请教一下，肌电的手势分类的应该用啥 CNN 模型？

我不知道用什么 CNN 分类器能很好预测值

16 个电极模拟的值，每列代表某个电极在人体表面读取的生物信号强度数值，等数据缩放完了，数据格式大概如下：

[[-0.00001221 0.00001285 0.00000142 -0.00000145 0.00000089 0.00000128 -0.00000432 -0.0000021 -0.00001083 -0.00000186 -0.000001 -0.00000192 0.00000232 0.00000245 -0.00000036 0.00000399] [-0.00001103 0.00001196 -0.00000081 -0.00000296 0.00000162 0.00000013 -0.00000231 -0.0000021 -0.00000726 -0.00000127 0.00000254 -0.00000042 0.00000163 0.00000758 -0.00000008 0.00000972] ... ] 

若使用 PIL 的 Image.fromarray(200 条 16 列的数值矩阵），输出为：

假若手打出 6 的手势，那么 16 个电极的一些电极贴近弯曲 3 个手指向后的手臂肌肉层能读取比较大的数值

可是，我用以下的代码训练几百轮，结果预测效果不太好,好几次预测，总是把实际值为 1 的手势预测输出 6

 def CNN(input_shape=(100,16,1), classes=9): X_input = Input(input_shape) X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='tanh', name='conv1')( X_input) X = MaxPooling2D(pool_size=(10, 1), strides=(10, 1), name='pool1')(X) X = Conv2D(filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same', activation='tanh', name='conv2')(X) X = MaxPooling2D(pool_size=(2, 1), strides=(2, 1), name='pool2')(X) X = Conv2D(filters=128, kernel_size=(3, 1), strides=(1, 1), padding='same', activation='tanh', name='conv3')(X) X = MaxPooling2D(pool_size=(2, 1), strides=(2, 1), name='pool3')(X) X = Flatten(name='flatten')(X) X = Dropout(0.5)(X) X = Dense(64, activation='tanh', name='fc1')(X) X = Dropout(0.4)(X) X = Dense(64, activation='tanh', name='fc2')(X) X = Dropout(0.3)(X) X = Dense(classes, activation='softmax')(X) model = Model(inputs=X_input, outputs=X, name='MultipleCNN') return model model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(X_train, Y_train, epochs=500, batch_size=64, verbose=1,validation_data=(X_test, Y_test), callbacks=callbacks_list) 

输出

...... Epoch 00999: val_accuracy did not improve from 0.82502 Epoch 1000/1000 138/138 [==============================] - 1s 8ms/step - loss: 0.0639 - accuracy: 0.9790 - val_loss: 1.0930 - val_accuracy: 0.8060 Epoch 01000: val_accuracy did not improve from 0.82502 276/276 [==============================] - 1s 3ms/step - loss: 0.0034 - accuracy: 0.9990 Train Loss = 0.003447444410994649 Train Accuracy = 0.9989800453186035 69/69 [==============================] - 0s 3ms/step - loss: 1.0930 - accuracy: 0.8060 Test Loss = 1.0929756164550781 Test Accuracy = 0.8059836626052856 time: 1228.2580664157867 

我迷糊了，测试集有 80%准确率，我实际测了十几下，怎么都是预测错的

兄弟萌，这种数据应该用啥模型的比较好？