我的模型采用二进制输入，这是从二进制编码的文本（不是一个热编码）。我实现了99.5%的二进制精度和85%的精度。训练结束后，我的测试结果达到了75%左右。然而，如果我重新启动内核并加载模型（使用下面的make_model函数），然后加载 _ 权重，我只能得到35%的准确率。为什么会有这么大的差异？

# start span identification model definition - increased parameters
def make_model(learn_rate: float):
    inputs_sp_can = Input(shape=(MAX_BIN_SPAN_LEN,), name="Candidate Span")
    sp_embedding_layer = Embedding(
        len(char2idx),
        EMBEDDING_DIM,
        embeddings_initializer=keras.initializers.Constant(embed_matrix),
        trainable=False,
    )(inputs_sp_can)
    x = Bidirectional(LSTM(960))(sp_embedding_layer)
    # x = BatchNormalization()(x)
    x = Dense(1920,activation = 'selu')(x)
    x = Dense(640, activation='selu')(x)
    x = Dropout(0.5)(x)
    x = Dense(1280, activation='selu')(x)
    x = models.Model(inputs=inputs_sp_can, outputs=x)

    inputs_sent = Input(shape=(MAX_BIN_SENT_LEN,), name="Input Sentences")
    sent_embedding_layer = Embedding(
        len(char2idx),
        EMBEDDING_DIM,
        embeddings_initializer=keras.initializers.Constant(embed_matrix),
        trainable=False,
    )(inputs_sent)
    y = Bidirectional(LSTM(1800))(sent_embedding_layer)
    # y = BatchNormalization()(y)
    y = Dense(1920,activation = 'selu')(y)
    y = Dense(640, activation='selu')(y)
    y = Dropout(0.5)(y)
    y = Dense(1280, activation='selu')(y)
    y = models.Model(inputs=inputs_sent, outputs=y)

    combined_layer = Concatenate(axis=1)(
        [x.output, y.output]
    )  # ([x.output,w.output,y.output])
    z = Dense(1024, activation='selu')(combined_layer)
    z = Dense(512, activation='selu')(z)
    z = Dense(512, activation='selu')(z)
    # z = Dropout(0.5)(z)
    # z = Dense(1024, activation='selu')(z)

    pre_span_label = Dense(128, activation="selu", name="pre_span_label")(z)
    span_label = Dense(len(label_idx), activation="softmax", name="span_label")(
        pre_span_label
    )

    model = models.Model(
        inputs=[x.input, y.input], outputs=[span_label]
    )  # [x.input,w.input,y.input], outputs = [span_check,span_label])

    myoptimizer = optimizers.Adam(learning_rate=learn_rate, clipnorm=0.1)

    model.compile(
        optimizer=myoptimizer,
        loss="categorical_crossentropy",
        metrics=["binary_accuracy", "accuracy"],
    )

    return model

学习率为0.00015。拟合函数如下：

filepath = save_path + "Char_Tok_Models/Models/weights.{epoch:02d}.hdf5"
checkpoint = ModelCheckpoint(
    filepath,
    monitor="val_loss",
    verbose=1,
    save_best_only=False,
    mode="max",
    period=5
)

callbacks_list = [checkpoint]

history = model.fit(
    [x_span_train, corr_sent_train],
    corr_label_train,
    validation_data=([x_span_val, corr_sent_val], [corr_label_val]),
    epochs=25,
    callbacks=callbacks_list,
    batch_size=64,
)  #

加载权重和评估模型的代码如下：

model = make_model(0.0002)
model.load_weights(save_path + "Char_Tok_Models/Models_Upd/weights.40.hdf5")

model.evaluate([x_span_test, corr_sent_test], corr_label_test)

我很感激你的帮助。谢谢.
我改变了学习率，并尝试在最后一层使用“sigmoid”激活。我的期望是推断至少有80%的准确率。