计算速度优化，摄像头读取逻辑改变，分类器更新。

tools/TrainCNN/backward.py

@@ -1,6 +1,7 @@
 #!/usr/bin/python3
 print("Preparing...")
 import tensorflow as tf
+import os
 from tqdm import tqdm
 import generate
 import forward
@@ -35,28 +36,22 @@ def save_bias(fp, val):
         print(val[i], file=fp)
 
 
-def save_para(folder, paras):
-    with open(folder + "/conv1_w", "w") as fp:
-        save_kernal(fp, paras[0])
-    with open(folder + "/conv1_b", "w") as fp:
-        save_bias(fp, paras[1])
-    with open(folder + "/conv2_w", "w") as fp:
-        save_kernal(fp, paras[2])
-    with open(folder + "/conv2_b", "w") as fp:
-        save_bias(fp, paras[3])
-    with open(folder + "/fc1_w", "w") as fp:
-        save_weight_mat(fp, paras[4])
-    with open(folder + "/fc1_b", "w") as fp:
-        save_bias(fp, paras[5])
-    with open(folder + "/fc2_w", "w") as fp:
-        save_weight_mat(fp, paras[6])
-    with open(folder + "/fc2_b", "w") as fp:
-        save_bias(fp, paras[7])
+def save_para(folder, paras, names, info):
+    os.system("mkdir %s/%s" % (folder, info))
+    for para, name in zip(paras, names):
+        fp = open("%s/%s/%s" % (folder, info, name), "w")
+        if name[-1:] == "b":
+            save_bias(fp, para)
+        elif name[:2] == "fc":
+            save_weight_mat(fp, para)
+        elif name[:4] == "conv":
+            save_kernal(fp, para)
+        fp.close()
 
 
-STEPS = 60000
-BATCH = 50
-LEARNING_RATE_BASE  = 0.001
+STEPS = 50000
+BATCH = 30
+LEARNING_RATE_BASE  = 0.0005
 LEARNING_RATE_DECAY = 0.99
 MOVING_AVERAGE_DECAY = 0.99
 
@@ -65,7 +60,7 @@ def train(dataset, show_bar=False):
     x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
     y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
     keep_rate = tf.placeholder(tf.float32)
-    nodes, vars = forward.forward(x, 0.01)
+    nodes, vars, vars_name = forward.forward(x, 0.01)
     y = nodes[-1]
 
     ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
@@ -101,35 +96,38 @@ 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:0.2}
+                feed_dict={x: images_samples, y_: labels_samples, keep_rate:0.4}
             )
 
-            if (i-1) % 100 == 0:
-                if (i-1) % 500 == 0:
-                    test_images, test_labels = dataset.sample_test_sets(5000)
-                    test_acc, output = sess.run([accuracy, y], feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
-                    output = np.argmax(output, axis=1)
-                    real = np.argmax(test_labels, axis=1)
-                    print("=============test-set===============")
-                    for n in range(forward.OUTPUT_NODES):
-                        print("label: %d, precise: %f, recall: %f" %
-                            (n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
-
-                    train_images, train_labels = dataset.sample_train_sets(5000)
-                    train_acc, output = sess.run([accuracy, y], feed_dict={x: train_images, y_: train_labels, keep_rate:1.0})
-                    output = np.argmax(output, axis=1)
-                    real = np.argmax(train_labels, axis=1)
-                    print("=============train-set===============")
-                    for n in range(forward.OUTPUT_NODES):
-                        print("label: %d, precise: %f, recall: %f" %
-                              (n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
-                    print("\n")
+            if step % 500 == 0:
+                test_images, test_labels = dataset.sample_test_sets(6000)
+                test_acc, output = sess.run([accuracy, y], feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
+                output = np.argmax(output, axis=1)
+                real = np.argmax(test_labels, axis=1)
+                print("=============test-set===============")
+                for n in range(forward.OUTPUT_NODES):
+                    print("label: %d, precise: %f, recall: %f" %
+                        (n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
 
+                train_images, train_labels = dataset.sample_train_sets(6000)
+                train_acc, output = sess.run([accuracy, y], feed_dict={x: train_images, y_: train_labels, keep_rate:1.0})
+                output = np.argmax(output, axis=1)
+                real = np.argmax(train_labels, axis=1)
+                print("=============train-set===============")
+                for n in range(forward.OUTPUT_NODES):
+                    print("label: %d, precise: %f, recall: %f" %
+                          (n, np.mean(real[output==n]==n), np.mean(output[real==n]==n)))
+                print("\n")
+                if train_acc >= 0.99 and test_acc >= 0.99:
+                    vars_val = sess.run(vars)
+                    save_para(
+                        "model",
+                        vars_val,
+                        vars_name,
+                        "steps:%d-train_acc:%f-test_acc:%f" % (step, train_acc, test_acc)
+                    )
                 bar.set_postfix({"loss": loss_value, "train_acc": train_acc, "test_acc": test_acc})
-
-        vars_val = sess.run(vars)
-        save_para("/home/xinyang/Workspace/RM_auto-aim/tools/para", vars_val)
-        print("save done!")
+        # print("save done!")
 
