Training a DCGAN to resemble tf_flowers dataset. It is an implementation of the architecture that is used in Matlab help in TensorFlow. It is based on the DCGAN implementation tutorial on TensorFlow website.
The generator creates 64x64 pixels flower images.
You can also checkout the outputs during training or watch how the outputs evolved to resemble flowers in this huge GIF file (may take some time to load).
The generator and discriminator networks are based on this experiment. Models details are as follows:
In [1] : generator.summary()
Out [1]: Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 8192) 827392
reshape (Reshape) (None, 4, 4, 512) 0
conv2d_transpose (Conv2DTra (None, 8, 8, 256) 3277056
nspose)
batch_normalization (BatchN (None, 8, 8, 256) 1024
ormalization)
re_lu (ReLU) (None, 8, 8, 256) 0
conv2d_transpose_1 (Conv2DT (None, 16, 16, 128) 819328
ranspose)
batch_normalization_1 (Batc (None, 16, 16, 128) 512
hNormalization)
re_lu_1 (ReLU) (None, 16, 16, 128) 0
conv2d_transpose_2 (Conv2DT (None, 32, 32, 64) 204864
ranspose)
batch_normalization_2 (Batc (None, 32, 32, 64) 256
hNormalization)
re_lu_2 (ReLU) (None, 32, 32, 64) 0
conv2d_transpose_3 (Conv2DT (None, 64, 64, 3) 4803
ranspose)
=================================================================
Total params: 5,135,235
Trainable params: 5,134,339
Non-trainable params: 896
_________________________________________________________________In [2] : discriminator.summary()
Out [2]: Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
random_flip (RandomFlip) (None, 64, 64, 3) 0
dropout (Dropout) (None, 64, 64, 3) 0
conv2d (Conv2D) (None, 32, 32, 64) 4864
leaky_re_lu (LeakyReLU) (None, 32, 32, 64) 0
conv2d_1 (Conv2D) (None, 16, 16, 64) 409664
batch_normalization_3 (Batc (None, 16, 16, 64) 256
hNormalization)
leaky_re_lu_1 (LeakyReLU) (None, 16, 16, 64) 0
conv2d_2 (Conv2D) (None, 8, 8, 64) 1638464
batch_normalization_4 (Batc (None, 8, 8, 64) 256
hNormalization)
leaky_re_lu_2 (LeakyReLU) (None, 8, 8, 64) 0
conv2d_3 (Conv2D) (None, 4, 4, 64) 6553664
batch_normalization_5 (Batc (None, 4, 4, 64) 256
hNormalization)
leaky_re_lu_3 (LeakyReLU) (None, 4, 4, 64) 0
conv2d_4 (Conv2D) (None, 2, 2, 1) 1025
flatten (Flatten) (None, 4) 0
dense_1 (Dense) (None, 1) 5
=================================================================
Total params: 8,608,454
Trainable params: 8,608,070
Non-trainable params: 384
_________________________________________________________________Training the models in Google Colab using the notebook is straightforward. You can also use TensorBoard to monitor generator and discriminator balance during training. The following image shows the network in balance (check here for more details and examples).
For inference, train the model or download the pretrained model from here, and use the following lines.
# creating noise vector
noise = tf.random.normal([num_examples_to_generate, noise_dim])
# builing the generator model
gen = make_generator_model()
# loading model's weights
gen.load_weights('gen.h5')
# generating images
predictions = gen(noise, training=False)