数据挖掘 - 我的训练精度是 1.0，但对训练数据的预测是错误的 - 吾爱随笔录

我的神经网络工作不正常，我正试图找出发生了什么。

我只将三张图像插入到迁移学习（mobilenet）神经网络中。这三个图像的类是：array([[0., 0., 0., 1.], [0., 1., 0., 0.], [0., 1., 0., 0. ]])

我在这些图片上做了 50 个 epoch，到了第 20 个 epoch 左右，训练精度保持在 1.0：

纪元 50/50 3/3 [===============================] - 6s 2s/step - loss: 1.3671 - acc ：1.0000 - val_loss：1.3770 - val_acc：0.0000e+00

然后当我去预测相同的三个图像的结果时，如下所示： predictions_test_2 = model_mn.predict(X, batch_size=1, verbose=1)

预测为：阵列（[[[0.2473848，0.25099277，0.251868，0.24975444]，[0.24154082，0.25245225，0.25358915，0.2524177]

如果训练精度是 1.0，那怎么可能？！

这是代码： def mobilenet(img_rows, img_cols, channel=1, num_classes=None):

model = MobileNet( include_top=True,weights='imagenet')

model.layers.pop()

model.outputs = [model.layers[-1].output]

model.layers[-1].outbound_nodes = []

x=Dense(num_classes, activation='softmax')(model.output)

model=Model(model.input,x)

#To set the first 8 layers to non-trainable (weights will not be updated)

for layer in model.layers[:8]:

   layer.trainable = False
model_new = Sequential()
for layer in model.layers[:-1]: # just exclude last layer from copying
    model_new.add(layer)
model=model_new
model.add(Dense(256,activation='relu',input_shape=(1000,)))

model.add(Dense(64, activation='relu'))
model.add(Dense(4,activation='softmax'))


# Learning rate is changed to 0.001
sgd = SGD(lr=1e-6,decay=1e-1,momentum=0.95, nesterov=True)
adam=Adam(lr=1e-6, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0000001, amsgrad=True)
model.compile(optimizer=adam, loss='categorical_crossentropy',metrics=['accuracy'])


# checkpoint
filepath="weights-improvement-mn-{epoch:02d}-{val_acc:.2f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
return model

model_mn = mobilenet(img_rows, img_cols, channel, num_classes)

model_mn.fit(X, Y,batch_size=3,epochs=50,shuffle=True,verbose=1,validation_data=(X_vall, Y_vall))