减少CNN训练时，测试集的内存占用

tools/TrainCNN/backward.py

@@ -5,6 +5,7 @@ from tqdm import tqdm
 import generate
 import forward
 import cv2
+import sys
 import numpy as np
 print("Finish!")
 
@@ -61,8 +62,6 @@ MOVING_AVERAGE_DECAY = 0.99
 
 
 def train(dataset, show_bar=False):
-    test_images, test_labels = dataset.all_test_sets()
-
     x = tf.placeholder(tf.float32, [None, generate.SRC_ROWS, generate.SRC_COLS, generate.SRC_CHANNELS])
     y_= tf.placeholder(tf.float32, [None, forward.OUTPUT_NODES])
     nodes, vars = forward.forward(x, 0.001)
@@ -104,15 +103,10 @@ def train(dataset, show_bar=False):
 
             if i % 100 == 0:
                 if i % 1000 == 0:
-                    acc = sess.run(accuracy, feed_dict={x: test_images, y_: test_labels})
+		    test_samples, test_labels = dataset.sample_test_sets(5000)
+                    acc = sess.run(accuracy, feed_dict={x: test_samples, y_: test_labels})
                 bar.set_postfix({"loss": loss_value, "acc": acc})
 
-
-            # if show_bar:
-            #     bar.title = "step: %d, loss: %f, acc: %f" % (step, loss_value, acc)
-            #     bar.cursor.restore()
-            #     bar.draw(value=i+1)
-
         # video = cv2.VideoCapture("/home/xinyang/Desktop/Video.mp4")
         # _ = True
         # while _:
@@ -148,11 +142,12 @@ def train(dataset, show_bar=False):
         #     res = sess.run(y, feed_dict={x: im})
         #     res = res.reshape([forward.OUTPUT_NODES])
         #     print(np.argmax(res))
-
+	
+	test_samples, test_labels = dataset.sample_test_sets(100)
         vars_val = sess.run(vars)
         save_para("/home/xinyang/Desktop/AutoAim/tools/para", vars_val)
-        nodes_val = sess.run(nodes, feed_dict={x:test_images})
-        return vars_val, nodes_val
+        nodes_val = sess.run(nodes, feed_dict={x:test_samples})
+        return vars_val, nodes_val, test_samples
 
 
 if __name__ == "__main__":
@@ -160,3 +155,4 @@ if __name__ == "__main__":
     dataset = generate.DataSet("/home/xinyang/Desktop/DataSets/box")
     print("Finish!")
     train(dataset, show_bar=True)
+    input("Press any key to end...")