         # pred = sess.run(y, feed_dict={x: test_images, keep_rate:1.0})
 

tools/TrainCNN/forward.py

@@ -29,16 +29,22 @@ def max_pool_2x2(x):
 CONV1_KERNAL_SIZE = 5
 
 # 第一层卷积输出通道数
-CONV1_OUTPUT_CHANNELS = 8
+CONV1_OUTPUT_CHANNELS = 4
 
 # 第二层卷积核大小
 CONV2_KERNAL_SIZE = 3
 
 # 第二层卷积输出通道数
-CONV2_OUTPUT_CHANNELS = 16
+CONV2_OUTPUT_CHANNELS = 6
+
+# 第三层卷积核大小
+CONV3_KERNAL_SIZE = 3
+
+# 第三层卷积输出通道数
+CONV3_OUTPUT_CHANNELS = 8
 
 # 第一层全连接宽度
-FC1_OUTPUT_NODES = 100
+FC1_OUTPUT_NODES = 50
 
 # 第二层全连接宽度（输出标签类型数）
 FC2_OUTPUT_NODES = 15
@@ -49,6 +55,7 @@ OUTPUT_NODES = FC2_OUTPUT_NODES
 
 def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
     vars = []
+    vars_name = []
     nodes = []
 
     conv1_w = get_weight(
@@ -57,7 +64,10 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
     conv1_b = get_bias([CONV1_OUTPUT_CHANNELS])
     conv1   = tf.nn.relu(tf.nn.bias_add(conv2d(x, conv1_w), conv1_b))
     pool1   = avg_pool_2x2(conv1)
+    print("conv1: ", conv1.shape)
+    print("pool1: ", pool1.shape)
     vars.extend([conv1_w, conv1_b])
+    vars_name.extend(["conv1_w", "conv1_b"])
     nodes.extend([conv1, pool1])
 
     conv2_w = get_weight(
@@ -66,27 +76,41 @@ def forward(x, regularizer=None, keep_rate=tf.constant(1.0)):
     conv2_b = get_bias([CONV2_OUTPUT_CHANNELS])
     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])
 
-    pool_shape = pool2.get_shape().as_list()
-    node = pool_shape[1] * pool_shape[2] * pool_shape[3]
-    reshaped = tf.reshape(pool2, [-1, node])
+    conv3_w = get_weight(
+        [CONV3_KERNAL_SIZE, CONV3_KERNAL_SIZE, CONV2_OUTPUT_CHANNELS, CONV3_OUTPUT_CHANNELS]
+    )
+    conv3_b = get_bias([CONV3_OUTPUT_CHANNELS])
+    conv3 = tf.nn.relu(tf.nn.bias_add(conv2d(pool2, conv3_w), conv3_b))
+    print("conv3: ", conv3.shape)
+    vars.extend([conv3_w, conv3_b])
+    vars_name.extend(["conv3_w", "conv3_b"])
+    nodes.extend([conv3])
+
+    conv_shape = conv3.get_shape().as_list()
+    node = conv_shape[1] * conv_shape[2] * conv_shape[3]
+    reshaped = tf.reshape(conv3, [-1, node])
     reshaped = tf.nn.dropout(reshaped, keep_rate)
+    print("reshaped: ", reshaped.shape)
 
     fc1_w = get_weight([node, FC1_OUTPUT_NODES], regularizer)
     fc1_b = get_bias([FC1_OUTPUT_NODES])
     fc1   = tf.nn.relu(tf.matmul(reshaped, fc1_w) + fc1_b)
-    fc1   = tf.nn.dropout(fc1, keep_rate)
     vars.extend([fc1_w, fc1_b])
+    vars_name.extend(["fc1_w", "fc1_b"])
     nodes.extend([fc1])
 
     fc2_w = get_weight([FC1_OUTPUT_NODES, FC2_OUTPUT_NODES], regularizer)
     fc2_b = get_bias([FC2_OUTPUT_NODES])
-#    fc2   = tf.nn.softmax(tf.matmul(fc1, fc2_w) + fc2_b)
-    fc2   = tf.matmul(fc1, fc2_w) + fc2_b
+    fc2 = tf.matmul(fc1, fc2_w) + fc2_b
     vars.extend([fc2_w, fc2_b])
+    vars_name.extend(["fc2_w", "fc2_b"])
     nodes.extend([fc2])
 
