Merge branch 'master' of https://github.com/lloi7/RM_auto-aim

others/include/log.h

@@ -6,6 +6,7 @@
 #define _LOG_H_
 
 #include <stdio.h>
+#include <systime.h>
 
 /************** Define the control code *************/
 #define START_CTR           "\033[0"
@@ -130,28 +131,22 @@
 
 /******************** the time counter API ************************/
 #if !defined(DO_NOT_CNT_TIME) && LOG_LEVEL > LOG_NONE
-	#ifdef Windows
-		#include <Windows.h>
     #define CNT_TIME(tag, codes, ...) do{ \
-			static SYSTEMTIME ts, te; \
-			GetLocalTime(&ts); \
+        class TimeCounter{ \
+        private: \
+            systime begin; \
+        public: \
+            TimeCounter(){ \
+                getsystime(begin); \
+            } \
+            ~TimeCounter(){ \
+                systime end; \
+                getsystime(end); \
+                LOGM(tag": %.1lfms", ##__VA_ARGS__, getTimeIntervalms(end, begin)); \
+            } \
+        } time_cnt; \
         codes; \
-			GetLocalTime(&te); \
-			LOGM(tag": %dms", (te.wSecond-ts.wSecond)*1000+(te.wMilliseconds-ts.wMilliseconds)); \
     }while (0)
-	#elif defined(Linux)
-		#include <sys/time.h>
-		#define CNT_TIME(tag, codes, ...) do{ \
-			static timeval ts, te; \
-			gettimeofday(&ts, NULL); \
-			codes; \
-			gettimeofday(&te, NULL); \
-			LOGM(tag": %.1lfms", ##__VA_ARGS__, (te.tv_sec-ts.tv_sec)*1000.0+(te.tv_usec-ts.tv_usec)/1000.0); \
-		}while (0)
-    #else
-        #warning "Unsupport plantform for CNT_TIME"
-        #define CNT_TIME(tag, codes, ...) codes
-    #endif
 #else
 	#define CNT_TIME(tag, codes, ...) codes
 #endif

tools/TrainCNN/backward.py

@@ -52,8 +52,8 @@ def save_para(folder, paras, names, info):
 
 
 STEPS = 100000
-BATCH = 40
-LEARNING_RATE_BASE = 0.0002
+BATCH = 50
+LEARNING_RATE_BASE = 0.0005
 LEARNING_RATE_DECAY = 0.99
 MOVING_AVERAGE_DECAY = 0.99
 
@@ -98,7 +98,7 @@ def train(dataset, show_bar=False):
 
             _, loss_value, step = sess.run(
                 [train_op, loss, global_step],
-                feed_dict={x: images_samples, y_: labels_samples, keep_rate: 0.3}
+                feed_dict={x: images_samples, y_: labels_samples, keep_rate: 0.4}
             )
 
             if step % 500 == 0:

tools/TrainCNN/forward.py

@@ -41,7 +41,7 @@ CONV2_OUTPUT_CHANNELS = 8
 CONV3_KERNAL_SIZE = 3
 
 # 第三层卷积输出通道数
-CONV3_OUTPUT_CHANNELS = 16
+CONV3_OUTPUT_CHANNELS = 12
 
 # 第一层全连接宽度
 FC1_OUTPUT_NODES = 60
@@ -77,7 +77,6 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
     conv2 = tf.nn.relu(tf.nn.bias_add(conv2d(pool1, conv2_w), conv2_b))
     pool2 = avg_pool_2x2(conv2)
     print("conv2: ", conv2.shape)
-    print("pool2: ", pool2.shape)
     vars.extend([conv2_w, conv2_b])
     vars_name.extend(["conv2_w", "conv2_b"])
     nodes.extend([conv2, pool2])

tools/para/conv1_b

@@ -1,5 +1,5 @@
 4
-0.021338634
--0.28292254
-0.39307028
-0.46493095
+1.0091771
+0.75360304
+0.32836446
+0.27722517

tools/para/conv1_w

@@ -2,303 +2,303 @@
 4
 5
 5
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--0.051313818
-0.3746486
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--0.012611773
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tools/para/conv2_b

@@ -1,9 +1,9 @@
 8
--0.552405
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+-0.49552673
+0.756849

tools/para/conv2_w

@@ -2,291 +2,291 @@
 8
 3
 3
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