Neural Programmer-Interpreter Implementation, in Tensorflow (with TFLearn). Based on the original Neural Programmer-Interpreter paper, by Reed and de Freitas.
A Neural Programmer-Interpreter can be decomposed into the following components (each of which are
implemented either in npi.py, or [task-name].py:
-
Neural Programmer-Interpreter Core: Simple LSTM Network (f_lstm), with hidden states h_t, c_t
- Behaves like a traffic controller ==> Based on task-specific encoder inputs, serves as a router, outputting high-dimensional information that encodes information about which subroutine to call next, and with what arguments.
- Shared across all tasks ==> only shared, central component.
-
Task-Specific Encoder Network: Architecture depends on specific task, but can also be trained via gradient descent (f_enc).
- Given the task environment, and previous subroutine arguments, generates a fixed-length state encoding s_t
-
Program Termination Network: Feed-Forward Network (f_end), takes LSTM Controller hidden state h_t and outputs a probability of terminating execution.
- Paper uses a threshold value of 0.5 to determine whether to stop, and terminate, or call next subroutine.
-
Subroutine Lookup Network: Feed-Forward Network (f_prog), takes LSTM Controller hidden state h_t and outputs a key embedding k_t to look up next subroutine to be called.
- Subroutine information is stored in two matrices, M_key (N x K), and M_prog (N x P), where each of the N rows denotes a different subroutine, and where K and P are the dimensionality of the key and program embeddings respectively.
- The next subroutine is chosen as follows:
- i* = argmax_i (M_i_key)^T k_t ==> Cosine similarity between the predicted key embedding and each of the program keys in M_key
- p_(t + 1) = M_i*_prog
- Note: To generate probabilities of next program, for training the network, I took a softmax over the array of cosine similarities, and used those as my next-program probabilities.
-
Argument Networks: Feed-Forward Networks (f_arg), takes LSTM Controller hidden state h_t and outputs subroutine arguments a_(t + 1).
- Note: In this implementation, I built separate networks for each of the three arguments.
+ model/
- npi.py: Core model definition for the Neural-Programmer Interpreter. Builds the shared
NPI LSTM Controller, the Termination Network, the Program ID Network, as well as the
specific Argument Networks.
+ tasks/
- [task-name]/
+ data/ - Contains training/test execution traces, stored in Python serialized (.pik)
format. Each .pik file contains a list of examples, where each example is
stored as a triple (in1, in2, trace), where in1/in2 are the numbers to be
added, and trace contains the specific execution trace.
+ env/ - Contains the environment/task-specific code, including any Task Configuration
parameters, trace-building helpers, etc.
+ [task-name].py - Contains model definition code for the Task-Specific Core - contains
task-specific TF placeholders, as well as the [f_enc] encoder
environment-encoder network.
+ train.py - Task-Specific Training Script - implements train_task() function.
+ eval.py - Task-Specific Evaluation Script - implements evaluate_task() function.
+ main.py - Core runner, accepts TF Flags for generating data (with specified number of
examples), training, saving, and evaluating model.
To test addition-task NPI code interactively, just call python main.py from the root of this
directory, provided that you have cloned the saved model checkpoints in tasks/addition/log/.
This will drop you into a REPL, where you can enter two numbers to be added, and step through the
predicted execution trace.
Note that for the time being, numbers much be smaller than 1000000000. This is not because of any limitations on the part of the NPI, but because of the backend helper functions that display the trace.
A sample execution trace for the addition 18 + 7 = 25 can be found below. Note that this is trace
is produced entirely by the NPI => There is are no external guides provided except for the initial
call to "ADD 18 7":
Enter Two Numbers, or Hit Enter for Random Pair: 18 7
Step: ADD, Arguments: [], Terminate: False
IN 1: -1, IN 2: -1, CARRY: -1, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000000
-----------------------
Output : 0000000000
Continue? c
Step: ADD1, Arguments: [], Terminate: False
IN 1: -1, IN 2: -1, CARRY: -1, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000000
-----------------------
Output : 0000000000
Continue? c
Step: WRITE, Arguments: [OUT, 5], Terminate: False
IN 1: -1, IN 2: -1, CARRY: -1, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000000
-----------------------
Output : 0000000005
Continue? c
Step: CARRY, Arguments: [], Terminate: False
IN 1: -1, IN 2: -1, CARRY: -1, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000000
-----------------------
Output : 0000000005
Continue? c
Step: MOVE_PTR, Arguments: [CARRY_PTR, LEFT], Terminate: False
IN 1: -1, IN 2: -1, CARRY: -1, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000000
-----------------------
Output : 0000000005
Continue? c
Step: WRITE, Arguments: [CARRY, 1], Terminate: False
IN 1: -1, IN 2: -1, CARRY: -2, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000005
Continue? c
Step: MOVE_PTR, Arguments: [CARRY_PTR, RIGHT], Terminate: False
IN 1: -1, IN 2: -1, CARRY: -2, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000005
Continue? c
Step: MOVE_PTR, Arguments: [IN1_PTR, LEFT], Terminate: False
IN 1: -1, IN 2: -1, CARRY: -1, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000005
Continue? c
Step: MOVE_PTR, Arguments: [IN2_PTR, LEFT], Terminate: False
IN 1: -2, IN 2: -1, CARRY: -1, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000005
Continue? c
Step: MOVE_PTR, Arguments: [CARRY_PTR, LEFT], Terminate: False
IN 1: -2, IN 2: -2, CARRY: -1, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000005
Continue? c
Step: MOVE_PTR, Arguments: [OUT_PTR, LEFT], Terminate: False
IN 1: -2, IN 2: -2, CARRY: -2, OUT: -1
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000005
Continue? c
Step: ADD1, Arguments: [], Terminate: False
IN 1: -2, IN 2: -2, CARRY: -2, OUT: -2
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000005
Continue? c
Step: WRITE, Arguments: [OUT, 2], Terminate: False
IN 1: -2, IN 2: -2, CARRY: -2, OUT: -2
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000025
Continue? c
Step: MOVE_PTR, Arguments: [IN1_PTR, LEFT], Terminate: False
IN 1: -2, IN 2: -2, CARRY: -2, OUT: -2
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000025
Continue? c
Step: MOVE_PTR, Arguments: [IN2_PTR, LEFT], Terminate: False
IN 1: -3, IN 2: -2, CARRY: -2, OUT: -2
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000025
Continue? c
Step: MOVE_PTR, Arguments: [CARRY_PTR, LEFT], Terminate: False
IN 1: -3, IN 2: -3, CARRY: -2, OUT: -2
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000025
Step: MOVE_PTR, Arguments: [CARRY_PTR, LEFT], Terminate: True
IN 1: -3, IN 2: -3, CARRY: -3, OUT: -2
Input 1: 0000000018
Input 2: 0000000007
Carry : 0000000010
-----------------------
Output : 0000000025
Model Output: 18 + 7 = 25
Correct Out : 18 + 7 = 25
Correct!