-    return nodes, vars
+    return nodes, vars, vars_name
 

tools/para/conv1_b

@@ -1,9 +1,5 @@
-8
--0.19897088
-2.2773967
--0.07212669
--0.22893764
--0.022769619
-0.9122422
-1.3221853
--0.21709026
+4
+0.30789205
+-0.043525748
+0.2050164
+0.74044687

tools/para/conv1_w

@@ -1,604 +1,304 @@
 3
-8
+4
 5
 5
--0.42449534
--0.14145629
-0.12018829
-0.4759054
-0.54840434
-0.28412497
-0.40357414
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-0.7217546
-0.77334726
-0.18300368
-0.3455078
-0.270947
-0.3452664
-0.3419373
--0.07475002
--0.23191033
--0.5056218
--0.5653023
--0.49549124
--0.356733
--0.70811594
--0.6640918
--0.5230208
--0.5567686
--0.34954152
--0.49958408
--0.31039435
--0.55455387
--0.3291702
--0.0030061502
--0.13863257
--0.40160847
--0.53651094
--0.24113898
--0.1733842
--0.2476929
--0.5121031
--0.47203135
--0.43864155
--0.058570273
--0.33658004
--0.6619966
--0.73419136
--0.7045188
-0.32072416
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--0.90990585
--0.6184357
--0.1404704
-0.20029725
-0.23383331
--0.5078964
-0.07829093
-0.41757903
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--0.21864063
-0.07738885
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--0.17151009
-0.07808888
--0.014565518
-0.27556416
-0.21755305
--0.42132428
--0.4472937
--0.686964
--0.6123315
--0.36468038
-0.39503688
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--0.6651138
--0.6533556
--0.8055196
--0.70568013
--0.59960866
--0.38753736
--0.75367606
--0.6284249
--0.73716867
--0.4809728
--0.43523777
--0.5271899
--0.6199644
--0.6201511
--0.58342254
--0.41127473
--0.54275876
--0.53538835
--0.56108046
--0.48543084
--0.74164164
--0.64662766
--0.856863
--0.6305302
--0.5739134
-0.95437706
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-0.7689269
-0.5900862
-0.6000688
--0.8985509
--0.5367204
--0.17939755
-0.1239284
-0.31574073
--0.29208726
-0.11087964
-0.13841438
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-0.1677956
-0.5068273
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-0.46504426
-0.56336105
-0.007133658
-0.056750543
-0.083241865
-0.06789419
-0.14583983
--0.6158809
--0.87346137
--0.80676347
--0.70183337
--0.6015298
-1.5393821
-2.1624126
-2.5511134
-2.7846441
-2.368676
-2.4525952
-3.2989857
-4.029959
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-1.5281351
-1.5740242
-0.9623409
-0.98793215
-1.1669405
-1.1152183
-1.2492605
--0.15674886
--0.038593877
--0.17274536
-0.09258732
-0.17494603
-0.105809644
-0.31649294
-0.343589
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-1.3859847
-2.0778801
-2.148741
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-3.0291762
-3.4696164
-3.2318525
-2.4367645
-2.8905299
-3.4577188
-3.8412285
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-2.7076635
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-3.2963896
-2.8063552
-1.9206675
-1.9905787
-2.0144224
-1.754742
-1.4237579
-0.83947563
-0.2254852
-0.03469266
--0.05631122
-0.04464834
--0.2178017
-0.13702668
-0.16909239
-0.26976383
-0.068580456
-0.12111518
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-0.094496034
-0.3766073
-0.46016395
-0.40080497
-0.3402491
-0.21284883
-0.13231997
--0.013396452
--0.083948776
-0.012691039
-0.11314143
-0.09580059
-0.2377315
-0.41866076
-0.15490921
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-0.39781362
-0.23634791
-0.122939676
-0.24806741
-0.13085991
-0.16097488
--0.020796934
-0.17553945
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-0.76530534
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--0.064461164
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--0.17301485
--0.1755835
--0.46472925
--0.32503256
--0.30224133
--0.49536815
--0.5347998
--0.62774706
--0.52409613
--0.46520838
--0.47201794
--0.62468505
-1.2727304
-1.8184105
-2.2249503
-2.4140065
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-3.196057
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-1.9101305
-1.2425265
--0.03470229
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-0.76658946
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--0.86873436
--0.6494321
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--0.086757496
--0.061029676
--0.79375947
--0.47139648
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--0.54239106
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--0.43658426
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--0.34542954
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-3.365891
-2.4578235
-2.5486002
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-2.4205306
-3.1097507
-3.152673
-2.882059
-1.976877
-1.6673613
-1.9114503
-1.7297668
-1.4748011
-0.6597555
--0.42657572
--0.52120584
--0.68196577
--0.69425035
--0.9403177
--0.30822635
--0.46618125
--0.5267847
--0.493324
--0.65498495
--0.23576419
--0.16038808
--0.16629791
--0.3212767
--0.59757406
--0.004436309
--0.00716624
--0.11501519
--0.18837191
--0.39238173
-0.050632488
-0.14269532
--0.121495865
--0.26217306
--0.37235448
-0.6217619
-0.75724506
-0.81917787
-0.82933223
-0.8388242
-0.8166705
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-0.9423948
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-1.1399423
-0.96763176
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