tensorflow高训练和验证准确度，但预测糟糕

Question

我正在使用Tensorflow进行Resnet，我的训练精度为损耗：0.1121-精度：0.9591 ，而我的验证精度为损耗：0.2006-精度：0.9405 。但是，当我运行model.predict（）时，输出完全错误。我什至用训练数据集进行了尝试，并得到了输出：

[0.1367569  0.2681733  0.0271427  0.3046791  0.01867249 0.05539117 0.00498121 0.00070545 0.03091554 0.00495859 0.00811522 0.01736604 0.02292746 0.00100211]

但我应该得到输出

[0 1 0 0 0 0 0 0 0 0 1 0 0 0]

我已经训练了20个纪元。这是我的代码：

if silence_debug:
    import os
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.keras import layers, models, backend
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import pandas as pd



#load images
df=pd.read_csv("/content/drive/My Drive/trainlist.csv")
columns = [
    "Atelectasis",
    "Cardiomegaly",
    "Effusion",
    "Infiltration",
    "Mass", 
    "Nodule",
    "Pneumonia",
    "Pneumothorax",
    "Consolidation",
    "Edema",
    "Emphysema",
    "Fibrosis",
    "Pleural Thickening",
    "Hernia",
]
datagen = ImageDataGenerator(
    rescale=1./255.,
    rotation_range=7,
    horizontal_flip=True,
)
traindata = datagen.flow_from_dataframe(
    dataframe=df[:76800],
    directory="/content/traindata/traindata",
    x_col="filename",
    y_col=columns,
    color_mode='grayscale',
    batch_size=8,
    class_mode="raw",
    target_size=(448,448),
    shuffle=False,
)

#load model
if load:
  #load saved model
  print("Loading model")
  model = load_model('/content/drive/My Drive/net_1.h5')
else:
    #create model
    def identity_block(input_tensor, filters):
        f1, f2, f3 = filters
        axis = 3 if backend.image_data_format() == 'channels_last' else 1
        #conv1
        res = layers.Conv2D(f1, (1,1), 1)(input_tensor)
        res = layers.BatchNormalization(axis=axis, momentum=0.01)(res)
        res = layers.Activation('relu')(res)
        #conv2
        res = layers.Conv2D(f2, (3,3), 1, padding='same')(res)
        res = layers.BatchNormalization(axis=axis, momentum=0.01)(res)
        res = layers.Activation('relu')(res)
        #conv3
        res = layers.Conv2D(f3, (1,1), 1)(res)
        res = layers.BatchNormalization(axis=axis, momentum=0.01)(res)
        res += input_tensor
        res = layers.Activation('relu')(res)
        return res

    def conv_block(input_tensor, filters, stride=2):
        f1, f2, f3 = filters
        axis = 3 if backend.image_data_format() == 'channels_last' else 1
        #conv1
        res = layers.Conv2D(f1, (1,1), 1)(input_tensor)
        res = layers.BatchNormalization(axis=axis, momentum=0.01)(res)
        res = layers.Activation('relu')(res)
        #conv2
        res = layers.Conv2D(f2, (3,3), stride, padding='same')(res)
        res = layers.BatchNormalization(axis=axis, momentum=0.01)(res)
        res = layers.Activation('relu')(res)
        #conv3
        res = layers.Conv2D(f3, (1,1), 1)(res)
        res = layers.BatchNormalization(axis=axis, momentum=0.01)(res)
        #shortcut, resizes input so it can be added with res
        shortcut = layers.Conv2D(f3, (1,1), stride)(input_tensor)
        shortcut = layers.BatchNormalization(axis=axis, momentum=0.01)(shortcut)
        res += shortcut
        res = layers.Activation('relu')(res)
        return res

    #model
    layerinput = layers.Input(shape=(448, 448, 1))
    layerlist = layers.Conv2D(64, (7,7), 2, activation='relu', padding='same')(layerinput)
    layerlist = layers.MaxPooling2D((3,3), 2, padding='same')(layerlist)
    #resblock 3x
    layerlist = conv_block(layerlist, (64, 64, 256), 1)
    layerlist = identity_block(layerlist, (64, 64, 256))
    layerlist = identity_block(layerlist, (64, 64, 256))
    layerlist = identity_block(layerlist, (64, 64, 256))
    #maxpool
    layerlist = layers.MaxPooling2D((3,3), 2, padding='same')(layerlist)
    #resblock 3x
    layerlist = conv_block(layerlist, (128, 128, 512))
    layerlist = identity_block(layerlist, (128, 128, 512))
    layerlist = identity_block(layerlist, (128, 128, 512))
    layerlist = identity_block(layerlist, (128, 128, 512))
    #resblock 5x
    layerlist = conv_block(layerlist, (256, 256, 1024))
    layerlist = identity_block(layerlist, (256, 256, 1024))
    layerlist = identity_block(layerlist, (256, 256, 1024))
    layerlist = identity_block(layerlist, (256, 256, 1024))
    layerlist = identity_block(layerlist, (256, 256, 1024))
    layerlist = identity_block(layerlist, (256, 256, 1024))
    #resblock 5x
    layerlist = conv_block(layerlist, (512, 512, 2048))
    layerlist = identity_block(layerlist, (512, 512, 2048))
    layerlist = identity_block(layerlist, (512, 512, 2048))
    layerlist = identity_block(layerlist, (512, 512, 2048))
    layerlist = identity_block(layerlist, (512, 512, 2048))
    layerlist = identity_block(layerlist, (512, 512, 2048))
    #fully connected layer
    layerlist = layers.AveragePooling2D((7,7), padding='same')(layerlist)
    layerlist = layers.Flatten()(layerlist)
    layerlist = layers.Dense(2048, activation='relu')(layerlist)
    layerlist = layers.Dense(14, activation='sigmoid')(layerlist)
    model = models.Model(inputs=layerinput, outputs=layerlist)
    #print (model.summary())
    model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])

#train model
try:
    model.fit(
        x=traindata,
        epochs=1,
        verbose=1,
    )
    print("\nsaving model")
    model.save('/content/drive/My Drive/net_1.h5')
except Exception as e:
    print('\n', e)

编辑 该模型是多标签的，因此一张图像可以有多个输出