BinaryNet on CIFAR10¶
In this example we demonstrate how to use Larq to build and train BinaryNet on the CIFAR10 dataset to achieve a validation accuracy approximately 83% on laptop hardware. On a Nvidia GTX 1050 Ti Max-Q it takes approximately 200 minutes to train. For simplicity, compared to the original papers BinaryConnect: Training Deep Neural Networks with binary weights during propagations, and Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, we do not impliment learning rate scaling, or image whitening.
pip install larq
import tensorflow as tf
import larq as lq
import numpy as np
import matplotlib.pyplot as plt
Import CIFAR10 Dataset¶
We download and normalize the CIFAR10 dataset.
num_classes = 10
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()
train_images = train_images.reshape((50000, 32, 32, 3)).astype("float32")
test_images = test_images.reshape((10000, 32, 32, 3)).astype("float32")
# Normalize pixel values to be between -1 and 1
train_images, test_images = train_images / 127.5 - 1, test_images / 127.5 - 1
train_labels = tf.keras.utils.to_categorical(train_labels, num_classes)
test_labels = tf.keras.utils.to_categorical(test_labels, num_classes)
Build BinaryNet¶
Here we build the BinaryNet model layer by layer using the Keras Sequential API.
# All quantized layers except the first will use the same options
kwargs = dict(input_quantizer="ste_sign",
kernel_quantizer="ste_sign",
kernel_constraint="weight_clip",
use_bias=False)
model = tf.keras.models.Sequential([
# In the first layer we only quantize the weights and not the input
lq.layers.QuantConv2D(128, 3,
kernel_quantizer="ste_sign",
kernel_constraint="weight_clip",
use_bias=False,
input_shape=(32, 32, 3)),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
lq.layers.QuantConv2D(128, 3, padding="same", **kwargs),
tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
lq.layers.QuantConv2D(256, 3, padding="same", **kwargs),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
lq.layers.QuantConv2D(256, 3, padding="same", **kwargs),
tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
lq.layers.QuantConv2D(512, 3, padding="same", **kwargs),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
lq.layers.QuantConv2D(512, 3, padding="same", **kwargs),
tf.keras.layers.MaxPool2D(pool_size=(2, 2), strides=(2, 2)),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
tf.keras.layers.Flatten(),
lq.layers.QuantDense(1024, **kwargs),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
lq.layers.QuantDense(1024, **kwargs),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
lq.layers.QuantDense(10, **kwargs),
tf.keras.layers.BatchNormalization(momentum=0.999, scale=False),
tf.keras.layers.Activation("softmax")
])
One can output a summary of the model:
model.compile(
tf.keras.optimizers.Adam(lr=0.01, decay=0.0001),
loss="categorical_crossentropy",
metrics=["accuracy"],
)
trained_model = model.fit(
train_images,
train_labels,
batch_size=50,
epochs=100,
validation_data=(test_images, test_labels),
shuffle=True
)
Model Output¶
We can now plot the final validation accuracy and loss:
plt.plot(trained_model.history['acc'])
plt.plot(trained_model.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
print(np.max(trained_model.history['acc']))
print(np.max(trained_model.history['val_acc']))
plt.plot(trained_model.history['loss'])
plt.plot(trained_model.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
print(np.min(trained_model.history['loss']))
print(np.min(trained_model.history['val_loss']))
