Python Alexnet val_acc为0，且val_损失在历次之后为0

我正试图根据我收集的数据训练Alexnet。 它包含转换为灰度的图像和关联的关键帧。 这是一个模拟自动驾驶汽车的程序

关键是：

w = [1,0,0]
a = [0,1,0]
d = [0,0,1]

这是我的密码

import numpy as np
from alexnet import alexnet

WIDTH = 100
HEIGHT = 80
LR = 1e-3
EPOCHS = 8
MODEL_NAME = 'Udacity Model Car NN'

model = alexnet(WIDTH,HEIGHT,LR)

train_data = np.load('data.npy',encoding="bytes")
train = train_data[:-200]
test = train_data[-200:]

X = np.array([i[0] for i in train]).reshape(-1,WIDTH,HEIGHT,1)
Y = [i[1] for i in train]

test_X = np.array([i[0] for i in test]).reshape(-1,WIDTH,HEIGHT,1)
test_Y = [i[1] for i in test]


model.fit({'input':X},{'targets':Y},n_epoch=EPOCHS,validation_set=({'input':test_X},{'targets:test_y'}),snapshot_step=500,show_metric=True,run_id=MODEL_NAME)


model.save(MODEL_NAME)

但在每个历元之后，验证精度保持为0，验证损失也保持为0

Training Step: 104  | total loss: 1.31713 | time: 119.279s| Momentum |epoch: 008 | loss: 1.31713 - acc: 0.3878 | val_loss: 0.00000 - val_acc: 0.0000 -- iter: 801/801

---

这可能是一个输入错误，请查看您作为验证传递的内容：

{'input':test_X},{'targets:test_y'}
\______________/ \_______________/
correct dict      this is a set with a string!    

虽然它应该是

{'input':test_X},{'targets':test_Y}