我正在训练我的网络10个纪元，但从来没有增加或减少验证准确性，这始终是相同的结果。 损失在变化，但准确性没有变化。
网络是PoseNet：https://github.com/alleboudy/DLinCV/blob/master/scripts/keras-posenet/scripts/posenet.py
我只是使用最后的输出（XYZ和WPQR预测）抑制中间输出。
使用GTX 1080，一切正常，但是使用RTX 2070却无法正常工作。
数据集：3万张图片*
时期：10（或更多，但什么也没有发生）
批量：100
优化器：亚当
损失功能：MSE
def create_posenet(weights_path=None):
## POSENET
with tf.device('/device:GPU:0'):
inputs = Input((config.IMG_HEIGHT, config.IMG_WIDTH, config.IMG_CHANNELS))
init = Conv2D(64,(7,7),padding='same',activation='relu',name='init/conv1')(inputs)
init = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='valid',name='init/max1')(init)
init = BatchNormalization(name='init/norm1')(init)
init = Conv2D(64, (1,1),padding='same',activation='relu',name='init/conv2_1')(init)
init = Conv2D(192,(3,3),padding='same',activation='relu',name='init/conv2_3')(init)
init = BatchNormalization(name='init/nomr2')(init)
init_out = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='valid',name='init/3x3')(init)
#inception 1
icp_1_1x1 = Conv2D(64, (1,1),padding='same',activation='relu',name='icp_1/1x1')(init_out)
icp_1_3x3_reduce = Conv2D(96, (1,1),padding='same',activation='relu',name='icp_1/3x3_reduce')(init_out)
icp_1_3x3 = Conv2D(128,(3,3),padding='same',activation='relu',name='icp_1/3x3')(icp_1_3x3_reduce)
icp_1_5x5_reduce = Conv2D(16, (1,1),padding='same',activation='relu',name='icp_1/5x5_reduce')(init_out)
icp_1_5x5 = Conv2D(32, (5,5),padding='same',activation='relu',name='icp_1/5x5')(icp_1_3x3_reduce)
icp_1_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_1/pool')(init_out)
icp_1_pool_proj = Conv2D(32, (1,1),padding='same',activation='relu',name='icp_1/pool_proj')(icp_1_pool)
icp_1_output = concatenate([icp_1_1x1,icp_1_3x3,icp_1_5x5,icp_1_pool_proj],name='icp_1/output')
#inception 2
icp_2_1x1 = Conv2D(128,(1,1),padding='same',activation='relu',name='icp_2/1x1')(icp_1_output)
icp_2_3x3_reduce = Conv2D(128,(1,1),padding='same',activation='relu',name='icp_2/3x3_reduce')(icp_1_output)
icp_2_3x3 = Conv2D(192,(3,3),padding='same',activation='relu',name='icp_2/3x3')(icp_2_3x3_reduce)
icp_2_5x5_reduce = Conv2D(32, (1,1),padding='same',activation='relu',name='icp_2/5x5_reduce')(icp_1_output)
icp_2_5x5 = Conv2D(96, (5,5),padding='same',activation='relu',name='icp_2/5x5')(icp_2_5x5_reduce)
icp_2_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_2/pool')(icp_1_output)
icp_2_pool_proj = Conv2D(64, (1,1),padding='same',activation='relu',name='icp_2/pool_proj')(icp_2_pool)
icp_2_output = concatenate([icp_2_1x1,icp_2_3x3,icp_2_5x5,icp_2_pool_proj],name='icp_2/output')
pool2_3x3_icp3 = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='valid',name='pool3/3x3_s2')(icp_2_output)
#inception 3
icp_3_1x1 = Conv2D(192,(1,1),padding='same',activation='relu',name='icp_3/1x1')(pool2_3x3_icp3)
icp_3_3x3_reduce = Conv2D(96, (1,1),padding='same',activation='relu',name='icp_3/3x3_reduce')(pool2_3x3_icp3)
icp_3_3x3 = Conv2D(208,(3,3),padding='same',activation='relu',name='icp_3/3x3')(icp_3_3x3_reduce)
