Python 使用keras进行回归时的误差并没有减少
我正在使用keras进行图像去马赛克。我使用不同的渠道，然后我建立一个MLP。代码如下-：Python 使用keras进行回归时的误差并没有减少,python,neural-network,keras,loss,Python,Neural Network,Keras,Loss,我正在使用keras进行图像去马赛克。我使用不同的渠道，然后我建立一个MLP。代码如下-： modelRed = Sequential() modelRed.add(Dense(12 , input_shape=(red_rows,))) modelRed.add(Activation('relu')) modelRed.add(Dense(8)) modelRed.add(Activation('relu')) modelRed.add(Dense(4)) modelRed.add(Ac
modelRed = Sequential()
modelRed.add(Dense(12 , input_shape=(red_rows,)))
modelRed.add(Activation('relu'))
modelRed.add(Dense(8))
modelRed.add(Activation('relu'))
modelRed.add(Dense(4))
modelRed.add(Activation('relu'))
modelRed.add(Dense(1))
modelRed.add(Activation('relu'))
# for a mean squared error regression problem
modelRed.compile(optimizer='adam', loss='mean_squared_error')
modelRed.fit(X_train_red, Y_train_red, batch_size=batch_size, nb_epoch=nb_epoch, validation_data=(X_test_red, Y_test_red), verbose=1)
Train on 3189384 samples, validate on 1321608 samples
Epoch 1/250
3189384/3189384 [==============================] - 27s - loss: 198.4509 - val_loss: 115.0920
Epoch 2/250
3189384/3189384 [==============================] - 28s - loss: 164.1169 - val_loss: 112.7843
Epoch 3/250
3189384/3189384 [==============================] - 26s - loss: 163.5934 - val_loss: 112.5161
Epoch 4/250
3189384/3189384 [==============================] - 27s - loss: 163.1947 - val_loss: 112.1536
Epoch 5/250
3189384/3189384 [==============================] - 27s - loss: 162.7713 - val_loss: 111.6212
Epoch 6/250
3189384/3189384 [==============================] - 23s - loss: 162.1866 - val_loss: 111.6891
Epoch 7/250
3189384/3189384 [==============================] - 27s - loss: 161.2873 - val_loss: 110.5558
Epoch 8/250
3189384/3189384 [==============================] - 27s - loss: 160.2043 - val_loss: 109.1132
Epoch 9/250
3189384/3189384 [==============================] - 26s - loss: 159.4309 - val_loss: 108.6063
Epoch 10/250
3189384/3189384 [==============================] - 26s - loss: 158.9754 - val_loss: 108.2700
Epoch 11/250
3189384/3189384 [==============================] - 27s - loss: 158.4734 - val_loss: 107.8127
Epoch 12/250
3189384/3189384 [==============================] - 25s - loss: 158.0978 - val_loss: 108.4448
Epoch 13/250
3189384/3189384 [==============================] - 26s - loss: 157.7607 - val_loss: 107.6800
Epoch 14/250
3189384/3189384 [==============================] - 26s - loss: 157.4270 - val_loss: 107.1163
Epoch 15/250
3189384/3189384 [==============================] - 27s - loss: 157.0340 - val_loss: 106.3458
Epoch 16/250
3189384/3189384 [==============================] - 27s - loss: 156.8197 - val_loss: 106.3995
Epoch 17/250
3189384/3189384 [==============================] - 26s - loss: 156.5804 - val_loss: 105.8783
Epoch 18/250
3189384/3189384 [==============================] - 26s - loss: 156.3417 - val_loss: 105.9321
Epoch 19/250
3189384/3189384 [==============================] - 27s - loss: 156.2147 - val_loss: 106.3168
Epoch 20/250
3189384/3189384 [==============================] - 25s - loss: 155.9733 - val_loss: 105.2670
Epoch 21/250
3189384/3189384 [==============================] - 27s - loss: 155.7053 - val_loss: 108.8695
Epoch 22/250
3189384/3189384 [==============================] - 26s - loss: 155.3440 - val_loss: 105.9274
Epoch 23/250
3189384/3189384 [==============================] - 25s - loss: 155.0451 - val_loss: 107.7409
Epoch 24/250
