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Tensorflow autoencoder sample project that learns basic emboss effect

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Tensorflow Autoencoder

A Tensorflow convolutional autoencoder sample with pretrained models. I trined my example to learn basic emboss effect samples.

Models

Encoder model with 3 convolutional layers to extract features vector

Model: "EncoderModel" Input and 3 convolutional layers
_________________________________________________________________
Layer (type)                 Output Shape              Param #
_________________________________________________________________
InputLayer (InputLayer)      [(None, 256, 256, 3)]     0
_________________________________________________________________
conv2d (Conv2D)              (None, 256, 256, 16)      448
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 128, 128, 16)      0
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 128, 128, 16)      2320
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 64, 64, 16)        0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 64, 64, 16)        2320
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 32, 32, 16)        0
_________________________________________________________________
Total params: 5,088
Trainable params: 5,088
Non-trainable params: 0

Decoder Model to reverse and upscale latent space

_________________________________________________________________
Model: "DecoderModel"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
_________________________________________________________________
LatentSpace (InputLayer)     [(None, 32, 32, 16)]      0
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 32, 32, 16)        2320
_________________________________________________________________
up_sampling2d (UpSampling2D) (None, 64, 64, 16)        0
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 64, 64, 16)        2320      
_________________________________________________________________
up_sampling2d_1 (UpSampling2 (None, 128, 128, 16)      0
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 128, 128, 16)      2320
_________________________________________________________________
up_sampling2d_2 (UpSampling2 (None, 256, 256, 16)      0
_________________________________________________________________
OutputLayer (Conv2D)         (None, 256, 256, 3)       435
_________________________________________________________________
Total params: 7,395
Trainable params: 7,395
Non-trainable params: 0

Final autoencoder model that connects encoder and decoder

_________________________________________________________________
Model: "Autoencoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
_________________________________________________________________
InputLayer (InputLayer)      [(None, 256, 256, 3)]     0
_________________________________________________________________
EncoderModel (Model)         (None, 32, 32, 16)        5088
_________________________________________________________________
DecoderModel (Model)         (None, 256, 256, 3)       7395
_________________________________________________________________
Total params: 12,483
Trainable params: 12,483
Non-trainable params: 0

Training

I trained my instance of autoEncoder.h5 model to learn emboss effect. Donwloaded flower pictures from https://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html. Resized these pictures by 256x256 and stored them in img/x folder. Applied emboss effect on each image and stored them in img/y folder with same file names (emboss.py).

Original Embossed

Runned trainer.py and trained models for 8189 image instances over 10 epochs and here is the final result.

Actual Target Predicted