def init(self, input_width):
self.input_width = input_width
if not isinstance(self.weight_initializer, Initializer):
weight_initializer = self.weight_initializer(
self.input_width, self.units)
if not isinstance(self.bias_initializer, Initializer):
bias_initializer = self.bias_initializer()
self.weights_buf = weight_initializer((self.units, self.input_width))
self.weights = array.to_device(self.queue, self.weights_buf)
self.bias_buf = bias_initializer((self.units, 1))
self.bias = array.to_device(self.queue, self.bias_buf)
# should probably make this 2d so it can have dimensions (output_width, batch_size)
self.output = array.zeros(self.queue, (self.batch_size, self.units),
dtype=dtype)
self.output_data = self.output.data
self.input_width = cltypes.uint(self.input_width)
self.output_width = cltypes.uint(self.units)
self.activation = self.activation
max_output, max_batch_size = self.queue.device.max_work_item_sizes[:2]
if self.output_width > max_output:
raise ValueError(
f"Layer output cannot exceed {max_output}, you gave {self.output_width}"
)
if self.batch_size > max_batch_size:
raise ValueError(
f"Batch size cannot exceed {max_batch_size}, you gave {self.batch_size}"
)
return self.units
def inferSingleStepCL(self, pattern, weights):
"""
__constant char* pattern,
__constant float* weights,
__global float* predictions,
__global float* sums,
uint const numBuckets
:param pattern:
:param param:
:return:
"""
cl_pattern = cl.Buffer(self._ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=pattern)
cl_weights = cl.Buffer(self._ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=weights)
predictions = np.empty(len(pattern), dtype=cltypes.float)
cl_predictions = cl.Buffer(self._ctx, mf.WRITE_ONLY,
predictions.nbytes)
cl_sums = cl.Buffer(self._ctx, mf.READ_WRITE, 32 * len(pattern))
self._prg.inferSingleStep(self._queue, (pattern.shape[0], ), None,
cl_pattern, cl_weights, cl_predictions,
cl_sums, cltypes.uint(self._numBuckets),
cltypes.uint(self._maxinput))
cl.enqueue_copy(self._queue, predictions, cl_predictions).wait()
return predictions
def compute(self, recordNum, pattern, bucketIdx, actValue, learn, infer):
"""
Computes 1 step
:param recordNum:
:param pattern: indices of active columns in the TM layer
:param classification: dict of bucketIdx and actualValue
:param learn:
:param infer:
:return:
"""
pattern = np.array(pattern, dtype=cltypes.uint)
if not self._init_buffers:
self._setup_buffers(pattern)
ev_copy_pattern = cl.enqueue_write_buffer(self._queue, self.cl_activeBitIdx, pattern)
# update bit activations on device side
ev_update_bit = self._prg.update_bit_activations(self._queue, (pattern.size,), None,
self.cl_bit_activations, self.cl_activeBitIdx,
wait_for=[ev_copy_pattern])
multiStepPredictions = {}
ev_learn = None
if learn:
ev_learn = [self._prg.learn(self._queue, (self.step_count * pattern.size,), None,
self.cl_activeBitIdx, self.cl_table_average, self.cl_table_counts, self.alpha,
self.actValueAlpha,
cltypes.uint(bucketIdx), self._numBuckets,
wait_for=[ev_update_bit])]
if infer:
"""
const __global float* averages,
const __global uint* counts,
const __global uint* activeBitIdx,
__global float2* predictions, // the array of predictions
__global const uint* bitActivations, // the number of times each bit has been active
uint const activeBits
"""
# kernel for every active bit in each step
ev_infer = self._prg.infer(self._queue, (self._numBuckets,), None,
self.cl_table_average, self.cl_table_counts, self.cl_activeBitIdx,
self.cl_predictions, self.cl_bit_activations,
cltypes.uint(pattern.size), wait_for=ev_learn)
cl.enqueue_copy(self._queue, self._predictions, self.cl_predictions, wait_for=[ev_infer]).wait()