3189384/3189384 [==============================] - 24s - loss: 154.9362 - val_loss: 104.4415
Epoch 25/250
3189384/3189384 [==============================] - 27s - loss: 154.6278 - val_loss: 104.0498
Epoch 26/250
3189384/3189384 [==============================] - 26s - loss: 154.4255 - val_loss: 104.9958
Epoch 27/250
3189384/3189384 [==============================] - 25s - loss: 154.1879 - val_loss: 103.9495
Epoch 28/250
3189384/3189384 [==============================] - 26s - loss: 154.0734 - val_loss: 103.5785
Epoch 29/250
3189384/3189384 [==============================] - 26s - loss: 153.9429 - val_loss: 107.9921
Epoch 30/250
3189384/3189384 [==============================] - 24s - loss: 153.7445 - val_loss: 103.5331
Epoch 31/250
3189384/3189384 [==============================] - 28s - loss: 153.5577 - val_loss: 103.7680
Epoch 32/250
3189384/3189384 [==============================] - 26s - loss: 153.4119 - val_loss: 103.7041
Epoch 33/250
3189384/3189384 [==============================] - 28s - loss: 153.2540 - val_loss: 103.2984
Epoch 34/250
3189384/3189384 [==============================] - 26s - loss: 152.9925 - val_loss: 103.0740
Epoch 35/250
3189384/3189384 [==============================] - 28s - loss: 152.7723 - val_loss: 107.2211
Epoch 36/250
3189384/3189384 [==============================] - 28s - loss: 152.6709 - val_loss: 102.5169
Epoch 37/250
3189384/3189384 [==============================] - 27s - loss: 152.4868 - val_loss: 103.2555
Epoch 38/250
3189384/3189384 [==============================] - 26s - loss: 152.3653 - val_loss: 101.7679
Epoch 39/250
3189384/3189384 [==============================] - 27s - loss: 152.1673 - val_loss: 102.5685
Epoch 40/250
3189384/3189384 [==============================] - 27s - loss: 151.9953 - val_loss: 105.8509
Epoch 41/250
3189384/3189384 [==============================] - 27s - loss: 151.8490 - val_loss: 101.6949
Epoch 42/250
3189384/3189384 [==============================] - 29s - loss: 151.8251 - val_loss: 104.2125
Epoch 43/250
3189384/3189384 [==============================] - 28s - loss: 151.7456 - val_loss: 101.3656
Epoch 44/250
3189384/3189384 [==============================] - 26s - loss: 151.6388 - val_loss: 100.9429
Epoch 45/250
3189384/3189384 [==============================] - 27s - loss: 151.4518 - val_loss: 101.2955
Epoch 46/250
3189384/3189384 [==============================] - 28s - loss: 151.4171 - val_loss: 101.2979
Epoch 47/250
3189384/3189384 [==============================] - 27s - loss: 151.3336 - val_loss: 101.6419
Epoch 48/250
3189384/3189384 [==============================] - 25s - loss: 151.3031 - val_loss: 101.1190
Epoch 49/250
3189384/3189384 [==============================] - 27s - loss: 151.1852 - val_loss: 102.2938
Epoch 50/250
3189384/3189384 [==============================] - 28s - loss: 151.0914 - val_loss: 101.3121
Epoch 51/250
3189384/3189384 [==============================] - 26s - loss: 151.0504 - val_loss: 102.0878
Epoch 52/250
3189384/3189384 [==============================] - 29s - loss: 151.0902 - val_loss: 101.4168
Epoch 53/250
3189384/3189384 [==============================] - 28s - loss: 150.9420 - val_loss: 101.3169
Epoch 54/250
3189384/3189384 [==============================] - 21s - loss: 150.7804 - val_loss: 101.0129
Epoch 55/250
3189384/3189384 [==============================] - 20s - loss: 150.7772 - val_loss: 100.8667
Epoch 56/250
3189384/3189384 [==============================] - 19s - loss: 150.7716 - val_loss: 101.2737
