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AaronJny
预测结果全部重复
大神， 我用最新的大乐透结果进行预测。先运行python3 train_with_whole_dataset.py，然后运行predict.py，结果预测结果全是最后一期的开奖号码（第1注 8 19 29 34 35 6 11，这个结果是更新下来的开奖数据中的最新一期结果）。这个问题出在哪？
[root@localhost lotto-master]# python3 predict.py 2020-12-04 09:55:42.890432: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2020-12-04 09:55:42.901135: I tensorflow/core/platform/profile_utils/cpu_utils.cc:104] CPU Frequency: 1696070000 Hz 2020-12-04 09:55:42.902469: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3da0480 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2020-12-04 09:55:42.902529: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version Model: "functional_1"
Layer (type) Output Shape Param # Connected to
x1 (InputLayer) [(None, 256, 35)] 0
x2 (InputLayer) [(None, 256, 35)] 0
x3 (InputLayer) [(None, 256, 35)] 0
x4 (InputLayer) [(None, 256, 35)] 0
x5 (InputLayer) [(None, 256, 35)] 0
x6 (InputLayer) [(None, 256, 12)] 0
x7 (InputLayer) [(None, 256, 12)] 0
bidirectional (Bidirectional) (None, 256, 128) 51200 x1[0][0]
bidirectional_2 (Bidirectional) (None, 256, 128) 51200 x2[0][0]
bidirectional_4 (Bidirectional) (None, 256, 128) 51200 x3[0][0]
bidirectional_6 (Bidirectional) (None, 256, 128) 51200 x4[0][0]
bidirectional_8 (Bidirectional) (None, 256, 128) 51200 x5[0][0]
bidirectional_10 (Bidirectional (None, 256, 128) 39424 x6[0][0]
bidirectional_12 (Bidirectional (None, 256, 128) 39424 x7[0][0]
dropout (Dropout) (None, 256, 128) 0 bidirectional[0][0]
dropout_2 (Dropout) (None, 256, 128) 0 bidirectional_2[0][0]
dropout_4 (Dropout) (None, 256, 128) 0 bidirectional_4[0][0]
dropout_6 (Dropout) (None, 256, 128) 0 bidirectional_6[0][0]
dropout_8 (Dropout) (None, 256, 128) 0 bidirectional_8[0][0]
dropout_10 (Dropout) (None, 256, 128) 0 bidirectional_10[0][0]
dropout_12 (Dropout) (None, 256, 128) 0 bidirectional_12[0][0]
bidirectional_1 (Bidirectional) (None, 256, 128) 98816 dropout[0][0]
bidirectional_3 (Bidirectional) (None, 256, 128) 98816 dropout_2[0][0]
bidirectional_5 (Bidirectional) (None, 256, 128) 98816 dropout_4[0][0]
bidirectional_7 (Bidirectional) (None, 256, 128) 98816 dropout_6[0][0]
bidirectional_9 (Bidirectional) (None, 256, 128) 98816 dropout_8[0][0]
bidirectional_11 (Bidirectional (None, 256, 128) 98816 dropout_10[0][0]
bidirectional_13 (Bidirectional (None, 256, 128) 98816 dropout_12[0][0]
dropout_1 (Dropout) (None, 256, 128) 0 bidirectional_1[0][0]
dropout_3 (Dropout) (None, 256, 128) 0 bidirectional_3[0][0]
dropout_5 (Dropout) (None, 256, 128) 0 bidirectional_5[0][0]
dropout_7 (Dropout) (None, 256, 128) 0 bidirectional_7[0][0]
dropout_9 (Dropout) (None, 256, 128) 0 bidirectional_9[0][0]
dropout_11 (Dropout) (None, 256, 128) 0 bidirectional_11[0][0]
dropout_13 (Dropout) (None, 256, 128) 0 bidirectional_13[0][0]
time_distributed (TimeDistribut (None, 256, 105) 13545 dropout_1[0][0]
time_distributed_1 (TimeDistrib (None, 256, 105) 13545 dropout_3[0][0]
time_distributed_2 (TimeDistrib (None, 256, 105) 13545 dropout_5[0][0]
time_distributed_3 (TimeDistrib (None, 256, 105) 13545 dropout_7[0][0]
time_distributed_4 (TimeDistrib (None, 256, 105) 13545 dropout_9[0][0]
time_distributed_5 (TimeDistrib (None, 256, 36) 4644 dropout_11[0][0]
time_distributed_6 (TimeDistrib (None, 256, 36) 4644 dropout_13[0][0]
flatten (Flatten) (None, 26880) 0 time_distributed[0][0]
flatten_1 (Flatten) (None, 26880) 0 time_distributed_1[0][0]
flatten_2 (Flatten) (None, 26880) 0 time_distributed_2[0][0]
flatten_3 (Flatten) (None, 26880) 0 time_distributed_3[0][0]
flatten_4 (Flatten) (None, 26880) 0 time_distributed_4[0][0]
flatten_5 (Flatten) (None, 9216) 0 time_distributed_5[0][0]
flatten_6 (Flatten) (None, 9216) 0 time_distributed_6[0][0]
dense_1 (Dense) (None, 105) 2822505 flatten[0][0]
dense_3 (Dense) (None, 105) 2822505 flatten_1[0][0]
dense_5 (Dense) (None, 105) 2822505 flatten_2[0][0]
dense_7 (Dense) (None, 105) 2822505 flatten_3[0][0]
dense_9 (Dense) (None, 105) 2822505 flatten_4[0][0]
dense_11 (Dense) (None, 36) 331812 flatten_5[0][0]
dense_13 (Dense) (None, 36) 331812 flatten_6[0][0]
concatenate (Concatenate) (None, 525) 0 dense_1[0][0]
dense_3[0][0]
dense_5[0][0]
dense_7[0][0]
dense_9[0][0]
concatenate_1 (Concatenate) (None, 72) 0 dense_11[0][0]
dense_13[0][0]
y1 (Dense) (None, 35) 18410 concatenate[0][0]
y2 (Dense) (None, 35) 18410 concatenate[0][0]
y3 (Dense) (None, 35) 18410 concatenate[0][0]
y4 (Dense) (None, 35) 18410 concatenate[0][0]
y5 (Dense) (None, 35) 18410 concatenate[0][0]
y6 (Dense) (None, 12) 876 concatenate_1[0][0]
y7 (Dense) (None, 12) 876 concatenate_1[0][0]
Total params: 15,973,524 Trainable params: 15,973,524 Non-trainable params: 0
本次预测结果如下： 第1注 8 19 29 34 35 6 11 第2注 8 19 29 34 35 6 11 第3注 8 19 29 34 35 6 11 第4注 8 19 29 34 35 6 11 第5注 8 19 29 34 35 6 11 第6注 8 19 29 34 35 6 11 第7注 8 19 29 34 35 6 11 第8注 8 19 29 34 35 6 11 第9注 8 19 29 34 35 6 11 第10注 8 19 29 34 35 6 11
我在我的环境下训练了400次。出来的结果也非常相似。 第1注 7 10 12 14 32 4 11 第2注 7 10 12 14 32 4 11 第3注 7 10 14 19 32 4 11 第4注 7 10 14 19 32 4 11 第5注 7 10 12 14 30 4 11
