The comprehensible data shape to me is like: (9186, 120, 120, 1) this means 9186 entry, of 120 by 120 pixel grey images. I learnt that using Time Distributed to design a CNN combined with an LSTM model, could learn more from images, knowing they are sequenced.
Following some tutorials, I found out I should expend another dimension which describes moving frames, in my case, I have 6 hours image, this would be good as a moving sequence, (probably I should try other lengths because time-series images are not of regular lengths, example:
8:00 img1 target1 9:00 img2 target2 10:00 img3 target3 11:00 img4 target4 12:00 img5 target5 13:00 img6 target6 19:00 img7 target7 ...
My question is, when I expend another dimension of lets say length 6, how could such a model here output for every entrie exactly one prediction, but using a frame of length 6 to learn the next ?
train_images.reshape((1531, 6, 120, 120, 1)).shape
=> This gives me the impression, that such a model would output a prediction for a sequence of 6, which means 1531 result.
Am I understanding it wrong ?
[Edit following wind's answer]
I think my problem persists but I was not aware, I declared the first layer as
Conv2D(64, (3, 3), activation='relu'),
input_shape=(6, 120, 120, 1)
for x_train as
train_images_ = train_images.reshape((1531, 6, 120, 120, 1))
while y_train declared as
y_train_ = y_train.reshape((1531, 6, 8, 1))
as you can see
y_train have the same length, with a frame of 6, image_length of 120, while, y_train consists of 8 target columns.
To my understanding, this is the way to follow with tensorflow, unfortuanatly, this is the error I am getting...
ValueError: Error when checking target: expected dense_2 to have 2 dimensions, but got array with shape (1531, 6, 8, 1)