Epoch 57/250
3189384/3189384 [==============================] - 18s - loss: 150.6755 - val_loss: 102.6338
Epoch 58/250
3189384/3189384 [==============================] - 19s - loss: 150.7102 - val_loss: 100.6505
Epoch 59/250
3189384/3189384 [==============================] - 19s - loss: 150.6123 - val_loss: 100.5806
Epoch 60/250
3189384/3189384 [==============================] - 20s - loss: 150.4697 - val_loss: 101.9158
Epoch 61/250
3189384/3189384 [==============================] - 18s - loss: 150.5636 - val_loss: 100.8117
Epoch 62/250
3189384/3189384 [==============================] - 17s - loss: 150.4429 - val_loss: 100.8131
Epoch 63/250
3189384/3189384 [==============================] - 20s - loss: 150.4293 - val_loss: 100.3910
Epoch 64/250
3189384/3189384 [==============================] - 20s - loss: 150.3402 - val_loss: 100.8772
Epoch 65/250
3189384/3189384 [==============================] - 19s - loss: 150.3486 - val_loss: 100.7313
Epoch 66/250
3189384/3189384 [==============================] - 19s - loss: 150.3270 - val_loss: 100.1140
Epoch 67/250
3189384/3189384 [==============================] - 19s - loss: 150.2567 - val_loss: 100.7384
Epoch 68/250
3189384/3189384 [==============================] - 20s - loss: 150.2960 - val_loss: 100.5642
Epoch 69/250
3189384/3189384 [==============================] - 19s - loss: 150.2077 - val_loss: 101.8608
Epoch 70/250
3189384/3189384 [==============================] - 19s - loss: 150.1999 - val_loss: 101.6729
Epoch 71/250
3189384/3189384 [==============================] - 19s - loss: 150.1400 - val_loss: 100.6969
Epoch 72/250
3189384/3189384 [==============================] - 17s - loss: 150.1811 - val_loss: 101.6791
Epoch 73/250
3189384/3189384 [==============================] - 19s - loss: 150.1928 - val_loss: 100.3208
Epoch 74/250
3189384/3189384 [==============================] - 19s - loss: 150.1076 - val_loss: 101.1390
Epoch 75/250
3189384/3189384 [==============================] - 20s - loss: 150.1662 - val_loss: 100.2958
Epoch 76/250
3189384/3189384 [==============================] - 20s - loss: 150.1308 - val_loss: 101.4206
Epoch 77/250
3189384/3189384 [==============================] - 20s - loss: 150.0151 - val_loss: 100.8570
Epoch 78/250
3189384/3189384 [==============================] - 21s - loss: 150.1159 - val_loss: 100.0430
Epoch 79/250
3189384/3189384 [==============================] - 20s - loss: 150.0752 - val_loss: 100.1425
Epoch 80/250
3189384/3189384 [==============================] - 20s - loss: 150.0975 - val_loss: 100.9499
Epoch 81/250
3189384/3189384 [==============================] - 19s - loss: 150.0325 - val_loss: 100.6844
Epoch 82/250
3189384/3189384 [==============================] - 19s - loss: 150.0400 - val_loss: 100.1474
Epoch 83/250
3189384/3189384 [==============================] - 21s - loss: 150.1016 - val_loss: 100.3526
Epoch 84/250
3189384/3189384 [==============================] - 20s - loss: 149.9997 - val_loss: 99.9948
Epoch 85/250
3189384/3189384 [==============================] - 20s - loss: 150.0394 - val_loss: 100.8612
Epoch 86/250
3189384/3189384 [==============================] - 20s - loss: 150.0178 - val_loss: 100.2646
Epoch 87/250
3189384/3189384 [==============================] - 20s - loss: 149.9514 - val_loss: 100.0428
Epoch 88/250
3189384/3189384 [==============================] - 20s - loss: 149.9144 - val_loss: 99.9527
Epoch 89/250
