Question

我正试图在Keras中对预先训练好的Inceptionv3进行微调，以解决多标签（17）预测问题。

以下是代码：

# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)

# add a new top layer
x = base_model.output
predictions = Dense(17, activation='sigmoid')(x)

# this is the model we will train
model = Model(inputs=base_model.input, outputs=predictions)

# we need to recompile the model for these modifications to take effect
# we use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(loss='binary_crossentropy', # We NEED binary here, since categorical_crossentropy l1 norms the output before calculating loss.
              optimizer=SGD(lr=0.0001, momentum=0.9))

# Fit the model (Add history so that the history may be saved)
history = model.fit(x_train, y_train,
          batch_size=128,
          epochs=1,
          verbose=1,
          callbacks=callbacks_list,
          validation_data=(x_valid, y_valid))

但是我遇到了以下错误消息，并且无法破译它所说的内容：

ValueError：检查目标时出错：期望dense_1有4 尺寸，但得到形状的数组（1024,17）

它似乎与某些事情有关，它不喜欢我作为目标的标签的单热编码。但是如何获得4个维度？

Answer 1

事实证明，从https://keras.io/applications/复制的代码不会是开箱即用的。以下帖子帮助了我： Keras VGG16 fine tuning

我需要做出的改变如下：

将输入形状添加到模型定义base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(299,299,3))和
添加Flatten（）图层以展平张量输出： x = base_model.output x = Flatten()(x) predictions = Dense(17, activation='sigmoid')(x)

然后该模型适合我！

在Keras中微调Inception_v3时的ValueError

1 个答案: