Modify copy task and readme

README.md

@@ -1,4 +1,9 @@
-# Differentiable Neural Computers and Sparse Differentiable Neural Computers, for Pytorch
+# Differentiable Neural Computers and family, for Pytorch
+
+Includes:
+1. Differentiable Neural Computers (DNC)
+2. Sparse Access Memory (SAM)
+3. Sparse Differentiable Neural Computers (SDNC)
 
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@@ -22,7 +27,7 @@
 [![Build Status](https://travis-ci.org/ixaxaar/pytorch-dnc.svg?branch=master)](https://travis-ci.org/ixaxaar/pytorch-dnc) [![PyPI version](https://badge.fury.io/py/dnc.svg)](https://badge.fury.io/py/dnc)
 
 This is an implementation of [Differentiable Neural Computers](http://people.idsia.ch/~rupesh/rnnsymposium2016/slides/graves.pdf), described in the paper [Hybrid computing using a neural network with dynamic external memory, Graves et al.](https://www.nature.com/articles/nature20101)
-and the Sparse version of the DNC (the SDNC) described in [Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes](http://papers.nips.cc/paper/6298-scaling-memory-augmented-neural-networks-with-sparse-reads-and-writes.pdf).
+and Sparse DNCs (SDNCs) and Sparse Access Memory (SAM) described in [Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes](http://papers.nips.cc/paper/6298-scaling-memory-augmented-neural-networks-with-sparse-reads-and-writes.pdf).
 
 ## Install
 
@@ -252,6 +257,107 @@ Memory vectors returned by forward pass (`np.ndarray`):
 | `debug_memory['write_weights']` | layer * time | nr_cells
 | `debug_memory['usage']` | layer * time | nr_cells
 
+### SAM
+
+**Constructor Parameters**:
+
+Following are the constructor parameters:
+
+| Argument | Default | Description |
+| --- | --- | --- |
+| input_size | `None` | Size of the input vectors |
+| hidden_size | `None` | Size of hidden units |
+| rnn_type | `'lstm'` | Type of recurrent cells used in the controller |
+| num_layers | `1` | Number of layers of recurrent units in the controller |
+| num_hidden_layers | `2` | Number of hidden layers per layer of the controller |
+| bias | `True` | Bias |
+| batch_first | `True` | Whether data is fed batch first |
+| dropout | `0` | Dropout between layers in the controller |
+| bidirectional | `False` | If the controller is bidirectional (Not yet implemented |
+| nr_cells | `5000` | Number of memory cells |
+| read_heads | `4` | Number of read heads |
+| sparse_reads | `4` | Number of sparse memory reads per read head |
+| cell_size | `10` | Size of each memory cell |
+| nonlinearity | `'tanh'` | If using 'rnn' as `rnn_type`, non-linearity of the RNNs |
+| gpu_id | `-1` | ID of the GPU, -1 for CPU |
+| independent_linears | `False` | Whether to use independent linear units to derive interface vector |
+| share_memory | `True` | Whether to share memory between controller layers |
+
+Following are the forward pass parameters:
+
+| Argument | Default | Description |
+| --- | --- | --- |
+| input | - | The input vector `(B*T*X)` or `(T*B*X)` |
+| hidden | `(None,None,None)` | Hidden states `(controller hidden, memory hidden, read vectors)` |
+| reset_experience | `False` | Whether to reset memory |
+| pass_through_memory | `True` | Whether to pass through memory |
+
+
+#### Example usage:
+
+```python
+from dnc import SAM
+
+rnn = SAM(
+  input_size=64,
+  hidden_size=128,
+  rnn_type='lstm',
+  num_layers=4,
+  nr_cells=100,
+  cell_size=32,
+  read_heads=4,
+  sparse_reads=4,
+  batch_first=True,
+  gpu_id=0
+)
+
+(controller_hidden, memory, read_vectors) = (None, None, None)
+
+output, (controller_hidden, memory, read_vectors) = \
+  rnn(torch.randn(10, 4, 64), (controller_hidden, memory, read_vectors, reset_experience=True))
+```
+
+
+#### Debugging:
+
+The `debug` option causes the network to return its memory hidden vectors (numpy `ndarray`s) for the first batch each forward step.
+These vectors can be analyzed or visualized, using visdom for example.
+
+```python
+from dnc import SAM
+
+rnn = SAM(
+  input_size=64,
+  hidden_size=128,
+  rnn_type='lstm',
+  num_layers=4,
+  nr_cells=100,
+  cell_size=32,
+  read_heads=4,
+  batch_first=True,
+  sparse_reads=4,
+  gpu_id=0,
+  debug=True
+)
+
+(controller_hidden, memory, read_vectors) = (None, None, None)
+
+output, (controller_hidden, memory, read_vectors), debug_memory = \
+  rnn(torch.randn(10, 4, 64), (controller_hidden, memory, read_vectors, reset_experience=True))
+```
+
+Memory vectors returned by forward pass (`np.ndarray`):
+
+| Key | Y axis (dimensions) | X axis (dimensions) |
+| --- | --- | --- |
+| `debug_memory['memory']` | layer * time | nr_cells * cell_size
+| `debug_memory['visible_memory']` | layer * time | sparse_reads+2*temporal_reads+1 * nr_cells
+| `debug_memory['read_positions']` | layer * time | sparse_reads+2*temporal_reads+1
+| `debug_memory['read_weights']` | layer * time | read_heads * nr_cells
+| `debug_memory['write_weights']` | layer * time | nr_cells
+| `debug_memory['usage']` | layer * time | nr_cells
+
+
 ## Example copy task
 
