训练期间的准确率为10%，然而在训练数据上使用相同模型的预测仅给出3.5%的准确率

Question

我正在用低于100次迭代/历元的代码训练VGG16块5，在块5的输出后添加两个密集和丢失层。

seq = Sequential()
vgg_base = VGG16(weights='imagenet', input_shape=(224, 224, 3),include_top=False, pooling='avg')
seq.add(vgg_base)

for layer in vgg_base.layers:
layer.trainable = False
for layer in vgg_base.layers[:14]:
layer.trainable = False
for layer in vgg_base.layers[15:]:
layer.trainable = True

regularizer = tf.keras.regularizers.l2(1e-2)
for layer in vgg_base.layers[15:]:
for attr in ['kernel_regularizer']:
if hasattr(layer, attr):
setattr(layer, attr, regularizer)
vgg_base.summary()
#seq.add(Flatten())
input_a = Input(shape=(224, 224, 3))
out_a = seq(input_a)
hidden1 = Dense(128, activation='relu', name='dense_1', kernel_regularizer=tf.keras.regularizers.l2(0.001),
activity_regularizer=tf.keras.regularizers.l2(0.001))(out_a)
hidden_drp1 = Dropout(0.5)(hidden1)
hidden2 = Dense(32, activation='relu', name='dense_2',kernel_regularizer=tf.keras.regularizers.l2(0.001),
activity_regularizer=tf.keras.regularizers.l2(0.001))(hidden_drp1)
hidden_drp2 = Dropout(0.2)(hidden2)
out = Dense(1, activation='sigmoid', name='dense_3')(hidden_drp2)
model = Model(input_a, out)

培训设置：

if os.path.exists(filepath):
print('weights found... loading...')
model.load_weights(filepath)
train_datagen = ImageDataGenerator(rescale=1.0/255)
#,validation_split=0.4)
#        validation_split=0.3) # set validation split
train_path = 'C:/fromtrainv4/'
train_set = train_datagen.flow_from_directory(
train_path,
target_size=(224,224),
shuffle=True,
color_mode='rgb',
batch_size=100,
class_mode='binary')
optim = Adam(lr=0.001)
loss = 'binary_crossentropy'
metrics = ['binary_accuracy', 'acc']
model.compile(loss=loss,
optimizer=optim,
metrics=metrics)
checkpoint = ModelCheckpoint(filepath,
monitor='loss',
verbose=1,
save_weights_only=False,
save_best_only=True,
mode='min')
my_callback = [tf.keras.callbacks.EarlyStopping(monitor='loss', patience=10),
checkpoint,tf.keras.callbacks.TensorBoard(log_dir='C:\traning_logs\')]
step_size_train = train_set.n//train_set.batch_size
history = model.fit_generator(train_set,
steps_per_epoch=step_size_train,
shuffle=False,
epochs=100,
callbacks=my_callback,
class_weight='balanced')

我已经确保数据具有与image_generator和其他一切相同的缩放比例。我使用以下代码加载模型：

from tensorflow.keras.models import load_model
model = load_model('C:/models/model.h5')

系统规格：

Processor: Intell Xeon
Ram: 16GB
GPU: NVIDIA RTX 2080 Super
Memory: M2. 256GB
Interpreter: Spyder Python 3.7.1
tensorflow version: 1.14.0
keras version: 2.3.1

有人能告诉我我可能做错了什么吗。如果需要更多的信息，请告诉我。

Answer 1

我猜您的模型正在做所谓的"过拟合"；。

简言之，通过对同一数据集进行100次训练迭代，它可以记住你的训练数据集，因此在测试时会有很好的结果。然而，它还没有找到问题的一般答案，这会导致在使用具有不同输入的新数据集测试时结果较低。