3189384/3189384 [==============================] - 20s - loss: 149.9725 - val_loss: 100.2454
Epoch 90/250
3189384/3189384 [==============================] - 20s - loss: 149.9293 - val_loss: 100.7448
Epoch 91/250
3189384/3189384 [==============================] - 20s - loss: 149.9375 - val_loss: 100.8647
Epoch 92/250
3189384/3189384 [==============================] - 20s - loss: 149.8886 - val_loss: 100.9337
Epoch 93/250
3189384/3189384 [==============================] - 20s - loss: 149.8927 - val_loss: 100.0454
Epoch 94/250
3189384/3189384 [==============================] - 20s - loss: 149.8331 - val_loss: 102.0999
Epoch 95/250
3189384/3189384 [==============================] - 21s - loss: 149.7996 - val_loss: 100.8161
Epoch 96/250
3189384/3189384 [==============================] - 20s - loss: 149.9432 - val_loss: 101.4724
Epoch 97/250
3189384/3189384 [==============================] - 20s - loss: 149.8627 - val_loss: 101.4424
Epoch 98/250
3189384/3189384 [==============================] - 20s - loss: 149.7552 - val_loss: 101.1071
Epoch 99/250
3189384/3189384 [==============================] - 20s - loss: 149.7364 - val_loss: 100.0904
Epoch 100/250
3189384/3189384 [==============================] - 20s - loss: 149.7900 - val_loss: 100.8228
Epoch 101/250
3189384/3189384 [==============================] - 20s - loss: 149.7288 - val_loss: 100.9811
Epoch 102/250
3189384/3189384 [==============================] - 19s - loss: 149.7511 - val_loss: 100.0828
Epoch 103/250
3189384/3189384 [==============================] - 20s - loss: 149.5770 - val_loss: 100.5401
Epoch 104/250
3189384/3189384 [==============================] - 18s - loss: 149.5594 - val_loss: 101.6771
Epoch 105/250
3189384/3189384 [==============================] - 20s - loss: 149.5985 - val_loss: 99.9006
Epoch 106/250
3189384/3189384 [==============================] - 21s - loss: 149.5497 - val_loss: 99.6502
Epoch 107/250
3189384/3189384 [==============================] - 20s - loss: 149.5558 - val_loss: 100.1236
Epoch 108/250
3189384/3189384 [==============================] - 19s - loss: 149.4798 - val_loss: 100.0641
Epoch 109/250
3189384/3189384 [==============================] - 20s - loss: 149.5083 - val_loss: 100.3339
Epoch 110/250
3189384/3189384 [==============================] - 18s - loss: 149.5003 - val_loss: 99.9933
Epoch 111/250
3189384/3189384 [==============================] - 19s - loss: 149.4592 - val_loss: 100.7275
Epoch 112/250
3189384/3189384 [==============================] - 21s - loss: 149.4799 - val_loss: 99.7688
Epoch 113/250
3189384/3189384 [==============================] - 20s - loss: 149.4295 - val_loss: 99.6558
Epoch 114/250
3189384/3189384 [==============================] - 19s - loss: 149.5045 - val_loss: 100.4619
Epoch 115/250
3189384/3189384 [==============================] - 19s - loss: 149.3667 - val_loss: 99.7501
Epoch 116/250
3189384/3189384 [==============================] - 20s - loss: 149.3659 - val_loss: 99.8401
Epoch 117/250
3189384/3189384 [==============================] - 20s - loss: 149.3168 - val_loss: 100.2400
Epoch 118/250
3189384/3189384 [==============================] - 19s - loss: 149.4157 - val_loss: 99.6916
Epoch 119/250
3189384/3189384 [==============================] - 20s - loss: 149.3364 - val_loss: 99.8611
Epoch 120/250
3189384/3189384 [==============================] - 19s - loss: 149.3278 - val_loss: 100.0638
Epoch 121/250
3189384/3189384 [==============================] - 20s - loss: 149.3423 - val_loss: 100.7594