 The copy task, as descibed in the original paper, is included in the repo.

tasks/copy_task.py

@@ -24,6 +24,7 @@ from torch.nn.utils import clip_grad_norm
 
 from dnc.dnc import DNC
 from dnc.sdnc import SDNC
+from dnc.sam import SAM
 from dnc.util import *
 
 parser = argparse.ArgumentParser(description='PyTorch Differentiable Neural Computer')
@@ -31,7 +32,7 @@ parser.add_argument('-input_size', type=int, default=6, help='dimension of input
 parser.add_argument('-rnn_type', type=str, default='lstm', help='type of recurrent cells to use for the controller')
 parser.add_argument('-nhid', type=int, default=64, help='number of hidden units of the inner nn')
 parser.add_argument('-dropout', type=float, default=0, help='controller dropout')
-parser.add_argument('-memory_type', type=str, default='dnc', help='dense or sparse memory')
+parser.add_argument('-memory_type', type=str, default='dnc', help='dense or sparse memory: dnc | sdnc | sam')
 
 parser.add_argument('-nlayer', type=int, default=1, help='number of layers')
 parser.add_argument('-nhlayer', type=int, default=2, help='number of hidden layers')
@@ -55,6 +56,7 @@ parser.add_argument('-log-interval', type=int, default=200, metavar='N', help='r
 parser.add_argument('-iterations', type=int, default=100000, metavar='N', help='total number of iteration')
 parser.add_argument('-summarize_freq', type=int, default=100, metavar='N', help='summarize frequency')
 parser.add_argument('-check_freq', type=int, default=100, metavar='N', help='check point frequency')
+parser.add_argument('-visdom', action='store_true', help='plot memory content on visdom per -summarize_freq steps')
 
 args = parser.parse_args()
 print(args)
@@ -129,7 +131,7 @@ if __name__ == '__main__':
         cell_size=mem_size,
         read_heads=read_heads,
         gpu_id=args.cuda,
-        debug=True,
+        debug=args.visdom,
         batch_first=True,
         independent_linears=True
     )
@@ -147,7 +149,24 @@ if __name__ == '__main__':
         temporal_reads=args.temporal_reads,
         read_heads=args.read_heads,
         gpu_id=args.cuda,
-        debug=False,
+        debug=args.visdom,
+        batch_first=True,
+        independent_linears=False
+    )
+  elif args.memory_type == 'sam':
+    rnn = SAM(
+        input_size=args.input_size,
+        hidden_size=args.nhid,
+        rnn_type=args.rnn_type,
+        num_layers=args.nlayer,
+        num_hidden_layers=args.nhlayer,
+        dropout=args.dropout,
+        nr_cells=mem_slot,
+        cell_size=mem_size,
+        sparse_reads=args.sparse_reads,
+        read_heads=args.read_heads,
+        gpu_id=args.cuda,
+        debug=args.visdom,
         batch_first=True,
         independent_linears=False
     )
@@ -252,7 +271,7 @@ if __name__ == '__main__':
                 xlabel='mem_slot'
             )
         )
-      else:
+      elif args.memory_type == 'sdnc':
         viz.heatmap(
             v['link_matrix'][-1].reshape(args.mem_slot, -1),
             opts=dict(
@@ -275,6 +294,7 @@ if __name__ == '__main__':
             )
         )
 
+      elif args.memory_type == 'sdnc' or args.memory_type == 'dnc':
         viz.heatmap(
             v['precedence'],
             opts=dict(