Conv-Mixer custom layer implementation in Tensorflow/Keras. ConvMixer is introduced in ICLR 2022 submission "Patches Are All You Need?" by Asher Trockman and Zico Kolter.
Feel free to make pull requests & suggestions. 😀
Example code is provided in example.py file.
First layer should be patcher, so patches can be extracted
inputs = tf.keras.layers.Input((w, h, c))
patches = Patcher(filters = 128, patch_size = (4, 4))(inputs)Conv mixer layers are added sequentially with a certain "depth"
t = patches
for i in range(depth):
t = ConvMixer(256, (3, 3), (1, 1), "gelu")(t)Then the features are ready to feed other layers. In this example i used Global Average Pooling and FC to perform classsification.
final_conv = tf.keras.layers.Conv2D(n_classes,
kernel_size=1,
strides=1,
padding="same",
activation="relu")(t)
outputs = tf.keras.layers.GlobalAveragePooling2D()(final_conv)
outputs = tf.keras.layers.Dense(n_classes, "softmax")(outputs)
model = tf.keras.Model(inputs, outputs)
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