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keras - Keras 多输出预期形状并得到形状

问题描述

我正在尝试训练一个检测 128d 向量来识别人脸的模型。模型的输入是图像，输出是从“face_recognition”库中获取的 128d 向量（回归）。

当我将 128 个输出用于训练时，我得到了这个错误：

ValueError: Error when checking target: expected dense_24 to have shape (1,) but got array with shape (128,)

但是当我只尝试一个输出时，fit 函数就起作用了。该预测形状的奇怪部分是 (1, 128) 但我不能给出 128 的输出来训练。

这是我的模型：

from keras.applications.vgg16 import VGG16
from keras.layers import Flatten, Dense
import keras
def build_facereg_disc():
  # load model
  model = VGG16(include_top=False, input_shape=(64, 64, 3))
  # add new classifier layers
  flat1 = Flatten()(model.outputs)
  class1 = Dense(2048, activation='relu')(flat1)
  output = Dense(128, activation='relu')(class1)
  # define new model
  model = models.Model(inputs=model.inputs, outputs=output)
  # summarize
  return model

facereg_disc = build_facereg_disc()
facereg_disc.compile(optimizer=keras.optimizers.Adam(),  # Optimizer
              # Loss function to minimize
              loss=keras.losses.SparseCategoricalCrossentropy(),
              # List of metrics to monitor
              metrics=['binary_crossentropy'])

和总结：

Model: "model_27"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_20 (InputLayer)        (None, 64, 64, 3)         0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         
_________________________________________________________________
flatten_10 (Flatten)         (None, 2048)              0         
_________________________________________________________________
dense_23 (Dense)             (None, 2048)              4196352   
_________________________________________________________________
dense_24 (Dense)             (None, 128)               262272    
=================================================================
Total params: 19,173,312
Trainable params: 19,173,312
Non-trainable params: 0

这是预处理：

dir_data      = "data_faces/img_align_celeba/"
Ntrain        = 2000 
Ntest         = 100
nm_imgs       = np.sort(os.listdir(dir_data))
## name of the jpg files for training set
nm_imgs_train = nm_imgs[:Ntrain]
## name of the jpg files for the testing data
nm_imgs_test  = nm_imgs[Ntrain:Ntrain + Ntest]
img_shape     = (64, 64, 3)

def get_npdata(nm_imgs_train):
    X_train = []
    for i, myid in enumerate(nm_imgs_train):
        image = load_img(dir_data + "/" + myid,
                         target_size=img_shape[:2])
        image = img_to_array(image)/255.0
        X_train.append(image)
    X_train = np.array(X_train)
    return(X_train)

X_train = get_npdata(nm_imgs_train)

X_train.shape = (2000, 64, 64, 3)
y_train.shape = (2000, 128)

我使用批量大小，如：

idx = np.random.randint(0, X_train.shape[0], half_batch)
            imgs = X_train[idx]

            labels = y_train[idx]

            reg_d_loss_real = facereg_disc.train_on_batch(imgs, labels)

标签： kerasdeep-learning

解决方案

您的问题来自您的损失函数。正如文档中所解释的，SparseCategoricalCrossentropy期望每个样本y_true是一个整数编码类，而CategoricalCrossentropy期望一个热编码表示（这是你的情况）。

所以，切换到CategoricalCrossentropy，你应该没问题。

但是，要重现，我必须更改：

flat1 = Flatten()(model.outputs)

至：

flat1 = Flatten()(model.outputs[0])

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