[Nengo-DL]: How to do batch training of a nengo-dl model?

Deep Learning
3

In order to use data generator for sim.fit, it would be required to specify data for input, output, n_steps, and all the bias nodes. Data for bias nodes can be specified as an array of ones in the shape of the bias and n_steps would be of shape batchsize x 1.

Here is an example of sim.fit with a data generator:


from urllib.request import urlretrieve

import matplotlib.pyplot as plt
import nengo
import numpy as np
import tensorflow as tf

import nengo_dl

seed = 0
np.random.seed(seed)
tf.random.set_seed(seed)

(train_images, train_labels), (test_images, test_labels) = (
    tf.keras.datasets.mnist.load_data())
# flatten images and add time dimension
train_images = train_images.reshape((train_images.shape[0], 1, -1))
train_labels = train_labels.reshape((train_labels.shape[0], 1, -1))
test_images = test_images.reshape((test_images.shape[0], 1, -1))
test_labels = test_labels.reshape((test_labels.shape[0], 1, -1))


n_steps = 200
scale_firing_rate = 100
synapse = 0.005
batchsize = 128
inp = tf.keras.Input(shape=(28, 28, 1))

# convolutional layers
conv0 = tf.keras.layers.Conv2D(
    filters=32,
    kernel_size=3,
    activation=tf.nn.relu,
)(inp)

conv1 = tf.keras.layers.Conv2D(
    filters=64,
    kernel_size=3,
    strides=2,
    activation=tf.nn.relu,
)(conv0)

# fully connected layer
flatten = tf.keras.layers.Flatten()(conv1)
dense = tf.keras.layers.Dense(units=10)(flatten)

model = tf.keras.Model(inputs=inp, outputs=dense)
model.summary()
converter = nengo_dl.Converter(model)

def get_batches():
    for i in range(0,10000,batchsize):
        ip = train_images[i:i+batchsize]
        label = train_labels[i:i+batchsize]
        yield ({'input_1': ip, 
                "n_steps": np.ones((batchsize, 1), dtype=np.int32), 
                "conv2d.0.bias":np.ones((batchsize, 32, 1), dtype=np.int32),
                "conv2d_1.0.bias":np.ones((batchsize, 64, 1), dtype=np.int32),
                "dense.0.bias":np.ones((batchsize, 10, 1), dtype=np.int32)},
                {'probe':label})
data_generator = get_batches()


with nengo_dl.Simulator(converter.net, minibatch_size=batchsize) as sim:
    # run training
    sim.compile(
        optimizer=tf.optimizers.RMSprop(0.001),
        loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
        metrics=[tf.metrics.sparse_categorical_accuracy],
    )
    sim.fit(
        data_generator,
        epochs=2,
        steps_per_epoch=10,
    )
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