From ad911759712d1008f651e8f6d549df167f88ac21 Mon Sep 17 00:00:00 2001
From: drasmuss <drasmuss@uwaterloo.ca>
Date: Thu, 29 Oct 2015 13:19:34 -0400
Subject: [PATCH] Basic proof of concept

---
 nengo_deeplearning/hf_nengo.py | 155 +++++++++++++++++++++++++++++++++
 1 file changed, 155 insertions(+)
 create mode 100644 nengo_deeplearning/hf_nengo.py

diff --git a/nengo_deeplearning/hf_nengo.py b/nengo_deeplearning/hf_nengo.py
new file mode 100644
index 0000000..a57159b
--- /dev/null
+++ b/nengo_deeplearning/hf_nengo.py
@@ -0,0 +1,155 @@
+import hessianfree as hf
+import matplotlib.pyplot as plt
+import nengo
+from nengo.utils.functions import piecewise
+import numpy as np
+
+
+def convert_layer(nonlinearity, n_neurons):
+    # TODO: let this map in both directions
+
+    ens = nengo.Ensemble(n_neurons, 1, gain=np.ones(n_neurons),
+                         bias=np.zeros(n_neurons))
+
+    if isinstance(nonlinearity, hf.nl.ReLU):
+        ens.neuron_type = nengo.neurons.RectifiedLinear()
+    elif isinstance(nonlinearity, hf.nl.Logistic):
+        ens.neuron_type = nengo.neurons.Sigmoid(tau_ref=1)
+    elif isinstance(nonlinearity, hf.nl.Linear):
+        ens = nengo.Node(size_in=n_neurons)
+        ens.neurons = ens
+    else:
+        raise TypeError("Cannot convert nonlinearity %s" % nonlinearity)
+
+    return ens
+
+
+def hf_to_nengo(hfnet, dt=1):
+    with nengo.Network() as net:
+        # convert each layer to an ensemble
+        layers = [convert_layer(l, hfnet.shape[i])
+                  for i, l in enumerate(hfnet.layers)]
+
+        # add in feedforward connections
+        for pre in hfnet.conns:
+            for post in hfnet.conns[pre]:
+                W, b = hfnet.get_weights(hfnet.W, (pre, post))
+                nengo.Connection(layers[pre].neurons, layers[post].neurons,
+                                 transform=W.T, synapse=None)
+                layers[post].bias += b
+
+        if isinstance(hfnet, hf.RNNet):
+            # bias on first timestep
+            bias = nengo.Node(piecewise({dt: [1], 2 * dt: [0]}), size_out=1)
+
+            for l in range(hfnet.n_layers):
+                if hfnet.rec_layers[l]:
+                    W, b = hfnet.get_weights(hfnet.W, (l, l))
+                    nengo.Connection(layers[l].neurons, layers[l].neurons,
+                                     transform=W.T, synapse=0)
+
+                    nengo.Connection(bias, layers[l].neurons,
+                                     transform=b[:, None], synapse=None)
+
+    net.layers = layers
+    return net
+
+
+class ArrayInput(nengo.processes.Process):
+    def __init__(self, data):
+        super(ArrayInput, self).__init__()
+        self.data = data
+
+    def make_step(self, size_in, size_out, dt, rng):
+        assert size_in == 0
+        assert size_out == self.data.shape[1]
+
+        def step(_):
+            step.count += 1
+            return self.data[step.count % self.data.shape[0]]
+        step.count = -1
+
+        return step
+
+
+def test_ff():
+    inputs = np.asarray([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
+    targets = np.asarray([[0], [1], [1], [0]], dtype=np.float32)
+
+    hfnet = hf.FFNet([2, 10, 1], layers=[hf.nl.Linear(), hf.nl.Logistic(),
+                                         hf.nl.Logistic()])
+    hfnet.run_batches(inputs, targets, max_epochs=40,
+                      optimizer=hf.opt.HessianFree(CG_iter=10))
+
+    outputs = hfnet.forward(inputs, hfnet.W)[-1]
+    print "hf net"
+    print inputs
+    print outputs
+
+    net = hf_to_nengo(hfnet)
+
+    with net:
+        inpt = nengo.Node(ArrayInput(inputs), size_out=2)
+        nengo.Connection(inpt, net.layers[0].neurons, synapse=None)
+        in_p = nengo.Probe(net.layers[0].neurons)
+        out_p = nengo.Probe(net.layers[-1].neurons)
+
+    sim = nengo.Simulator(net, dt=1)
+    sim.run(4)
+
+    print "nengo net"
+    print sim.data[in_p]
+    print sim.data[out_p]
+
+
+def test_rnn():
+    n_inputs = 10
+    sig_len = 10
+    inputs = np.outer(np.linspace(0.1, 0.9, n_inputs),
+                      np.ones(sig_len))[:, :, None]
+    targets = np.outer(np.linspace(0.1, 0.9, n_inputs),
+                       np.linspace(0, 1, sig_len))[:, :, None]
+    inputs = inputs.astype(np.float32)
+    targets = targets.astype(np.float32)
+
+    hfnet = hf.RNNet(shape=[1, 10, 1], layers=hf.nl.Logistic())
+    hfnet.run_batches(inputs, targets, max_epochs=30,
+                      optimizer=hf.opt.HessianFree(CG_iter=100))
+
+    plt.figure()
+    plt.plot(inputs.squeeze().T)
+    plt.title("hf inputs")
+
+    outputs = hfnet.forward(inputs, hfnet.W)[-1]
+    plt.figure()
+    plt.plot(outputs.squeeze().T)
+    plt.title("hf outputs")
+
+    net = hf_to_nengo(hfnet)
+    with net:
+        array_in = ArrayInput(inputs[0])
+        inp = nengo.Node(array_in, size_out=1)
+        nengo.Connection(inp, net.layers[0], synapse=None)
+        in_p = nengo.Probe(net.layers[0].neurons)
+        out_p = nengo.Probe(net.layers[-1].neurons)
+
+    plt.figure()
+    plt_in = plt.gca()
+    plt.title("nengo inputs")
+    plt.figure()
+    plt_out = plt.gca()
+    plt.title("nengo outputs")
+
+    sim = nengo.Simulator(net, dt=1)
+    for i in range(n_inputs):
+        array_in.data = inputs[i]
+        sim.reset()
+        sim.run(sig_len)
+
+        plt_in.plot(sim.data[in_p])
+        plt_out.plot(sim.data[out_p])
+
+    plt.show()
+
+# test_ff()
+test_rnn()