Epoch 1/8
222/222 [==============================] - 18s 67ms/step - loss: 1.4523 - accuracy: 0.9709 - val_loss: 1.3310 - val_accuracy: 0.9865
Epoch 2/8
222/222 [==============================] - 14s 63ms/step - loss: 1.3345 - accuracy: 0.9747 - val_loss: 1.2312 - val_accuracy: 0.9865
Epoch 3/8
222/222 [==============================] - 14s 64ms/step - loss: 1.1911 - accuracy: 0.9868 - val_loss: 1.1245 - val_accuracy: 0.9887
Epoch 4/8
222/222 [==============================] - 14s 63ms/step - loss: 1.0926 - accuracy: 0.9873 - val_loss: 1.0798 - val_accuracy: 0.9769
Epoch 5/8
222/222 [==============================] - 14s 63ms/step - loss: 1.0622 - accuracy: 0.9760 - val_loss: 1.0887 - val_accuracy: 0.9555
Epoch 6/8
222/222 [==============================] - 14s 63ms/step - loss: 0.9589 - accuracy: 0.9841 - val_loss: 0.9216 - val_accuracy: 0.9814
Epoch 7/8
222/222 [==============================] - 14s 64ms/step - loss: 0.8648 - accuracy: 0.9885 - val_loss: 0.8241 - val_accuracy: 0.9896
Epoch 8/8
222/222 [==============================] - 14s 63ms/step - loss: 0.7993 - accuracy: 0.9908 - val_loss: 0.7694 - val_accuracy: 0.9893
Model: "model_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_6 (InputLayer) [(None, 32, 32, 3)] 0
_________________________________________________________________
model_1 (Functional) (None, 10) 3250058
=================================================================
Total params: 3,250,058
Trainable params: 3,228,170
Non-trainable params: 21,888
_________________________________________________________________
Epoch 1/8
222/222 [==============================] - 18s 66ms/step - loss: 1.4423 - accuracy: 0.9741 - val_loss: 1.3361 - val_accuracy: 0.9839
Epoch 2/8
222/222 [==============================] - 14s 64ms/step - loss: 1.3457 - accuracy: 0.9734 - val_loss: 1.2327 - val_accuracy: 0.9845
Epoch 3/8
222/222 [==============================] - 14s 63ms/step - loss: 1.1927 - accuracy: 0.9893 - val_loss: 1.1287 - val_accuracy: 0.9870
这是我的输出，正如你在训练后加载模型时看到的那样，与训练前的值相比，损失的值仍然相同。我真的很困惑。
这是我的代码，我想使用两个模型（合并后，最终合并），我使用load_modeand model.save。因为我想模仿联邦学习过程。
希望有人能给我一些想法。
def train2():
img_input = Input(shape=(32, 32, 3))
Mobilenet2 = load_model('Final combining.h5')
output = Mobilenet2(img_input)
model = Model(img_input, output)
model.summary()
# set optimizer
sgd = optimizers.SGD(lr=.1, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# start training
h2 = model.fit(X_train2, y_2_train, batch_size=batch_size,
steps_per_epoch=len(X_train2) // batch_size,
epochs=epochs1,
# callbacks=cbks,
validation_data=(X_test, y_test))
# callbacks=callbacks
model.save('After combining.h5')
def train3():
img_input = Input(shape=(32, 32, 3))
Mobilenet1 = load_model('After combining.h5')
output = Mobilenet1(img_input)
model = Model(img_input, output)
model.summary()
# set optimizer
sgd = optimizers.SGD(lr=.1, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
# start training
h3 = model.fit(X_train1, y_1_train, batch_size=batch_size,
steps_per_epoch=len(X_train1) // batch_size,
epochs=epochs1,
# callbacks=cbks,
validation_data=(X_test, y_test))
# callbacks=callbacks
model.save('Final combining.h5')
我用for循环来控制训练过程，输出的是最后一次迭代...，accuracy和loss的值和第一次迭代差不多
for _ in range(5):
num = 0
if num % 2==0:
train2()
num+=1
else:
train3()
num+=1