Epoch 122/250
3189384/3189384 [==============================] - 20s - loss: 149.3336 - val_loss: 100.1827
Epoch 123/250
3189384/3189384 [==============================] - 19s - loss: 149.3595 - val_loss: 100.6511
Epoch 124/250
3189384/3189384 [==============================] - 19s - loss: 149.2708 - val_loss: 101.1436
Epoch 125/250
3189384/3189384 [==============================] - 20s - loss: 149.3454 - val_loss: 99.6360
Epoch 126/250
3189384/3189384 [==============================] - 19s - loss: 149.3264 - val_loss: 99.7426
Epoch 127/250
3189384/3189384 [==============================] - 20s - loss: 149.3152 - val_loss: 99.5290
Epoch 128/250
3189384/3189384 [==============================] - 20s - loss: 149.2971 - val_loss: 100.0325
Epoch 129/250
3189384/3189384 [==============================] - 19s - loss: 149.2365 - val_loss: 101.4393
Epoch 130/250
3189384/3189384 [==============================] - 19s - loss: 149.2813 - val_loss: 99.9453
Epoch 131/250
3189384/3189384 [==============================] - 19s - loss: 149.2793 - val_loss: 99.4344
Epoch 132/250
3189384/3189384 [==============================] - 19s - loss: 149.2455 - val_loss: 99.4761
Epoch 133/250
3189384/3189384 [==============================] - 19s - loss: 149.1975 - val_loss: 99.2306
Epoch 134/250
3189384/3189384 [==============================] - 19s - loss: 149.2811 - val_loss: 100.6100
Epoch 135/250
3189384/3189384 [==============================] - 19s - loss: 149.2285 - val_loss: 103.0949
Epoch 136/250
3189384/3189384 [==============================] - 19s - loss: 149.2315 - val_loss: 99.8146
Epoch 137/250
3189384/3189384 [==============================] - 20s - loss: 149.2820 - val_loss: 99.6034
Epoch 138/250
3189384/3189384 [==============================] - 19s - loss: 149.2051 - val_loss: 99.6248
Epoch 139/250
3189384/3189384 [==============================] - 19s - loss: 149.1962 - val_loss: 100.9793
Epoch 140/250
3189384/3189384 [==============================] - 20s - loss: 149.1947 - val_loss: 100.4904
Epoch 141/250
3189384/3189384 [==============================] - 18s - loss: 149.1905 - val_loss: 100.3685
Epoch 142/250
3189384/3189384 [==============================] - 18s - loss: 149.2253 - val_loss: 101.2468
Epoch 143/250
3189384/3189384 [==============================] - 18s - loss: 149.1867 - val_loss: 99.5863
Epoch 144/250
3189384/3189384 [==============================] - 19s - loss: 149.1776 - val_loss: 100.3279
Epoch 145/250
3189384/3189384 [==============================] - 20s - loss: 149.2580 - val_loss: 101.8950
Epoch 146/250
3189384/3189384 [==============================] - 18s - loss: 149.1016 - val_loss: 99.3928
Epoch 147/250
3189384/3189384 [==============================] - 19s - loss: 149.1625 - val_loss: 99.4247
Epoch 148/250
3189384/3189384 [==============================] - 19s - loss: 149.1728 - val_loss: 99.8834
Epoch 149/250
3189384/3189384 [==============================] - 20s - loss: 149.1103 - val_loss: 99.5946
Epoch 150/250
3189384/3189384 [==============================] - 19s - loss: 149.2158 - val_loss: 100.1131
Epoch 151/250
3189384/3189384 [==============================] - 20s - loss: 149.1529 - val_loss: 99.7070
Epoch 152/250
3189384/3189384 [==============================] - 19s - loss: 149.1417 - val_loss: 100.2416
Epoch 153/250
3189384/3189384 [==============================] - 19s - loss: 149.1114 - val_loss: 99.7661
Epoch 154/250
