Epoch 1/100, Loss: 2.3002
Epoch 2/100, Loss: 2.3123
Epoch 3/100, Loss: 2.3011
Epoch 4/100, Loss: 2.3003
Epoch 5/100, Loss: 2.3112
Epoch 6/100, Loss: 2.2946
Epoch 7/100, Loss: 2.3166
Epoch 8/100, Loss: 2.3059
Epoch 9/100, Loss: 2.3019
Epoch 10/100, Loss: 2.2965
Epoch 11/100, Loss: 2.3008
Epoch 12/100, Loss: 2.2898
Epoch 13/100, Loss: 2.2929
Epoch 14/100, Loss: 2.3041
Epoch 15/100, Loss: 2.3064
Epoch 16/100, Loss: 2.3092
Epoch 17/100, Loss: 2.3025
Epoch 18/100, Loss: 2.2937
Epoch 19/100, Loss: 2.3180
Epoch 20/100, Loss: 2.3020
Epoch 21/100, Loss: 2.3014
Epoch 22/100, Loss: 2.2992
Epoch 23/100, Loss: 2.2998
Epoch 24/100, Loss: 2.3110
Epoch 25/100, Loss: 2.3079
Epoch 26/100, Loss: 2.2960
Epoch 27/100, Loss: 2.2950
Epoch 28/100, Loss: 2.3016
Epoch 29/100, Loss: 2.3003
Epoch 30/100, Loss: 2.3045
Epoch 31/100, Loss: 2.2979
Epoch 32/100, Loss: 2.2883
Epoch 33/100, Loss: 2.2950
Epoch 34/100, Loss: 2.3081
Epoch 35/100, Loss: 2.2903
Epoch 36/100, Loss: 2.2882
Epoch 37/100, Loss: 2.3086
Epoch 38/100, Loss: 2.3024
Epoch 39/100, Loss: 2.2875
Epoch 40/100, Loss: 2.2955
Epoch 41/100, Loss: 2.2923
Epoch 42/100, Loss: 2.2937
Epoch 43/100, Loss: 2.2817
Epoch 44/100, Loss: 2.2998
Epoch 45/100, Loss: 2.2871
Epoch 46/100, Loss: 2.2985
Epoch 47/100, Loss: 2.3055
Epoch 48/100, Loss: 2.3113
Epoch 49/100, Loss: 2.3136
Epoch 50/100, Loss: 2.2975
Epoch 51/100, Loss: 2.2948
Epoch 52/100, Loss: 2.2966
Epoch 53/100, Loss: 2.3053
Epoch 54/100, Loss: 2.2987
Epoch 55/100, Loss: 2.3014
Epoch 56/100, Loss: 2.3008
Epoch 57/100, Loss: 2.2970
Epoch 58/100, Loss: 2.2844
Epoch 59/100, Loss: 2.3035
Epoch 60/100, Loss: 2.2943
Epoch 61/100, Loss: 2.2893
Epoch 62/100, Loss: 2.3065
Epoch 63/100, Loss: 2.2968
Epoch 64/100, Loss: 2.2972
Epoch 65/100, Loss: 2.2965
Epoch 66/100, Loss: 2.3090
Epoch 67/100, Loss: 2.2935
Epoch 68/100, Loss: 2.2890
Epoch 69/100, Loss: 2.3008
Epoch 70/100, Loss: 2.3005
Epoch 71/100, Loss: 2.2891
Epoch 72/100, Loss: 2.3105
Epoch 73/100, Loss: 2.3061
Epoch 74/100, Loss: 2.2951
Epoch 75/100, Loss: 2.2963
Epoch 76/100, Loss: 2.3025
Epoch 77/100, Loss: 2.3000
Epoch 78/100, Loss: 2.2995
Epoch 79/100, Loss: 2.2883
Epoch 80/100, Loss: 2.2999
Epoch 81/100, Loss: 2.3020
Epoch 82/100, Loss: 2.2879
Epoch 83/100, Loss: 2.3041
Epoch 84/100, Loss: 2.3057
Epoch 85/100, Loss: 2.2907
Epoch 86/100, Loss: 2.3039
Epoch 87/100, Loss: 2.2888
Epoch 88/100, Loss: 2.2999
Epoch 89/100, Loss: 2.3098
Epoch 90/100, Loss: 2.3005
Epoch 91/100, Loss: 2.3165
Epoch 92/100, Loss: 2.3127
Epoch 93/100, Loss: 2.2971
Epoch 94/100, Loss: 2.2953
Epoch 95/100, Loss: 2.3099
Epoch 96/100, Loss: 2.3232
Epoch 97/100, Loss: 2.2814
Epoch 98/100, Loss: 2.2932
Epoch 99/100, Loss: 2.3031
Epoch 100/100, Loss: 2.3142
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-1-8428a47f3551> in <module>
116
117 # 测试模型
--> 118 y_pred = nn.predict(X_test)
119 accuracy = np.mean(y_pred == np.argmax(y_test, axis=1))
120 print(f'Test Accuracy: {accuracy * 100:.2f}%')
<ipython-input-1-8428a47f3551> in predict(self, X)
91
92 def predict(self, X):
---> 93 _, _, _, _, _, _, y_pred = self.forward(X)
94 return np.argmax(y_pred, axis=1)
95
ValueError: not enough values to unpack (expected 7, got 2)
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