【发布时间】：2021-06-13 12:52:35
【问题描述】：
我正在运行以下代码：
basemodel.fit(X_train,y_train,epochs=25,validation_split=.1,callbacks=call_back)
但我得到了一个结果Epoch 00014: val_accuracy did not improve from 0.57709。我不确定那里有什么问题，因为我清楚地看到我的损失减少了，而且我的准确性提高了。
这是结果
Epoch 1/25
909/909 [==============================] - 13s 6ms/step - loss: 1.6465 - accuracy: 0.3396 - val_loss: 1.4830 - val_accuracy: 0.4334
Epoch 00001: val_accuracy improved from -inf to 0.43344, saving model to checkpoint/best_model.h5
Epoch 2/25
909/909 [==============================] - 5s 5ms/step - loss: 1.3402 - accuracy: 0.4860 - val_loss: 1.3291 - val_accuracy: 0.4926
Epoch 00002: val_accuracy improved from 0.43344 to 0.49257, saving model to checkpoint/best_model.h5
Epoch 3/25
909/909 [==============================] - 5s 5ms/step - loss: 1.2050 - accuracy: 0.5418 - val_loss: 1.2769 - val_accuracy: 0.5025
Epoch 00003: val_accuracy improved from 0.49257 to 0.50248, saving model to checkpoint/best_model.h5
Epoch 4/25
909/909 [==============================] - 5s 5ms/step - loss: 1.1054 - accuracy: 0.5806 - val_loss: 1.1936 - val_accuracy: 0.5495
Epoch 00004: val_accuracy improved from 0.50248 to 0.54954, saving model to checkpoint/best_model.h5
Epoch 5/25
909/909 [==============================] - 5s 5ms/step - loss: 1.0190 - accuracy: 0.6159 - val_loss: 1.1535 - val_accuracy: 0.5551
Epoch 00005: val_accuracy improved from 0.54954 to 0.55511, saving model to checkpoint/best_model.h5
Epoch 6/25
909/909 [==============================] - 5s 5ms/step - loss: 0.9329 - accuracy: 0.6502 - val_loss: 1.1962 - val_accuracy: 0.5641
Epoch 00006: val_accuracy improved from 0.55511 to 0.56409, saving model to checkpoint/best_model.h5
Epoch 7/25
909/909 [==============================] - 5s 5ms/step - loss: 0.8435 - accuracy: 0.6846 - val_loss: 1.1707 - val_accuracy: 0.5771
Epoch 00007: val_accuracy improved from 0.56409 to 0.57709, saving model to checkpoint/best_model.h5
Epoch 8/25
909/909 [==============================] - 5s 5ms/step - loss: 0.7527 - accuracy: 0.7201 - val_loss: 1.3817 - val_accuracy: 0.5545
Epoch 00008: val_accuracy did not improve from 0.57709
Epoch 9/25
909/909 [==============================] - 5s 5ms/step - loss: 0.6633 - accuracy: 0.7576 - val_loss: 1.5021 - val_accuracy: 0.5207
Epoch 00009: val_accuracy did not improve from 0.57709
Epoch 10/25
909/909 [==============================] - 5s 5ms/step - loss: 0.5865 - accuracy: 0.7874 - val_loss: 1.5610 - val_accuracy: 0.5721
Epoch 00010: val_accuracy did not improve from 0.57709
Epoch 11/25
909/909 [==============================] - 5s 5ms/step - loss: 0.5154 - accuracy: 0.8097 - val_loss: 1.5723 - val_accuracy: 0.5430
Epoch 00011: val_accuracy did not improve from 0.57709
Epoch 12/25
909/909 [==============================] - 5s 6ms/step - loss: 0.4540 - accuracy: 0.8333 - val_loss: 2.1641 - val_accuracy: 0.5650
Epoch 00012: val_accuracy did not improve from 0.57709
Epoch 13/25
909/909 [==============================] - 5s 5ms/step - loss: 0.4106 - accuracy: 0.8511 - val_loss: 2.3236 - val_accuracy: 0.5322
Epoch 00013: val_accuracy did not improve from 0.57709
Epoch 14/25
909/909 [==============================] - 5s 5ms/step - loss: 0.3747 - accuracy: 0.8682 - val_loss: 1.8985 - val_accuracy: 0.5567
Epoch 00014: val_accuracy did not improve from 0.57709
Epoch 15/25
909/909 [==============================] - 5s 5ms/step - loss: 0.3480 - accuracy: 0.8768 - val_loss: 2.1689 - val_accuracy: 0.5505
Epoch 00015: val_accuracy did not improve from 0.57709
Epoch 16/25
909/909 [==============================] - 5s 5ms/step - loss: 0.3224 - accuracy: 0.8878 - val_loss: 2.0880 - val_accuracy: 0.5269
Epoch 00016: val_accuracy did not improve from 0.57709
Epoch 17/25
909/909 [==============================] - 5s 5ms/step - loss: 0.3157 - accuracy: 0.8912 - val_loss: 2.2746 - val_accuracy: 0.5328
Epoch 00017: val_accuracy did not improve from 0.57709
Epoch 18/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2960 - accuracy: 0.8992 - val_loss: 2.3014 - val_accuracy: 0.5582
Epoch 00018: val_accuracy did not improve from 0.57709
Epoch 19/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2961 - accuracy: 0.8998 - val_loss: 2.8190 - val_accuracy: 0.5399
Epoch 00019: val_accuracy did not improve from 0.57709
Epoch 20/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2945 - accuracy: 0.9016 - val_loss: 2.5621 - val_accuracy: 0.5495
Epoch 00020: val_accuracy did not improve from 0.57709
Epoch 21/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2772 - accuracy: 0.9075 - val_loss: 2.6602 - val_accuracy: 0.5402
Epoch 00021: val_accuracy did not improve from 0.57709
Epoch 22/25
909/909 [==============================] - 5s 6ms/step - loss: 0.2857 - accuracy: 0.9070 - val_loss: 2.7156 - val_accuracy: 0.5381
Epoch 00022: val_accuracy did not improve from 0.57709
Epoch 23/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2767 - accuracy: 0.9098 - val_loss: 3.4705 - val_accuracy: 0.5291
Epoch 00023: val_accuracy did not improve from 0.57709
Epoch 24/25
909/909 [==============================] - 5s 6ms/step - loss: 0.2725 - accuracy: 0.9100 - val_loss: 3.5462 - val_accuracy: 0.5706
Epoch 00024: val_accuracy did not improve from 0.57709
Epoch 25/25
909/909 [==============================] - 5s 5ms/step - loss: 0.2675 - accuracy: 0.9134 - val_loss: 2.3214 - val_accuracy: 0.5254
Epoch 00025: val_accuracy did not improve from 0.57709
<tensorflow.python.keras.callbacks.History at 0x7f9d42d7afd0>
下面是我的代码截图：
我的学习率是 0.01。
basemodel.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=.01), loss='sparse_categorical_crossentropy', metrics=['accuracy'])
【问题讨论】：
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我们不接受代码作为图像，您必须将所有代码都包含在文本中。
标签： tensorflow keras conv-neural-network