neural-network - 在我的情况下我应该考虑什么来减少 val_loss？

Question

我是 cnn 的新手，我想知道如何改进我的模型？增强已经完成。提前致谢。

model = Sequential()
model.add(Conv2D(16, (3,3), activation='relu', strides=(1,1), 
                 padding='same', input_shape=input_shape))
model.add(Conv2D(32, (3,3), activation='relu', strides=(1,1),
                 padding='same'))
model.add(Conv2D(64, (3,3), activation='relu', strides=(1,1),
                 padding='same'))
#model.add(Conv2D(128, (3,3), activation='relu', strides=(1,1), 
 #            padding='same'))                 
#model.add(MaxPool2D(2,2))#AveragePooling2D
model.add(AveragePooling2D(2,2))#AveragePooling2D

model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))    
model.add(Dense(10, activation='softmax'))
model.summary()
#opt = keras.optimizers.Adam(learning_rate=0.001)
model.compile(loss='categorical_crossentropy', 
              optimizer= "Adam",
              metrics=\['acc'\] )][1]][1]

history = model.fit(X, y, epochs=150, batch_size=32, 
      shuffle=True, validation_split=0.1
      callbacks = [checkpoint])

Epoch 00140: val_acc did not improve from 0.93082
Epoch 141/150
28620/28620 [==============================] - 37s 1ms/step - loss: 0.1654 - acc: 0.9401 - val_loss: 0.2388 - val_acc: 0.9267

Epoch 00141: val_acc did not improve from 0.93082
Epoch 142/150
28620/28620 [==============================] - 38s 1ms/step - loss: 0.1314 - acc: 0.9516 - val_loss: 0.2728 - val_acc: 0.9091

Epoch 00142: val_acc did not improve from 0.93082
Epoch 143/150
28620/28620 [==============================] - 37s 1ms/step - loss: 0.1425 - acc: 0.9476 - val_loss: 0.2439 - val_acc: 0.9242

Epoch 00143: val_acc did not improve from 0.93082
Epoch 144/150
28620/28620 [==============================] - 37s 1ms/step - loss: 0.1434 - acc: 0.9473 - val_loss: 0.3709 - val_acc: 0.8824

Epoch 00144: val_acc did not improve from 0.93082
Epoch 145/150
28620/28620 [==============================] - 37s 1ms/step - loss: 0.1483 - acc: 0.9468 - val_loss: 0.2544 - val_acc: 0.9208

Epoch 00145: val_acc did not improve from 0.93082
Epoch 146/150
28620/28620 [==============================] - 35s 1ms/step - loss: 0.1366 - acc: 0.9501 - val_loss: 0.2872 - val_acc: 0.9110

Epoch 00146: val_acc did not improve from 0.93082
Epoch 147/150
28620/28620 [==============================] - 36s 1ms/step - loss: 0.1476 - acc: 0.9465 - val_loss: 0.3147 - val_acc: 0.9013

Epoch 00147: val_acc did not improve from 0.93082
Epoch 148/150
28620/28620 [==============================] - 36s 1ms/step - loss: 0.1391 - acc: 0.9486 - val_loss: 0.2838 - val_acc: 0.9069

Epoch 00148: val_acc did not improve from 0.93082
Epoch 149/150
28620/28620 [==============================] - 35s 1ms/step - loss: 0.1392 - acc: 0.9486 - val_loss: 0.2541 - val_acc: 0.9211

Epoch 00149: val_acc did not improve from 0.93082
Epoch 150/150
28620/28620 [==============================] - 37s 1ms/step - loss: 0.1401 - acc: 0.9489 - val_loss: 0.2213 - val_acc: 0.9308

Epoch 00150: val_acc did not improve from 0.93082

标签： neural-networkconv-neural-network