Epoch 1/2 103/Unknown - 8s 80ms/step - 损失：0.0175（model.fit() 保持 运行 永远，即使在超过训练图像总数之后）

Question

我正在数据集 https://www.kaggle.com/jessicali9530/celeba-dataset.

上开发自动编码器

import tensorflow
tensorflow.__version__

---

输出：

'2.2.0-rc3'

---

from tensorflow.keras.preprocessing import image
data_gen = image.ImageDataGenerator(rescale=1.0/255)

batch_size = 20

train_data_gen = data_gen.flow_from_directory(directory=train_dest_path,
                                              target_size=(256, 256),
                                             batch_size=batch_size,
                                             shuffle=True,
                                              class_mode = 'input')
test_data_gen = data_gen.flow_from_directory(directory=test_dest_path,
                                             target_size=(256,256),
                                            batch_size=batch_size,
                                            shuffle=True,
                                             class_mode= 'input')
# autoencoder 
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, UpSampling2D
from tensorflow.keras import Model
from tensorflow.keras.optimizers import Adam, SGD

#parameters
inchannel = 3 
x, y  = 256, 256
input_img = Input(shape=(x,y,inchannel))

def autoencoder_model(input_img):
    #encoder 
    conv1 = Conv2D(32, kernel_size=(3,3), activation='relu', padding='same')(input_img)
    pool1 = MaxPooling2D(pool_size=(2,2))(conv1)
    conv2 = Conv2D(64, kernel_size=(3,3), activation='relu', padding='same')(pool1)
    pool2 = MaxPooling2D(pool_size=(2,2))(conv2)
    conv3 = Conv2D(128, kernel_size=(3,3), activation='relu', padding='same')(pool2)

    #decoder 
    conv4 = Conv2D(128, kernel_size=(3,3), activation='relu', padding='same')(conv3)
    pool3 = UpSampling2D(size=(2,2))(conv4)
    conv5 = Conv2D(64, kernel_size=(3,3), activation='relu', padding='same')(pool3)
    pool4 = UpSampling2D(size=(2,2))(conv5)
    decoded = Conv2D(3, kernel_size=(3,3), activation='relu', padding='same')(pool4)
    return decoded

model = Model(inputs=input_img, outputs=autoencoder_model(input_img))
model.compile(loss='mean_squared_error', optimizer=Adam())
model.summary()

---

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 256, 256, 3)]     0         
_________________________________________________________________
conv2d (Conv2D)              (None, 256, 256, 32)      896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 128, 128, 32)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 128, 128, 64)      18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 64, 64, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 64, 64, 128)       73856     
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 64, 64, 128)       147584    
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 128, 128, 128)     0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 128, 128, 64)      73792     
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 256, 256, 64)      0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 256, 256, 3)       1731      
=================================================================
Total params: 316,355
Trainable params: 316,355
Non-trainable params: 0

---

from tensorflow.keras.callbacks import ModelCheckpoint

epochs = 2
num_training_steps = train_data_gen.samples/batch_size
checkpoint_directory = '/gdrive/My Drive/Colab Notebooks'

checkpoint = ModelCheckpoint(checkpoint_directory, verbose=1, save_weights_only=False, save_freq='epoch')
model.fit(train_data_gen, epochs=epochs, verbose=1, callbacks=[checkpoint])

输出：

Epoch 1/2
    103/Unknown - 8s 80ms/step - loss: 0.0175

花了很多时间后，我仍然无法理解为什么我在 model.fit() 的输出中得到 "Unknown"。此外，model.fit() 将永远保持 运行，即使我只从 flow_from_directory() 中的训练数据集中获取 1000 张图像。它超过 1000，我不明白为什么会这样。

Answer 1

当使用生成器作为输入执行 model.fit 时，您必须设置 steps_per_epoch 参数。对于生成器，您无法知道它们输出的图像数量（在这种情况下它们会永远持续下去），因此将其设置为数据集中的图像数量除以批量大小。