def __init__(self):
self.filepath = os.path.join(os.path.dirname(__file__), '..')
self.module_name = 'lif_convnet'
# --- LIF vision network weights configurations ---
self.vision_network_filename = \
os.path.join(self.filepath, self.module_name, 'params.pkl')
self.vision_network_data = \
load_model_pickle(self.vision_network_filename)
self.mem_okey = 'fc1'
self.classify_okey = 'fc10'
# --- Visual associative memory configurations ---
centers_filename = \
os.path.join(self.filepath, self.module_name, 'class_centers.npz')
sps = np.load(centers_filename, encoding='latin1')['centers']
self.num_classes = sps.shape[0]
self.sps_output_scale = 1.0 / 32.0
# For magic number 32.0, see reference_code/vision_3/data_analysis.py
# (print np.mean(norms) for sp_2000)
means_filename = \
os.path.join(self.filepath, self.module_name,
'fc10_class_means.npz')
self.sps_fc10 = np.eye(self.num_classes)
self.sps_fc10_means = np.load(means_filename,
encoding='latin1')['means']
# For magic number 10.1, see reference_code/vision_3/data_analysis.py
# (print np.mean(norms) for sp_10)
# --- Mandatory data object attributes ---
self.am_threshold = 0.5
self.sps = sps
self.dimensions = self.sps.shape[1]
self.sps_element_scale = 1.0 * self.sps_output_scale
# retrieve from https://figshare.com/s/cdde71007405eb11a88f
filename = 'ilsvrc-2012-batches-test3.tar.gz'
X_test, Y_test, data_mean, label_names = load_ilsvrc2012(filename, n_files=1)
X_test = X_test.astype('float32')
# crop data
X_test = X_test[:, :, 16:-16, 16:-16]
data_mean = data_mean[:, 16:-16, 16:-16]
image_shape = X_test.shape[1:]
# subtract mean
X_test -= data_mean
# retrieve from https://figshare.com/s/f343c68df647e675af28
cc_model = load_model_pickle('ilsvrc2012-lif-48.pkl')
# --- Run model in Nengo
presentation_time = 0.2
model = nengo.Network()
with model:
u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.001))
nengo.Connection(u, ccnet.inputs['data'], synapse=None)
# input_p = nengo.Probe(u)
output_p = nengo.Probe(ccnet.output)
# --- image display
import os
from pathlib import Path
import nengo_extras.data
from nengo_extras.cuda_convnet import load_model_pickle
REPO_LOCAL_PATH = Path(os.getcwd())
# layers, weights, etc.
REPO_LOCAL_DATA_PATH = REPO_LOCAL_PATH.joinpath('data')
VIS_NET_DATA_FNAME = 'ilsvrc2012-lif-48.pkl'
VIS_NET_DATA_PATH = REPO_LOCAL_DATA_PATH.joinpath(
'neural_net_weights/{}'.format(VIS_NET_DATA_FNAME)
)
VIS_NET_DATA_PATH = str(VIS_NET_DATA_PATH)
VIS_NET_DATA_URL = 'https://ndownloader.figshare.com/files/5370917?private_link=f343c68df647e675af28'
# download if not already
VIS_NET_DATA_PATH = nengo_extras.data.get_file(filename=VIS_NET_DATA_PATH,
url=VIS_NET_DATA_URL)
VIS_NET_DATA = load_model_pickle(str(VIS_NET_DATA_PATH))
def __init__(self):
self.filepath = os.path.join(os.path.dirname(__file__), '..')
self.module_name = 'lif_imagenet'
# --- LIF vision network weights configurations ---
# retrieve from https://figshare.com/s/f343c68df647e675af28
self.vision_network_filename = \
os.path.join(self.filepath, self.module_name, 'params.pkl')
# Try alternate params filename if can't locate default file
if not os.path.exists(self.vision_network_filename):
self.vision_network_filename = \
os.path.join(self.filepath, self.module_name,
'ilsvrc2012-lif-48.pkl')
self.vision_network_data = \
load_model_pickle(self.vision_network_filename)
# --- Spaun symbol associative memory configurations ---
self.spaun_am_threshold = 0.5
self.spaun_am_input_bias = -9.0
self.spaun_am_input_scale = 10.0
centers_filename = \
os.path.join(self.filepath, self.module_name,
'spaun_sym_class_centers.npz')
self.sps_spaun_sym = np.load(centers_filename,
encoding='latin1')['class_centers']
self.spaun_sym_num_classes = self.sps_spaun_sym.shape[0]
self.spaun_sym_out_dimensions = self.sps_spaun_sym.shape[1]
# --- Imagenet associative memory configurations ---
# self.imagenet_am_threshold = 0.5
# self.imagenet_am_threshold = 0.30
self.imagenet_am_threshold = 0.21
means_filename = \
os.path.join(self.filepath, self.module_name,
'class_means.npz')
self.imagenet_sps_means = np.load(means_filename,
encoding='latin1')['means']
