使用Keras VGG16的工作-故障输出形状

Question

I am trying to train the VGG16 model (from keras.applications) from scratch and I get a weird error

X_train shape is (73257, 48, 48, 3)

Y_train shape is (73257, 10)

I have no idea what's going on... I assume it has to do with the conv layer before it but since I'm importing the model directly from keras I'm having issues figuring out where I went wrong.

My dataset is comprised of 73,257 images which have (48,48,3) shape. I am essentially trying to do character recognition (think mnist flavor) but I am getting hung up on feeding this through the model (with weights set to 0).

model = keras.applications.vgg16.VGG16(include_top=False,
                                       weights=None,
                                       input_shape=(48, 48, 3),
                                       input_tensor=None, pooling='avg', classes=10)
sgd = SGD(lr=.1)

model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])
print('X_train shape is {}'.format(X_train.shape))
print('Y_train shape is {}'.format(y_train.shape))

model.fit(X_train, y_train,
          epochs=20,
          batch_size=128)

score = model.evaluate(X_test, y_test, batch_size=128)

This is the error I am getting

File "/home/codebrotherone/PycharmProjects/Computer Vision/deep_neural/dnn.py", line 169, in VGG16 batch_size=128)

File "/home/codebrotherone/anaconda2/envs/tensorflow/lib/python3.4/site-packages/keras/engine/training.py", line 1574, in fit batch_size=batch_size)

File "/home/codebrotherone/anaconda2/envs/tensorflow/lib/python3.4/site-packages/keras/engine/training.py", line 1411, in _standardize_user_data exception_prefix='target')

File "/home/codebrotherone/anaconda2/envs/tensorflow/lib/python3.4/site-packages/keras/engine/training.py", line 141, in _standardize_input_data str(array.shape))

ValueError: Error when checking target: expected block5_pool to have 4 dimensions, but got array with shape (73257, 10)

Answer 1

Ideally, for classification problems, you should have include_top=True and classes=10 .

This is enough. Since you're not including top, and are using a global pooling, you should end up with something like (73257,512). But the message you're getting suggests that you have not used the pooling in this attempt. Something is not quite matching.

Anyway, go with this:

model = keras.applications.vgg16.VGG16(include_top=True,
                                   input_shape=(48, 48, 3),
                                   classes=10)