icp_3_5x5_reduce = Conv2D(16, (1,1),padding='same',activation='relu',name='icp_3/5x5_reduce')(pool2_3x3_icp3)
icp_3_5x5 = Conv2D(48, (5,5),padding='same',activation='relu',name='icp_3/5x5')(icp_3_5x5_reduce)
icp_3_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_3/pool')(pool2_3x3_icp3)
icp_3_pool_proj = Conv2D(64, (1,1),padding='same',activation='relu',name='icp_3/pool_proj')(icp_3_pool)
icp_3_output = concatenate([icp_3_1x1,icp_3_3x3,icp_3_5x5,icp_3_pool_proj],name='icp_3/output')
#inception 4
icp_4_1x1 = Conv2D(160,(1,1),padding='same',activation='relu',name='icp_4/1x1')(icp_3_output)
icp_4_3x3_reduce = Conv2D(112,(1,1),padding='same',activation='relu',name='icp_4/3x3_reduce')(icp_3_output)
icp_4_3x3 = Conv2D(224,(3,3),padding='same',activation='relu',name='icp_4/3x3')(icp_4_3x3_reduce)
icp_4_5x5_reduce = Conv2D(24, (1,1),padding='same',activation='relu',name='icp_4/5x5_reduce')(icp_3_output)
icp_4_5x5 = Conv2D(64, (5,5),padding='same',activation='relu',name='icp_4/5x5')(icp_4_5x5_reduce)
icp_4_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_4/pool')(icp_3_output)
icp_4_pool_proj = Conv2D(64, (1,1),padding='same',activation='relu',name='icp_4/pool_proj')(icp_4_pool)
icp_4_output = concatenate([icp_4_1x1,icp_4_3x3,icp_4_5x5,icp_4_pool_proj],name='icp_4_output')
#inception 5
icp_5_1x1 = Conv2D(128,(1,1),padding='same',activation='relu',name='icp_5/1x1')(icp_4_output)
icp_5_3x3_reduce = Conv2D(128,(1,1),padding='same',activation='relu',name='icp_5/3x3_reduce')(icp_4_output)
icp_5_3x3 = Conv2D(256,(3,3),padding='same',activation='relu',name='icp_5/3x3')(icp_5_3x3_reduce)
icp_5_5x5_reduce = Conv2D(24, (1,1),padding='same',activation='relu',name='icp_5/5x5_reduce')(icp_4_output)
icp_5_5x5 = Conv2D(64, (5,5),padding='same',activation='relu',name='icp_5/5x5')(icp_5_5x5_reduce)
icp_5_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_5/pool')(icp_4_output)
icp_5_pool_proj = Conv2D(64, (1,1),padding='same',activation='relu',name='icp_5/pool_proj')(icp_5_pool)
icp_5_output = concatenate([icp_5_1x1,icp_5_3x3,icp_5_5x5,icp_5_pool_proj],name='icp_5/output')
#inception 6
icp_6_1x1 = Conv2D(112,(1,1),padding='same',activation='relu',name='icp_6/1x1')(icp_5_output)
icp_6_3x3_reduce = Conv2D(144,(1,1),padding='same',activation='relu',name='icp_6/3x3_reduce')(icp_5_output)
icp_6_3x3 = Conv2D(288,(3,3),padding='same',activation='relu',name='icp_6/3x3')(icp_6_3x3_reduce)
icp_6_5x5_reduce = Conv2D(32, (1,1),padding='same',activation='relu',name='icp_6/5x5_reduce')(icp_5_output)
icp_6_5x5 = Conv2D(64, (5,5),padding='same',activation='relu',name='icp_6/5x5')(icp_6_5x5_reduce)
icp_6_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_6/pool')(icp_5_output)
icp_6_pool_proj = Conv2D(64, (1,1),padding='same',activation='relu',name='icp_6/pool_proj')(icp_6_pool)
icp_6_output = concatenate([icp_6_1x1,icp_6_3x3,icp_6_5x5,icp_6_pool_proj],name='icp_6/output')
#inception 7
icp_7_1x1 = Conv2D(256,(1,1),padding='same',activation='relu',name='icp_7/1x1')(icp_6_output)
icp_7_3x3_reduce = Conv2D(160,(1,1),padding='same',activation='relu',name='icp_7/3x3_reduce')(icp_6_output)
icp_7_3x3 = Conv2D(320,(3,3),padding='same',activation='relu',name='icp_7/3x3')(icp_7_3x3_reduce)
icp_7_5x5_reduce = Conv2D(32, (1,1),padding='same',activation='relu',name='icp_7/5x5_reduce')(icp_6_output)
icp_7_5x5 = Conv2D(128,(5,5),padding='same',activation='relu',name='icp_7/5x5')(icp_7_5x5_reduce)
icp_7_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_7/pool')(icp_6_output)
icp_7_pool_proj = Conv2D(128,(1,1),padding='same',activation='relu',name='icp_7/pool_proj')(icp_7_pool)
icp_7_output = concatenate([icp_7_1x1,icp_7_3x3,icp_7_5x5,icp_7_pool_proj],name='icp_7/output')
pool3_3x3_icp8 = MaxPooling2D(pool_size=(3,3),strides=(2,2),padding='valid',name='pool4/3x3_s2')(icp_7_output)
#inception 8
icp_8_1x1 = Conv2D(256,(1,1),padding='same',activation='relu',name='icp_8/1x1')(pool3_3x3_icp8)
icp_8_3x3_reduce = Conv2D(160,(1,1),padding='same',activation='relu',name='icp_8/3x3_reduce')(pool3_3x3_icp8)
icp_8_3x3 = Conv2D(320,(3,3),padding='same',activation='relu',name='icp_8/3x3')(icp_8_3x3_reduce)
icp_8_5x5_reduce = Conv2D(32, (1,1),padding='same',activation='relu',name='icp_8/5x5_reduce')(pool3_3x3_icp8)
icp_8_5x5 = Conv2D(128,(5,5),padding='same',activation='relu',name='icp_8/5x5')(icp_8_5x5_reduce)
icp_8_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_8/pool')(pool3_3x3_icp8)
icp_8_pool_proj = Conv2D(128,(1,1),padding='same',activation='relu',name='icp_8/pool_proj')(icp_8_pool)
icp_8_output = concatenate([icp_8_1x1,icp_8_3x3,icp_8_5x5,icp_8_pool_proj],name='icp_8/output')
#inception 9
icp_9_1x1 = Conv2D(384,(1,1),padding='same',activation='relu',name='icp_9/1x1')(icp_8_output)
icp_9_3x3_reduce = Conv2D(192,(1,1),padding='same',activation='relu',name='icp_9/3x3_reduce')(icp_8_output)
icp_9_3x3 = Conv2D(384,(3,3),padding='same',activation='relu',name='icp_9/3x3')(icp_9_3x3_reduce)
icp_9_5x5_reduce = Conv2D(48, (1,1),padding='same',activation='relu',name='icp_9/5x5_reduce')(icp_8_output)
icp_9_5x5 = Conv2D(128,(5,5),padding='same',activation='relu',name='icp_9/5x5')(icp_9_5x5_reduce)
icp_9_pool = MaxPooling2D(pool_size=(3,3),strides=(1,1),padding='same',name='icp_9/pool')(icp_8_output)
icp_9_pool_proj = Conv2D(128,(1,1),padding='same',activation='relu',name='icp_9/pool_proj')(icp_9_pool)
icp_9_output = concatenate([icp_9_1x1,icp_9_3x3,icp_9_5x5,icp_9_pool_proj],name='icp_9/output')
# --- classification 3 --- #
cls_pose = AveragePooling2D(pool_size=(2,2),strides=(1,1),name='pool5/7x7_s2')(icp_9_output)
cls_pose = Flatten()(cls_pose)
cls_pose = Dropout(0.5)(cls_pose)
cls_pose_xyz = Dense(3,name='cls3_fc_pose_xyz')(cls_pose)
cls_pose_wpqr = Dense(4,name='cls3_fc_pose_wpqr')(cls_pose)
model_posenet = Model(inputs=[inputs], outputs=[cls_pose_xyz, cls_pose_wpqr])
model_posenet.compile(optimizer = 'adam',
loss = ['mse',
'mse'],
metrics = ['accuracy'])
return model_posenet
XYZ预测的输出未更改：
