antirectifier.md

Simple custom layer example: Antirectifier

Author: fchollet
Date created: 2016/01/06
Last modified: 2023/11/20
Description: Demonstration of custom layer creation.

View in Colab • GitHub source

---

Introduction

This example shows how to create custom layers, using the Antirectifier layer (originally proposed as a Keras example script in January 2016), an alternative to ReLU. Instead of zeroing-out the negative part of the input, it splits the negative and positive parts and returns the concatenation of the absolute value of both. This avoids loss of information, at the cost of an increase in dimensionality. To fix the dimensionality increase, we linearly combine the features back to a space of the original size.

---

Setup

import keras
from keras import layers
from keras import ops

---

The Antirectifier layer

To implement a custom layer:

• Create the state variables via add_weight() in __init__ or build(). Similarly, you can also create sublayers.

• Implement the call() method, taking the layer's input tensor(s) and return the output tensor(s).

• Optionally, you can also enable serialization by implementing get_config(), which returns a configuration dictionary.

See also the guide Making new layers and models via subclassing.

class Antirectifier(layers.Layer):
    def __init__(self, initializer="he_normal", **kwargs):
        super().__init__(**kwargs)
        self.initializer = keras.initializers.get(initializer)

    def build(self, input_shape):
        output_dim = input_shape[-1]
        self.kernel = self.add_weight(
            shape=(output_dim * 2, output_dim),
            initializer=self.initializer,
            name="kernel",
            trainable=True,
        )

    def call(self, inputs):
        inputs -= ops.mean(inputs, axis=-1, keepdims=True)
        pos = ops.relu(inputs)
        neg = ops.relu(-inputs)
        concatenated = ops.concatenate([pos, neg], axis=-1)
        mixed = ops.matmul(concatenated, self.kernel)
        return mixed

    def get_config(self):
        # Implement get_config to enable serialization. This is optional.
        base_config = super().get_config()
        config = {"initializer": keras.initializers.serialize(self.initializer)}
        return dict(list(base_config.items()) + list(config.items()))

---

Let's test-drive it on MNIST

# Training parameters
batch_size = 128
num_classes = 10
epochs = 20

# The data, split between train and test sets
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

x_train = x_train.reshape(-1, 784)
x_test = x_test.reshape(-1, 784)
x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
x_train /= 255
x_test /= 255
print(x_train.shape[0], "train samples")
print(x_test.shape[0], "test samples")

# Build the model
model = keras.Sequential(
    [
        keras.Input(shape=(784,)),
        layers.Dense(256),
        Antirectifier(),
        layers.Dense(256),
        Antirectifier(),
        layers.Dropout(0.5),
        layers.Dense(10),
    ]
)

# Compile the model
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.RMSprop(),
    metrics=[keras.metrics.SparseCategoricalAccuracy()],
)

# Train the model
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.15)

# Test the model
model.evaluate(x_test, y_test)

``` 60000 train samples 10000 test samples Epoch 1/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 2s 5ms/step - loss: 0.6226 - sparse_categorical_accuracy: 0.8146 - val_loss: 0.4256 - val_sparse_categorical_accuracy: 0.8808 Epoch 2/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.1887 - sparse_categorical_accuracy: 0.9455 - val_loss: 0.1556 - val_sparse_categorical_accuracy: 0.9588 Epoch 3/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.1406 - sparse_categorical_accuracy: 0.9608 - val_loss: 0.1531 - val_sparse_categorical_accuracy: 0.9611 Epoch 4/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.1084 - sparse_categorical_accuracy: 0.9691 - val_loss: 0.1178 - val_sparse_categorical_accuracy: 0.9731 Epoch 5/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0995 - sparse_categorical_accuracy: 0.9738 - val_loss: 0.2207 - val_sparse_categorical_accuracy: 0.9526 Epoch 6/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0831 - sparse_categorical_accuracy: 0.9769 - val_loss: 0.2092 - val_sparse_categorical_accuracy: 0.9533 Epoch 7/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0736 - sparse_categorical_accuracy: 0.9807 - val_loss: 0.1129 - val_sparse_categorical_accuracy: 0.9749 Epoch 8/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0653 - sparse_categorical_accuracy: 0.9827 - val_loss: 0.1000 - val_sparse_categorical_accuracy: 0.9791 Epoch 9/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0590 - sparse_categorical_accuracy: 0.9833 - val_loss: 0.1320 - val_sparse_categorical_accuracy: 0.9750 Epoch 10/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0587 - sparse_categorical_accuracy: 0.9854 - val_loss: 0.1439 - val_sparse_categorical_accuracy: 0.9747 Epoch 11/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0622 - sparse_categorical_accuracy: 0.9853 - val_loss: 0.1473 - val_sparse_categorical_accuracy: 0.9753 Epoch 12/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0554 - sparse_categorical_accuracy: 0.9869 - val_loss: 0.1529 - val_sparse_categorical_accuracy: 0.9757 Epoch 13/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0507 - sparse_categorical_accuracy: 0.9884 - val_loss: 0.1452 - val_sparse_categorical_accuracy: 0.9783 Epoch 14/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0468 - sparse_categorical_accuracy: 0.9889 - val_loss: 0.1435 - val_sparse_categorical_accuracy: 0.9796 Epoch 15/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9892 - val_loss: 0.1580 - val_sparse_categorical_accuracy: 0.9770 Epoch 16/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0492 - sparse_categorical_accuracy: 0.9888 - val_loss: 0.1957 - val_sparse_categorical_accuracy: 0.9753 Epoch 17/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9896 - val_loss: 0.1865 - val_sparse_categorical_accuracy: 0.9779 Epoch 18/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0478 - sparse_categorical_accuracy: 0.9893 - val_loss: 0.2107 - val_sparse_categorical_accuracy: 0.9747 Epoch 19/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0494 - sparse_categorical_accuracy: 0.9894 - val_loss: 0.2306 - val_sparse_categorical_accuracy: 0.9734 Epoch 20/20 399/399 ━━━━━━━━━━━━━━━━━━━━ 1s 4ms/step - loss: 0.0473 - sparse_categorical_accuracy: 0.9910 - val_loss: 0.2201 - val_sparse_categorical_accuracy: 0.9731 313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 802us/step - loss: 0.2086 - sparse_categorical_accuracy: 0.9710

[0.19070196151733398, 0.9740999937057495]

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