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更新ＣＮＮ参数

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xinyang
2019-07-25 15:06:23 +08:00
parent 453670308c
commit 504d1aca86
10 changed files with 115426 additions and 26831 deletions
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37
tools/TrainCNN/backward.py
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@@ -54,7 +54,7 @@ def save_para(folder, paras):
save_bias(fp, paras[7])
STEPS = 100000
STEPS = 60000
BATCH = 50
LEARNING_RATE_BASE = 0.001
LEARNING_RATE_DECAY = 0.99
@@ -101,16 +101,31 @@ 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:0.2}
)
if i % 100 == 0:
if i % 500 == 0:
test_images, test_labels = dataset.sample_test_sets(10000)
acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels, keep_rate:1.0})
bar.set_postfix({"loss": loss_value, "acc": acc})
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")
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)
@@ -206,9 +221,9 @@ def train(dataset, show_bar=False):
if __name__ == "__main__":
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
dataset = generate.DataSet("/home/xinyang/Workspace/box_cut")
# import os
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
dataset = generate.DataSet("/home/xinyang/Workspace/box_resize")
train(dataset, show_bar=True)
input("press enter to continue...")

6
tools/TrainCNN/forward.py
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@@ -29,16 +29,16 @@ def max_pool_2x2(x):
CONV1_KERNAL_SIZE = 5
# 第一层卷积输出通道数
CONV1_OUTPUT_CHANNELS = 6
CONV1_OUTPUT_CHANNELS = 8
# 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 = 12
CONV2_OUTPUT_CHANNELS = 16
# 第一层全连接宽度
FC1_OUTPUT_NODES = 30
FC1_OUTPUT_NODES = 100
# 第二层全连接宽度（输出标签类型数）
FC2_OUTPUT_NODES = 15

16
tools/para/conv1_b
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@@ -1,7 +1,9 @@
6
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8
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2.2773967
-0.07212669
-0.22893764
-0.022769619
0.9122422
1.3221853
-0.21709026

1052
tools/para/conv1_w
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30
tools/para/conv2_b
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@@ -1,13 +1,17 @@
12
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-0.00558426
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16
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-0.027841559
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-0.09058099
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0.25698876
0.076515704
-0.017468728

1804
tools/para/conv2_w
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132
tools/para/fc1_b
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@@ -1,31 +1,101 @@
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tools/para/fc1_w
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30
tools/para/fc2_b
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@@ -1,16 +1,16 @@
15
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0.30222887

1952
tools/para/fc2_w
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