Resistor Ohm Value Estimation Challenge

需要将误差降低到1%以下

1、调整模型架构：增加模型的复杂性，例如增加卷积层或全连接层的数量，增加神经元数量等

# 在 ResistorEstimator 类的 CNN 方法中进行修改
def CNN(self, type):input = Input(shape=(64, 64, 3), name='input')h = self.conv2d(input, 32, (3, 3), padding=True, activation='relu', id=1)h = self.conv2d(h, 64, (3, 3), padding=True, activation='relu', id=2)# 添加更多卷积层...h = GlobalAveragePooling2D()(h)h = Dense(256, activation='relu', name='dense_1')(h)if type == 'cls':output = Dense(14, activation='softmax', name='output')elif type == 'reg':output = Dense(1, activation='linear', name='output')return Model(inputs=input, outputs=output)

2、学习率调整

model.cnn.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])

3、数据增强：调参

generator = ImageDataGenerator(rotation_range=45,width_shift_range=0.1,height_shift_range=0.1,zoom_range=[0.9, 1.1],horizontal_flip=True,vertical_flip=False,fill_mode='nearest')

4、增加训练时长：增加epochs的值

history = model.cnn.fit(train_ds, epochs=200, callbacks=[tb_cb], validation_data=valid_ds)

目前还在苦逼调参中。。