所以在过去的几个月里，我一直在学习Tensorflow和Keras的神经网络，所以我想尝试为CIFAR10数据集建立一个模型（下面的代码）。
然而，在训练过程中，准确度变得更好（从1个时期后的约35％到5个时期后的约60-65％），但val_acc保持不变或仅增加一点。以下是打印结果：
Epoch 1/5
50000/50000 [==============================] - 454s 9ms/step - loss: 1.7761 - acc: 0.3584 - val_loss: 8.6776 - val_acc: 0.4489
Epoch 2/5
50000/50000 [==============================] - 452s 9ms/step - loss: 1.3670 - acc: 0.5131 - val_loss: 8.9749 - val_acc: 0.4365
Epoch 3/5
50000/50000 [==============================] - 451s 9ms/step - loss: 1.2089 - acc: 0.5721 - val_loss: 7.7254 - val_acc: 0.5118
Epoch 4/5
50000/50000 [==============================] - 452s 9ms/step - loss: 1.1140 - acc: 0.6080 - val_loss: 7.9587 - val_acc: 0.4997
Epoch 5/5
50000/50000 [==============================] - 452s 9ms/step - loss: 1.0306 - acc: 0.6385 - val_loss: 7.4351 - val_acc: 0.5321
10000/10000 [==============================] - 27s 3ms/step
loss: 7.435152648162842
accuracy: 0.5321
我在互联网上四处看看，我最好的猜测是我的模型过度装配，所以我尝试删除一些图层，添加更多的辍学图层并减少过滤器的数量，但没有任何显示任何增强。
最奇怪的是，不久之前我根据一些教程制作了一个非常相似的模型，在8个时期之后它的最终准确度为80％。 （虽然我丢失了那个文件）
以下是我的模型的代码：
model = Sequential()
model.add(Conv2D(filters=256,
kernel_size=(3, 3),
activation='relu',
data_format='channels_last',
input_shape=(32, 32, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(filters=128,
kernel_size=(2, 2),
activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer=adam(),
loss=categorical_crossentropy,
metrics=['accuracy'])
model.fit(train_images, train_labels,
batch_size=1000,
epochs=5,
verbose=1,
validation_data=(test_images, test_labels))
loss, accuracy = model.evaluate(test_images, test_labels)
print('loss: ', loss, '\naccuracy: ', accuracy)
train_images和test_images numpy arrays的大小为(50000,32,32,3)，(10000,32,32,3)和train_labels以及test_labels为numpy arrays大小为(50000,10)和(10000,10)。
我的问题：导致这种情况的原因以及我该怎么办？
我将模型更改为：
model = Sequential()
model.add(Conv2D(filters=64,
kernel_size=(3, 3),
activation='relu',
kernel_initializer='he_normal', # better for relu based networks
input_shape=(32, 32, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(filters=256,
kernel_size=(3, 3),
activation='relu',
kernel_initializer='he_normal'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(10, activation='softmax'))
现在输出：
Epoch 1/10
50000/50000 [==============================] - 326s 7ms/step - loss: 1.4916 - acc: 0.4809 - val_loss: 7.7175 - val_acc: 0.5134
Epoch 2/10
50000/50000 [==============================] - 338s 7ms/step - loss: 1.0622 - acc: 0.6265 - val_loss: 6.9945 - val_acc: 0.5588
Epoch 3/10
50000/50000 [==============================] - 326s 7ms/step - loss: 0.8957 - acc: 0.6892 - val_loss: 6.6270 - val_acc: 0.5833
Epoch 4/10
50000/50000 [==============================] - 324s 6ms/step - loss: 0.7813 - acc: 0.7271 - val_loss: 5.5790 - val_acc: 0.6474
Epoch 5/10
50000/50000 [==============================] - 327s 7ms/step - loss: 0.6690 - acc: 0.7668 - val_loss: 5.7479 - val_acc: 0.6358
Epoch 6/10
50000/50000 [==============================] - 320s 6ms/step - loss: 0.5671 - acc: 0.8031 - val_loss: 5.8720 - val_acc: 0.6302
Epoch 7/10
50000/50000 [==============================] - 328s 7ms/step - loss: 0.4865 - acc: 0.8319 - val_loss: 5.6320 - val_acc: 0.6451
Epoch 8/10
50000/50000 [==============================] - 320s 6ms/step - loss: 0.3995 - acc: 0.8611 - val_loss: 5.3879 - val_acc: 0.6615
Epoch 9/10
50000/50000 [==============================] - 320s 6ms/step - loss: 0.3337 - acc: 0.8837 - val_loss: 5.6874 - val_acc: 0.6432
Epoch 10/10
50000/50000 [==============================] - 320s 6ms/step - loss: 0.2806 - acc: 0.9033 - val_loss: 5.7424 - val_acc: 0.6399
10000/10000 [==============================] - 19s 2ms/step
loss: 5.74234927444458
accuracy: 0.6399
似乎我再次过度补充，即使我在迄今为止得到的帮助下改变了模型......任何解释或提示？
输入图像为(32,32,3) numpy数组，标准化为(0,1)
答案 0 :(得分：5)
You haven't included how you prepare the data, here's one addition that made this network learn much better:
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
If you do data normalization like that, then your network is fine: it hits ~65-70% test accuracy after 5 epochs, which is a good result. Note that 5 epochs is just a start, it would need around 30-50 epochs to really learn the data well and show a result close to state of the art.
Below are some minor improvements that I noticed and can get you extra performance points:
he_normal initializer is better than glorot_uniform (which is a default in Conv2D).256 -> 64 and 128 -> 256 and the accuracy improved.0.5 -> 0.4.3x3 is more common than 2x2. I think you should try it for the second conv layer as well. In fact, you can play with all hyper-parameters to find the best combination.Here's the final code:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(filters=64,
kernel_size=(3, 3),
activation='relu',
kernel_initializer='he_normal',
input_shape=(32, 32, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(filters=256,
kernel_size=(2, 2),
kernel_initializer='he_normal',
activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer=adam(),
loss=categorical_crossentropy,
metrics=['accuracy'])
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
model.fit(x_train, y_train,
batch_size=500,
epochs=5,
verbose=1,
validation_data=(x_test, y_test))
loss, accuracy = model.evaluate(x_test, y_test)
print('loss: ', loss, '\naccuracy: ', accuracy)
The result after 5 epochs:
loss: 0.822134458447
accuracy: 0.7126
By the way, you might be interested to compare your approach with keras example CIFAR-10 conv net.