史诗1/10 20165/20165 [==============================]-61s 3ms / step-损失：1.0996-cls3_fc_pose_xyz_loss：0.3909-cls3_fc_pose_wpqr_loss ：0.7087-cls3_fc_pose_xyz_acc：0.9814-cls3_fc_pose_wpqr_acc：0.8663-val_loss：1.5001-val_cls3_fc_pose_xyz_loss：0.5134-val_cls3_fc_pose_w_p_r_c_c_c_c_c_c_c_c_c_f_c_c_f_c_c_c_c_c_c_c_f_c_c_c_c_c_c_c_c_c_c_f_c_c_c_f_c_c_c_c_f_c_c_c_c_f_c_c_f_c_c_f_c_c_f_c_c_c_c_f_c_c_c_c_f_c_c_c_c_f_c_c_f_c_f_c_c_f_c_c_c_f_c_c_p_f_r_loss：0.9867-val_cl_acc_c_c_c_p_r_loss：0.9867 -val
史诗2/10 20165/20165 [==============================]-54s 3ms / step-损失：0.2953-cls3_fc_pose_xyz_loss：0.1026-cls3_fc_pose_wpqr_loss ：0.1927-cls3_fc_pose_xyz_acc：0.9819-cls3_fc_pose_wpqr_acc：0.8858-val_loss：1.2819-val_cls3_fc_pose_xyz_loss：0.4634-val_cls3_fc_pose_w__p_r_c_f_c_c_f_c_c_f_c_f_c_f_c_c_f x
史诗3/10
20165/20165 [==============================]-54s 3ms / step-损失：0.1802-cls3_fc_pose_xyz_loss：0.0627-cls3_fc_pose_wpqr_loss ：0.1175-cls3_fc_pose_xyz_acc：0.9838-cls3_fc_pose_wpqr_acc：0.9086-val_loss：1.2151-val_cls3_fc_pose_xyz_loss：0.3737-val_cls3_fc_pose_w_p_r_c_f_val_c_f_c_f_c_f_c_f_c_f_c_f_c_f_c：0 c_f_c_c_f_c：0 c_f_c_f_c_y_c：_c _strong：0.8414-
史诗4/10
20165/20165 [==============================]-54s 3ms / step-损失：0.1470-cls3_fc_pose_xyz_loss：0.0543-cls3_fc_pose_wpqr_loss ：0.0927-cls3_fc_pose_xyz_acc：0.9854-cls3_fc_pose_wpqr_acc：0.9183-val_loss：1.2588-val_cls3_fc_pose_xyz_loss：0.3980-val_cls3_fc_pose_w__p_r_c_c_f_c_c_f_c_c_f_c_f_c_c_f_c_c_f x
史诗5/10 20165/20165 [==============================]-54s 3ms / step-损失：0.1268-cls3_fc_pose_xyz_loss：0.0465-cls3_fc_pose_wpqr_loss ：0.0804-cls3_fc_pose_xyz_acc：0.9875-cls3_fc_pose_wpqr_acc：0.9288-val_loss：1.2391-val_cls3_fc_pose_xyz_loss：0.4460-val_cls3_fc_pose_w__p_r_c_c_f_c_c_f_c_c_f x f x y>
史诗6/10 20165/20165 [==============================]-54s 3ms / step-损失：0.1196-cls3_fc_pose_xyz_loss：0.0437-cls3_fc_pose_wpqr_loss ：0.0759-cls3_fc_pose_xyz_acc：0.9886-cls3_fc_pose_wpqr_acc：0.9309-val_loss：1.1939-val_cls3_fc_pose_xyz_loss：0.3995-val_cls3_fc_pose_w__p_r_c_c_f_c_c_f_c_c_f_c_x_c_f x
史诗7/10 20165/20165 [==============================]-54s 3ms / step-损失：0.1133-cls3_fc_pose_xyz_loss：0.0424-cls3_fc_pose_wpqr_loss ：0.0709-cls3_fc_pose_xyz_acc：0.9901-cls3_fc_pose_wpqr_acc：0.9333-val_loss：1.1904-val_cls3_fc_pose_xyz_loss：0.4390-val_cls3_fc_pose_w__p_r_s_c_f_c_c_f_c_c_f_c_x_c_f x
史诗8/10 20165/20165 [==============================]-54s 3ms / step-损失：0.1030-cls3_fc_pose_xyz_loss：0.0394-cls3_fc_pose_wpqr_loss ：0.0636-cls3_fc_pose_xyz_acc：0.9910-cls3_fc_pose_wpqr_acc：0.9382-val_loss：1.2242-val_cls3_fc_pose_xyz_loss：0.4447-val_cls3_fc_pose_w__p_r_c_s_f_c_f_c_c_f_c_c_f_c_f_c_c_f x
史诗9/10 20165/20165 [==============================]-54s 3ms / step-损失：0.1056-cls3_fc_pose_xyz_loss：0.0394-cls3_fc_pose_wpqr_loss ：0.0661-cls3_fc_pose_xyz_acc：0.9912-cls3_fc_pose_wpqr_acc：0.9395-val_loss：1.3230-val_cls3_fc_pose_xyz_loss：0.4622-val_cls3_fc_pose_w__p_r_c_s_f_c_c_f_c_c_f_c_c_f_c_c_c_f x
Epoch 10/10 20165/20165 [==============================]-54s 3ms / step-损失：0.0995-cls3_fc_pose_xyz_loss：0.0369-cls3_fc_pose_wpqr_loss ：0.0626-cls3_fc_pose_xyz_acc：0.9912-cls3_fc_pose_wpqr_acc：0.9421-val_loss：1.2388-val_cls3_fc_pose_xyz_loss：0.4127-val_cls3_fc_pose_w__p_r_c_f_c_c_f_c_c_f x