self.imagenet_num_classes = self.imagenet_sps_means.shape[0]
self.imagenet_out_dimensions = self.imagenet_num_classes
self.sps_fc1000 = np.eye(self.imagenet_num_classes)
# --- Combined configurations ---
# Here, we are going to output layer 'pool2' to the spaun_sym AM
# (output dimensions = self.spaun_sym_out_dimensions) + 'fc1000' to
# imagenet AM (output_dimensions = self.imagenet_out_dimensions)
self.spaun_sym_okey = 'pool2'
self.output_dimensions = (self.spaun_sym_out_dimensions +
self.imagenet_out_dimensions)
# --- Mandatory data object attributes ---
self.sps = np.zeros((self.spaun_sym_num_classes + # HACK: To avoid
self.imagenet_num_classes, 10)) # creating vis_wm
self.dimensions = self.sps.shape[1] # of 1000+ dims
self.sps_element_scale = 1.0
self.am_threshold = 0.5
# crop data
Xtest = Xtest[:, :, 16:-16, 16:-16]
data_mean = data_mean[:, 16:-16, 16:-16]
image_shape = Xtest.shape[1:]
# subtract mean
Xtest -= data_mean
if 0:
# shuffle images
i = rng.permutation(Xtest.shape[0])
Xtest, Ytest = Xtest[i], Ytest[i]
# retrieve from https://figshare.com/s/f343c68df647e675af28
cc_model = load_model_pickle('~/data/ilsvrc2012-lif-48.pkl')
# --- Run model in Nengo
presentation_time = 0.2
c0 = 0.14 # classification start time
model = nengo.Network()
with model:
u = nengo.Node(nengo.processes.PresentInput(Xtest, presentation_time))
ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.003))
nengo.Connection(u, ccnet.input, synapse=None)
# input_p = nengo.Probe(u)
output_p = nengo.Probe(ccnet.output)
spike_ps = [
nengo.Probe(ccnet.layers_by_name['conv1_neuron'].ensemble.neurons),
(X_train, y_train), (X_test, y_test), label_names = load_cifar10(label_names=True)
X_train = X_train.reshape(-1, 3, 32, 32).astype('float32')
X_test = X_test.reshape(-1, 3, 32, 32).astype('float32')
# crop data
X_train = X_train[:, :, 4:-4, 4:-4]
X_test = X_test[:, :, 4:-4, 4:-4]
# subtract mean
data_mean = X_train.mean(axis=0)
X_train -= data_mean
X_test -= data_mean
# retrieve from https://figshare.com/s/49741f9e2d0d29f68871
cc_model = load_model_pickle('cifar10-lif-1628.pkl')
# --- Run model in Nengo
presentation_time = 0.2
model = nengo.Network()
with model:
u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.005))
nengo.Connection(u, ccnet.inputs['data'], synapse=None)
input_p = nengo.Probe(u)
output_p = nengo.Probe(ccnet.output)
# --- image display
image_shape = X_test.shape[1:]
# retrieve from https://figshare.com/s/cdde71007405eb11a88f
filename = 'ilsvrc-2012-batches-test3.tar.gz'
X_test, Y_test, data_mean, label_names = load_ilsvrc2012(filename, n_files=1)
X_test = X_test.astype('float32')
# crop data
X_test = X_test[:, :, 16:-16, 16:-16]
data_mean = data_mean[:, 16:-16, 16:-16]
image_shape = X_test.shape[1:]
# subtract mean
X_test -= data_mean
# retrieve from https://figshare.com/s/f343c68df647e675af28
cc_model = load_model_pickle('ilsvrc2012-lif-48.pkl')
# --- Run model in Nengo
presentation_time = 0.2
model = nengo.Network()
with model:
u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.001))
nengo.Connection(u, ccnet.inputs['data'], synapse=None)
# input_p = nengo.Probe(u)
output_p = nengo.Probe(ccnet.output)
# --- image display
display_f = image_display_function(image_shape, scale=1., offset=data_mean)
(X_train, y_train), (X_test,
y_test), label_names = load_cifar10(label_names=True)
X_train = X_train.reshape(-1, 3, 32, 32).astype('float32')
X_test = X_test.reshape(-1, 3, 32, 32).astype('float32')
# crop data
X_train = X_train[:, :, 4:-4, 4:-4]
X_test = X_test[:, :, 4:-4, 4:-4]
# subtract mean
data_mean = X_train.mean(axis=0)
X_train -= data_mean
X_test -= data_mean
# retrieve from https://figshare.com/s/49741f9e2d0d29f68871
cc_model = load_model_pickle('cifar10-lif-1628.pkl')
# --- Run model in Nengo
presentation_time = 0.2
model = nengo.Network()
with model:
u = nengo.Node(nengo.processes.PresentInput(X_test, presentation_time))
ccnet = CudaConvnetNetwork(cc_model, synapse=nengo.synapses.Alpha(0.005))
nengo.Connection(u, ccnet.inputs['data'], synapse=None)
input_p = nengo.Probe(u)
output_p = nengo.Probe(ccnet.output)
# --- image display
image_shape = X_test.shape[1:]