Data Science Asked on August 29, 2021
Here is where I’m at. I built and trained an autoencoder in tensorflow. The model summary looks like so:
Model: "model_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_3 (InputLayer) [(None, None, None, 3)] 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, None, None, 4) 112
_________________________________________________________________
conv2d_5 (Conv2D) (None, None, None, 16) 592
_________________________________________________________________
dense_5 (Dense) (None, None, None, 32) 544
_________________________________________________________________
dense_6 (Dense) (None, None, None, 8) 264
_________________________________________________________________
dense_7 (Dense) (None, None, None, 32) 288
_________________________________________________________________
conv2d_transpose_2 (Conv2DTr (None, None, None, 16) 4624
_________________________________________________________________
conv2d_transpose_3 (Conv2DTr (None, None, None, 3) 435
=================================================================
Total params: 6,859
Trainable params: 6,859
Non-trainable params: 0
_________________________________________________________________
The layer ‘dense_6’ is my latent space. Now, I am passing through a (256,256,3) image into the encoder part and am getting out (1, 252, 252, 8) tensor. Obviously, the output shape is listed as (None, None, None, 8), so it makes sense that I am getting this tensor out, but I was expecting to get just an 8-dimensional vector out.
Did I design my autoencoder wrong maybe? I’m quite confused and could definitely use some clarification. Thanks!
Found out what I was doing wrong. Yes, I built the autoencoder wrong. I didn't think about how I need to flatten the tensor before passing it into a dense layer. Important step but easy to forget...
Here is my new model summary in case someone else needs some guidance:
Model: "model_9"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_8 (InputLayer) [(None, 256, 256, 3)] 0
_________________________________________________________________
conv2d_14 (Conv2D) (None, 254, 254, 4) 112
_________________________________________________________________
conv2d_15 (Conv2D) (None, 252, 252, 16) 592
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 126, 126, 16) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 254016) 0
_________________________________________________________________
dense_16 (Dense) (None, 16) 4064272
_________________________________________________________________
latent (Dense) (None, 8) 136
_________________________________________________________________
dense_17 (Dense) (None, 16) 144
_________________________________________________________________
reshape_4 (Reshape) (None, 256, 256, 3) 0
_________________________________________________________________
conv2d_transpose_11 (Conv2DT (None, 258, 258, 16) 448
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 129, 129, 16) 0
_________________________________________________________________
conv2d_transpose_12 (Conv2DT (None, 131, 131, 3) 435
=================================================================
Total params: 4,066,139
Trainable params: 4,066,139
Non-trainable params: 0
_________________________________________________________________
Answered by Tim on August 29, 2021
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