Why does my Keras TimeDistributed CNN + LSTM model expect an incomplete shape

Issue

I am building a small CNN LSTM model in Keras for practice with video classification. The input dimensions of my data are (1, 5, 30, 10, 3) (batch size, time steps, width, height, channels). I found some success with ConvLSTM2D, but I’d like to make a model using TimeDistributed as I want to compare the performance of LSTM versus GRU.

The model successfully trains (albeit with some very strange accuracies),

https://i.imgur.com/5uAbPkR.png

but when I save it to my computer and call model.predict on an array of dimensions (1, 5, 30, 10, 3), I get this error:

ValueError: Input 0 is incompatible with layer sequential_12: expected shape=(None, None, 10, 30, 3), found shape=(1, 5, 30, 10, 3)

This happens even using images from the training set it supposedly achieved 100% accuracy on.

I am quite new to machine learning so it’s likely I overlooked something simple, but after scouring stackoverflow and Google all day for any leads I am getting nowhere.

The model looks like this.

model = Sequential()
model.add(Input((5,10,30,3)))

model.add(TimeDistributed(Conv2D(filters=2,  
                                 kernel_size=(3,3), 
                                 padding='same', 
                                 activation='relu')))
model.add(TimeDistributed(Flatten()))

model.add(LSTM(4))

model.add(Dense(16, activation='sigmoid'))
model.add(Dense(2, activation='softmax'))

sgd = SGD(learning_rate=0.01)

model.compile(optimizer=sgd,
              loss='binary_crossentropy',
              metrics=['accuracy'])

Shouldn’t the expected input shape be the same as the first Input layer I used in training?

Solution

I’m even more newbie than you and probably giving the wrong advice.
If I understood correctly, if I were you, I would try

frame = frame [None, …]

for what needs to be predicted. It helped me once.

I would not have written this if I had not been doing a similar task. I am very interested in your experience and I can share mine. Do you want to discuss this issue?

Answered By – Михаил Юрченко

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