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KerasCodeExamples.txt

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Prediction mean: 6.39, stddev: 0.78, 95% CI: [7.92 - 4.85] - Actual: 7.0
Demonstration of custom layer creation.
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 tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
The Antirectifier layer
class Antirectifier(layers.Layer):
def __init__(self, initializer=\"he_normal\", **kwargs):
super(Antirectifier, self).__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 -= tf.reduce_mean(inputs, axis=-1, keepdims=True)
pos = tf.nn.relu(inputs)
neg = tf.nn.relu(-inputs)
concatenated = tf.concat([pos, neg], axis=-1)
mixed = tf.matmul(concatenated, self.kernel)
return mixed
def get_config(self):
# Implement get_config to enable serialization. This is optional.
base_config = super(Antirectifier, self).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: 0.3827 - sparse_categorical_accuracy: 0.8882 - val_loss: 0.1407 - val_sparse_categorical_accuracy: 0.9587
Epoch 2/20
399/399 [==============================] - 2s 5ms/step - loss: 0.1771 - sparse_categorical_accuracy: 0.9513 - val_loss: 0.1337 - val_sparse_categorical_accuracy: 0.9674
Epoch 3/20
399/399 [==============================] - 2s 5ms/step - loss: 0.1400 - sparse_categorical_accuracy: 0.9620 - val_loss: 0.1225 - val_sparse_categorical_accuracy: 0.9709
Epoch 4/20
399/399 [==============================] - 2s 5ms/step - loss: 0.1099 - sparse_categorical_accuracy: 0.9707 - val_loss: 0.1465 - val_sparse_categorical_accuracy: 0.9636
Epoch 5/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0996 - sparse_categorical_accuracy: 0.9739 - val_loss: 0.1703 - val_sparse_categorical_accuracy: 0.9626
Epoch 6/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0860 - sparse_categorical_accuracy: 0.9774 - val_loss: 0.1354 - val_sparse_categorical_accuracy: 0.9712
Epoch 7/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0833 - sparse_categorical_accuracy: 0.9791 - val_loss: 0.2018 - val_sparse_categorical_accuracy: 0.9574
Epoch 8/20
399/399 [==============================] - 2s 5ms/step - loss: 0.0712 - sparse_categorical_accuracy: 0.9814 - val_loss: 0.1527 - val_sparse_categorical_accuracy: 0.9723