Neural networks can be augmented with external memory systems to increase their memory capacity and allow them to perform tasks that require long-term memory, such as sequence-to-sequence tasks or navigation. Two popular memory-augmented neural network architectures are neural Turing machines (NTMs) and memory-augmented neural networks (MANNs).
In TensorFlow, these architectures can be implemented using the tf.raw_ops module, which allows for more flexibility in defining custom operations. Here’s an example of implementing a basic NTM architecture in TensorFlow:
import tensorflow as tf
class NTM(tf.keras.Model):
def __init__(self, num_units, memory_size, memory_dim, output_dim):
super(NTM, self).__init__()
# Initialize controller
self.controller = tf.keras.layers.Dense(num_units)
# Initialize memory
self.memory = tf.Variable(tf.zeros((memory_size, memory_dim)))
# Initialize read and write heads
self.read_head = tf.Variable(tf.zeros(memory_dim))
self.write_head = tf.Variable(tf.zeros(memory_dim))
# Initialize output layer
self.output_layer = tf.keras.layers.Dense(output_dim)
def call(self, inputs):
# Split inputs into input vector and control signal
input_vector, control_signal = tf.split(inputs, [self.memory_dim, self.num_units])
# Compute control signal with controller
control_signal = self.controller(control_signal)
# Compute read weights using content-based addressing
content_similarity = tf.matmul(self.memory, input_vector, transpose_b=True)
read_weights = tf.nn.softmax(content_similarity)
# Read from memory using read weights
read_vector = tf.reduce_sum(read_weights[:, tf.newaxis, :] * self.memory, axis=-1)
# Compute write weights using content-based and location-based addressing
content_similarity = tf.matmul(self.memory, input_vector, transpose_b=True)
location_similarity = tf.matmul(self.memory, self.write_head, transpose_b=True)
write_weights = tf.nn.softmax(content_similarity + location_similarity)
# Write to memory using write weights and input vector
self.memory.assign(tf.reduce_sum(write_weights[:, tf.newaxis, :] * input_vector[:, tf.newaxis, :], axis=0))
# Update read and write heads
self.read_head.assign(tf.reduce_sum(read_weights[:, tf.newaxis, :] * self.memory, axis=0))
self.write_head.assign(tf.reduce_sum(write_weights[:, tf.newaxis, :] * input_vector[:, tf.newaxis, :], axis=0))
# Concatenate read head and control signal and pass through output layer
output = self.output_layer(tf.concat([self.read_head, control_signal], axis=-1))
return outputIn this example, we define an NTM model as a subclass of tf.keras.Model. The model consists of a controller, a memory bank, and read and write heads. The controller is a fully connected layer that takes the control signal as input and produces a control signal for the memory. The memory bank is represented as a tensor of size (memory_size, memory_dim), where memory_size is the number of memory slots and memory_dim is the dimensionality of each slot. The read and write heads are vectors of size memory_dim that are used to read from and write to the memory bank.
The call method of the model takes an input vector and a control signal as input. The input vector is used to read from and write to the memory bank, while the control signal is passed through the controller to produce a control signal for the memory. The input vector is split into two parts: one part is used for content-based addressing to compute the read and write weights, and the other part is used to