I am fine-tuning a MobileNet with 14 new classes. When I add new layers by:
x=mobile.layers[-6].output
x=Flatten(x)
predictions = Dense(14, activation='softmax')(x)
model = Model(inputs=mobile.input, outputs=predictions)
I get the error:
'Tensor' object has no attribute 'lower'
Also using:
model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy'])
model.fit_generator(train_batches, steps_per_epoch=18,
validation_data=valid_batches, validation_steps=3, epochs=60, verbose=2)
I get the error:
Error when checking target: expected dense_1 to have 4 dimensions, but got array with shape (10, 14)
What does lower
mean? I saw other fine-tuning scripts and there were no other arguments other than the name of the model which is x
in this case.
The tensor must be passed to the layer when you are calling it, and not as an argument. Therefore it must be like this:
x = Flatten()(x) # first the layer is constructed and then it is called on x
To make it more clear, it is equivalent to this:
flatten_layer = Flatten() # instantiate the layer
x = flatten_layer(x) # call it on the given tensor