fix bug

tools/TrainCNN/backward.py

@@ -70,8 +70,8 @@ def train(dataset, show_bar=False):
     nodes, vars = forward.forward(x, 0.01)
     y = nodes[-1]
 
-#    ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
-    ce  = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
+    ce  = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1))
+#    ce  = tf.nn.weighted_cross_entropy_with_logits(logits=y, labels=tf.argmax(y_, 1), pos_weight=1)
     cem = tf.reduce_mean(ce)
     loss= cem + tf.add_n(tf.get_collection("losses"))
 
@@ -114,7 +114,7 @@ def train(dataset, show_bar=False):
 
 
         vars_val = sess.run(vars)
-        save_para("/home/xinyang/Desktop/RM_auto-aim/tools/para", vars_val)
+        save_para("/home/xinyang/Workspace/RM_auto-aim/tools/para", vars_val)
         print("save done!")
 #        nodes_val = sess.run(nodes, feed_dict={x:test_images})
 #        return vars_val, nodes_val
@@ -204,5 +204,5 @@ def train(dataset, show_bar=False):
 
 
 if __name__ == "__main__":
-    dataset = generate.DataSet("/home/xinyang/Desktop/box_cut")
+    dataset = generate.DataSet("/home/xinyang/Workspace/dataset/box_cut")
     train(dataset, show_bar=True)

tools/para/conv1_b

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tools/para/conv1_w

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tools/para/conv2_b

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tools/para/conv2_w

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