 100,
    gen_rate = 0.0002,
    disc_rate = 0.0003,
    images_source = 'D:/PlantVillage-Dataset/raw/color/Orange___Haunglongbing_(Citrus_greening)',
    resized_path = 'resized_images',
    result_path = 'generated_images',
    checkpoint_path = 'training_checkpoints',
    batch_size = 256,
    optimizer = Adam,
    number_of_images_to_display = 16,
    epoches = 100,

)
dcgan.training()

我收到以下警告，然后是属性错误：

WARNING:tensorflow:Entity <bound method Dcgan.Generator of <__main__.Dcgan object at 0x0000024B49994E88>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Failed to parse source code of <bound method Dcgan.Generator of <__main__.Dcgan object at 0x0000024B49994E88>>, which Python reported as:
    def Generator(self): ## followed by the entire code of Generator
......
AttributeError                            Traceback (most recent call last)
<ipython-input-122-9387d007efdb> in <module>
----> 1 dcgan.training()

<ipython-input-118-f9eb662d913f> in training(self)
    134         for epoch in range(self.epoches):
    135             start_time = time.time()
--> 136             self.train_step()
AttributeError: 'str' object has no attribute '_keras_mask'

我对python还是很陌生。我确信该错误是由于我犯的一些错误而不是 Python 本身的错误（如警告所示）。我只是想不通它是什么...

标签： pythonclassmachine-learningdeep-learning