3189384/3189384 [==============================] - 19s - loss: 149.1847 - val_loss: 99.3258
Epoch 155/250
3189384/3189384 [==============================] - 19s - loss: 149.1204 - val_loss: 100.7560
Epoch 156/250
3189384/3189384 [==============================] - 19s - loss: 149.0693 - val_loss: 100.3359
Epoch 157/250
3189384/3189384 [==============================] - 19s - loss: 149.1283 - val_loss: 99.6533
Epoch 158/250
3189384/3189384 [==============================] - 19s - loss: 149.0860 - val_loss: 99.5419
Epoch 159/250
3189384/3189384 [==============================] - 19s - loss: 149.0667 - val_loss: 99.1946
Epoch 160/250
3189384/3189384 [==============================] - 18s - loss: 149.0769 - val_loss: 99.4479
Epoch 161/250
3189384/3189384 [==============================] - 20s - loss: 149.0613 - val_loss: 99.4561
Epoch 162/250
3189384/3189384 [==============================] - 19s - loss: 149.0951 - val_loss: 99.4610
Epoch 163/250
3189384/3189384 [==============================] - 18s - loss: 149.0441 - val_loss: 100.1443
Epoch 164/250
3189384/3189384 [==============================] - 19s - loss: 149.0430 - val_loss: 99.0341
Epoch 165/250
3189384/3189384 [==============================] - 20s - loss: 149.0439 - val_loss: 99.2335
Epoch 166/250
3189384/3189384 [==============================] - 19s - loss: 149.0988 - val_loss: 101.0581
Epoch 167/250
3189384/3189384 [==============================] - 19s - loss: 149.0660 - val_loss: 99.5455
Epoch 168/250
3189384/3189384 [==============================] - 18s - loss: 149.0017 - val_loss: 99.0922
Epoch 169/250
3189384/3189384 [==============================] - 19s - loss: 149.0740 - val_loss: 99.7199
Epoch 170/250
3189384/3189384 [==============================] - 19s - loss: 149.0057 - val_loss: 99.3090
Epoch 171/250
3189384/3189384 [==============================] - 19s - loss: 149.0293 - val_loss: 99.7018
Epoch 172/250
3189384/3189384 [==============================] - 19s - loss: 149.0007 - val_loss: 99.1355
Epoch 173/250
3189384/3189384 [==============================] - 20s - loss: 149.0428 - val_loss: 99.4297
Epoch 174/250
3189384/3189384 [==============================] - 20s - loss: 148.9911 - val_loss: 99.7230
Epoch 175/250
3189384/3189384 [==============================] - 19s - loss: 148.9911 - val_loss: 101.1982
Epoch 176/250
3189384/3189384 [==============================] - 19s - loss: 148.9810 - val_loss: 99.5931
Epoch 177/250
3189384/3189384 [==============================] - 19s - loss: 149.0001 - val_loss: 99.3125
Epoch 178/250
3189384/3189384 [==============================] - 19s - loss: 148.9734 - val_loss: 99.3773
Epoch 179/250
3189384/3189384 [==============================] - 19s - loss: 148.9829 - val_loss: 99.4396
Epoch 180/250
3189384/3189384 [==============================] - 20s - loss: 148.9214 - val_loss: 99.7884
Epoch 181/250
3189384/3189384 [==============================] - 19s - loss: 148.9743 - val_loss: 99.2298
Epoch 182/250
3189384/3189384 [==============================] - 18s - loss: 148.8978 - val_loss: 99.1585
Epoch 183/250
3189384/3189384 [==============================] - 19s - loss: 148.9413 - val_loss: 99.2344
Epoch 184/250
3189384/3189384 [==============================] - 19s - loss: 148.9120 - val_loss: 99.0136
Epoch 185/250
3189384/3189384 [==============================] - 17s - loss: 148.8783 - val_loss: 100.9004
Epoch 186/250
3189384/3189384 [==============================] - 19s - loss: 148.8389 - val_loss: 100.4856
