ChatGPT 3.5 provides the following answer:

Certainly! Bit-packing is a technique to efficiently store binary data by packing multiple binary values into a single byte. This can be useful when dealing with large datasets of binary inputs, as it can reduce memory usage. In this example, I’ll show you how to bit-pack binary inputs and then unpack them in batches using TensorFlow for training.

Let’s assume you have a dataset of binary inputs represented as arrays of 0s and 1s. Here’s how you can perform bit-packing and unpacking using TensorFlow:

import numpy as np

import tensorflow as tf

#
Generate some random binary data

num_samples = 1000

input_size = 32

binary_data = np.random.randint(0, 2, size=(num_samples, input_size), dtype=np.uint8)

#
Function to bit-pack binary data

def bit_pack(data):

packed_data = []

current_byte = 0

bit_count = 0

```
for bit in data:
current_byte |= (bit << bit_count)
bit_count += 1
if bit_count == 8:
packed_data.append(current_byte)
current_byte = 0
bit_count = 0
if bit_count > 0:
packed_data.append(current_byte)
return np.array(packed_data, dtype=np.uint8)
```

#
Function to unpack bit-packed data

def bit_unpack(packed_data, original_size):

unpacked_data = []

```
for byte in packed_data:
for _ in range(8):
unpacked_data.append(byte & 1)
byte >>= 1
return np.array(unpacked_data[:original_size], dtype=np.uint8)
```

#
Bit-pack the binary data

packed_binary_data = np.array([bit_pack(sample) for sample in binary_data])

#
Create a TensorFlow dataset

batch_size = 32

dataset = tf.data.Dataset.from_tensor_slices((packed_binary_data, binary_data))

dataset = dataset.batch(batch_size)

#
Example usage in a training loop

for packed_batch, original_batch in dataset:

unpacked_batch = np.array([bit_unpack(packed_sample, input_size) for packed_sample in packed_batch.numpy()])

```
# Now you can use the unpacked_batch for training
# ...
```