資源簡介
基于殘差網絡的訓練模型,準確率可以達到99%,測試集有86%

代碼片段和文件信息
import?mxnet?as?mx
r?=?0?#?r?mean
g?=?0?#?g?mean
b?=?0?#?b?mean
r_2?=?0?#?r^2
g_2?=?0?#?g^2
b_2?=?0?#?b^2
total?=?0
import?os
img_path?=?“./image/train“
img_list?=?os.listdir(img_path)
for?img_name?in?img_list:
????img?=?mx.image.imread(img_path?+?“/“?+?img_name)?#?ndarray?width?x?height?x?3
????img?=?img.astype(‘float32‘)?/?255.
????total?+=?img.shape[0]?*?img.shape[1]
????r?+=?img[:?:?0].sum().asscalar()
????g?+=?img[:?:?1].sum().asscalar()
????b?+=?img[:?:?2].sum().asscalar()
????r_2?+=?(img[:?:?0]**2).sum().asscalar()
????g_2?+=?(img[:?:?1]**2).sum().asscalar()
????b_2?+=?(img[:?:?2]**2).sum().asscalar()
r_mean?=?r?/?total
g_mean?=?g?/?total
b_mean?=?b?/?total
r_var?=?r_2?/?total?-?r_mean?**?2
g_var?=?g_2?/?total?-?g_mean?**?2
b_var?=?b_2?/?total?-?b_mean?**?2
print(“[“r_mean““g_mean““b_mean“]“)
?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????文件????????3338??2019-01-15?04:09??resnet.py
?????文件????????1469??2019-01-18?22:34??test.py
?????文件????????4985??2019-01-18?21:45??train.py
?????文件?????????872??2019-01-15?02:46??avg_data.py
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