Epoch 187/250
3189384/3189384 [==============================] - 17s - loss: 148.8828 - val_loss: 99.3965
Epoch 188/250
3189384/3189384 [==============================] - 18s - loss: 148.8865 - val_loss: 98.8341
Epoch 189/250
3189384/3189384 [==============================] - 20s - loss: 148.8830 - val_loss: 99.0880
Epoch 190/250
3189384/3189384 [==============================] - 18s - loss: 148.8664 - val_loss: 98.9686
Epoch 191/250
3189384/3189384 [==============================] - 19s - loss: 148.8263 - val_loss: 98.8838
Epoch 192/250
3189384/3189384 [==============================] - 18s - loss: 148.7862 - val_loss: 99.0053
Epoch 193/250
3189384/3189384 [==============================] - 18s - loss: 148.8435 - val_loss: 99.5392
Epoch 194/250
3189384/3189384 [==============================] - 20s - loss: 148.7384 - val_loss: 99.9544
Epoch 195/250
3189384/3189384 [==============================] - 19s - loss: 148.7617 - val_loss: 99.5124
Epoch 196/250
3189384/3189384 [==============================] - 18s - loss: 148.8297 - val_loss: 99.7426
Epoch 197/250
3189384/3189384 [==============================] - 18s - loss: 148.8292 - val_loss: 99.0186
Epoch 198/250
3189384/3189384 [==============================] - 19s - loss: 148.7386 - val_loss: 98.9358
Epoch 199/250
3189384/3189384 [==============================] - 18s - loss: 148.8312 - val_loss: 99.1099
Epoch 200/250
3189384/3189384 [==============================] - 19s - loss: 148.8239 - val_loss: 99.0065
Epoch 201/250
3189384/3189384 [==============================] - 18s - loss: 148.6835 - val_loss: 99.0648
Epoch 202/250
3189384/3189384 [==============================] - 19s - loss: 148.7894 - val_loss: 99.2819
Epoch 203/250
3189384/3189384 [==============================] - 19s - loss: 148.7926 - val_loss: 99.2102
Epoch 204/250
3189384/3189384 [==============================] - 18s - loss: 148.7083 - val_loss: 101.1436
Epoch 205/250
3189384/3189384 [==============================] - 19s - loss: 148.7803 - val_loss: 99.2455
Epoch 206/250
3189384/3189384 [==============================] - 19s - loss: 148.7795 - val_loss: 99.3742
Epoch 207/250
3189384/3189384 [==============================] - 17s - loss: 148.6948 - val_loss: 100.0003
Epoch 208/250
3189384/3189384 [==============================] - 20s - loss: 148.7431 - val_loss: 100.4932
Epoch 209/250
3189384/3189384 [==============================] - 18s - loss: 148.7704 - val_loss: 98.9162
Epoch 210/250
3189384/3189384 [==============================] - 18s - loss: 148.7105 - val_loss: 99.9950
Epoch 211/250
3189384/3189384 [==============================] - 18s - loss: 148.7366 - val_loss: 99.1594
Epoch 212/250
3189384/3189384 [==============================] - 20s - loss: 148.7822 - val_loss: 98.9526
Epoch 213/250
3189384/3189384 [==============================] - 20s - loss: 148.7025 - val_loss: 99.2840
Epoch 214/250
3189384/3189384 [==============================] - 19s - loss: 148.7471 - val_loss: 99.0218
Epoch 215/250
3189384/3189384 [==============================] - 19s - loss: 148.6716 - val_loss: 99.0359
Epoch 216/250
3189384/3189384 [==============================] - 18s - loss: 148.6982 - val_loss: 98.9464
Epoch 217/250
3189384/3189384 [==============================] - 20s - loss: 148.7371 - val_loss: 100.0163
Epoch 218/250
3189384/3189384 [==============================] - 18s - loss: 148.6641 - val_loss: 99.8047
Epoch 219/250
3189384/3189384 [==============================] - 20s - loss: 148.6957 - val_loss: 99.2249
Epoch 220/250
3189384/3189384 [==============================] - 19s - loss: 148.7248 - val_loss: 98.8837
Epoch 221/250
3189384/3189384 [==============================] - 19s - loss: 148.6348 - val_loss: 99.4872
Epoch 222/250
3189384/3189384 [==============================] - 19s - loss: 148.6926 - val_loss: 99.2652
Epoch 223/250
3189384/3189384 [==============================] - 20s - loss: 148.6983 - val_loss: 99.6037
Epoch 224/250
3189384/3189384 [==============================] - 19s - loss: 148.6424 - val_loss: 99.7763
Epoch 225/250
3189384/3189384 [==============================] - 20s - loss: 148.6962 - val_loss: 100.1271
Epoch 226/250
3189384/3189384 [==============================] - 18s - loss: 148.6427 - val_loss: 99.7176
Epoch 227/250
3189384/3189384 [==============================] - 19s - loss: 148.6249 - val_loss: 99.0244
Epoch 228/250
3189384/3189384 [==============================] - 19s - loss: 148.7087 - val_loss: 98.9011
Epoch 229/250
3189384/3189384 [==============================] - 20s - loss: 148.6634 - val_loss: 99.3864
Epoch 230/250
3189384/3189384 [==============================] - 19s - loss: 148.6680 - val_loss: 99.0461
Epoch 231/250
3189384/3189384 [==============================] - 19s - loss: 148.6788 - val_loss: 98.8900
Epoch 232/250
3189384/3189384 [==============================] - 19s - loss: 148.7143 - val_loss: 100.0765
Epoch 233/250
3189384/3189384 [==============================] - 18s - loss: 148.6532 - val_loss: 99.7450
Epoch 234/250
3189384/3189384 [==============================] - 20s - loss: 148.6910 - val_loss: 99.0379
Epoch 235/250
3189384/3189384 [==============================] - 19s - loss: 148.6293 - val_loss: 99.5760
Epoch 236/250
3189384/3189384 [==============================] - 21s - loss: 148.5989 - val_loss: 99.0081
Epoch 237/250
3189384/3189384 [==============================] - 21s - loss: 148.5898 - val_loss: 98.7291
Epoch 238/250
3189384/3189384 [==============================] - 19s - loss: 148.6142 - val_loss: 99.5720
Epoch 239/250
3189384/3189384 [==============================] - 21s - loss: 148.6579 - val_loss: 99.6840
Epoch 240/250
3189384/3189384 [==============================] - 20s - loss: 148.6669 - val_loss: 100.9804
Epoch 241/250
3189384/3189384 [==============================] - 20s - loss: 148.6196 - val_loss: 99.1919
Epoch 242/250
3189384/3189384 [==============================] - 20s - loss: 148.6002 - val_loss: 98.8735
Epoch 243/250
3189384/3189384 [==============================] - 21s - loss: 148.6069 - val_loss: 98.9618
Epoch 244/250
3189384/3189384 [==============================] - 19s - loss: 148.6802 - val_loss: 99.0968
Epoch 245/250
3189384/3189384 [==============================] - 21s - loss: 148.6096 - val_loss: 99.0785
Epoch 246/250
3189384/3189384 [==============================] - 20s - loss: 148.6481 - val_loss: 101.0822
Epoch 247/250
3189384/3189384 [==============================] - 21s - loss: 148.6492 - val_loss: 99.1234
Epoch 248/250
3189384/3189384 [==============================] - 20s - loss: 148.6716 - val_loss: 99.0074
Epoch 249/250
3189384/3189384 [==============================] - 20s - loss: 148.5658 - val_loss: 98.9340
Epoch 250/250
3189384/3189384 [==============================] - 19s - loss: 148.6153 - val_loss: 99.0102
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MLP辍学批量规范化min-maxnormalMLP辍学批量规范化min-maxnormal