# print("Activations", self.bucket_activations)
# multiStepPredictions['actualValues'] = predictions['x'] / len(pattern)
# multiStepPredictions[step] = predictions['y'] / len(pattern) # the probability for each bucket
# print("Actual Values", multiStepPredictions['actualValues'])
multiStepPredictions[1] = self._predictions.copy()
# print("Probability", multiStepPredictions[1])
self.bucket_activations[bucketIdx] += 1
return multiStepPredictions
def _get_overlap_score_loop_bin(self, encoding):
"""
Returns an array with boosted and non-boosted scores as a vector
:param encoding: the encoded data
:return:
overlap_loop(
__constant int64* activeBits, // active bits in sorted order
__constant synapse_struct* synapses, // all the synapses
__global uint2* overlaps, // columns to store overlap scores
__constant float* boostFactors, // boost values for columns
const float synPermConnected,
const int synapsesPerColumn,
const uint numActiveBits
)
"""
active_bits = np.where(encoding == 1)[0]
cl.enqueue_write_buffer(self._queue, self.cl_active_bits, active_bits)
cl_synapses = self._get_cl_synapses_buffer()
cl_boostFactors = self._get_cl_boost_factor_buffer()
overlap = np.zeros(self.columnCount, dtype=cl.array.vec.uint2
) # array of overlap and boosted overlap scores
cl_overlap = cl.Buffer(self._ctx, mf.WRITE_ONLY, overlap.nbytes)
self.prog.overlap_loop_bin(self._queue, (self.columnCount, ), None,
self.cl_active_bits, cl_synapses,
cl_overlap, cl_boostFactors,
self.synPermConnected,
self.synapsesPerColumn,
cltypes.uint(active_bits.size)).wait()
cl.enqueue_copy(self._queue, overlap, cl_overlap).wait()
return overlap
def __init__(self,
queue,
activationThreshold=14,
cellsPerColumn=32,
columnCount=2048,
globalDecay=0.0,
initialPerm=0.21,
inputWidth=2048,
maxAge=0,
maxSegmentsPerCell=128,
maxSynapsesPerSegment=32,
minThreshold=11,
newSynapseCount=20,
outputType='normal',
pamLength=3,
permanenceDec=0.1,
permanenceInc=0.1,
seed=1960,
temporalImp='cl',
verbosity=0):
if temporalImp != 'cl':
raise ValueError('This implementation only supports OpenCL')
self.activationThreshold = cltypes.uint(activationThreshold)
self.columnCount = cltypes.uint(columnCount)
self.cellsPerColumn = cltypes.uint(cellsPerColumn)
self.globalDecay = cltypes.float(globalDecay)
self.initialPerm = cltypes.float(initialPerm)
self.maxAge = cltypes.uint(maxAge)
self.maxSegmentsPerCell = cltypes.uint(maxSegmentsPerCell)
self.maxSynapsesPerSegment = cltypes.uint(maxSynapsesPerSegment)
self.minThreshold = cltypes.uint(minThreshold)
self.newSynapseCount = cltypes.uint(newSynapseCount)
self.outputType = outputType
self.pamLength = cltypes.uint(pamLength)
self.permanenceDec = cltypes.float(permanenceDec)
self.permanenceInc = cltypes.float(permanenceInc)
np.random.seed(seed)
self.verbosity = verbosity
self.columnCount = columnCount
self.inputWidth = inputWidth
self._queue = queue
self._ctx = queue.context
np.random.seed(seed)
self._setup_cl_buffers()
def __init__(self, queue, numBuckets, steps=[1], bits=2048, alpha=0.001, actValueAlpha=0.3, verbosity=False):
self._prg = cl.Program(queue.context, kernel_src).build()
self._learn_iteration = 0
self.bit_activations = np.zeros(bits, dtype=cltypes.uint)
self.bucket_activations = np.zeros(numBuckets, dtype=cltypes.uint)
self.steps = steps
self.step_count = len(steps)
self.alpha = cltypes.float(alpha)
self.actValueAlpha = cltypes.float(actValueAlpha)
self.bits = bits # number of bits in the input
self._queue = queue # the opencl queue
self._ctx = queue.context # the opencl context
self._numBuckets = cltypes.uint(numBuckets)
self._verbose = verbosity
self._init_buffers = False
def run(ctx, src):
w, h = 1024, 512
depth = 1024
img = np.zeros((h, w), dtype=cltypes.float)
time = run_kernel(
ctx,
src,
(w, h),
Mem(img),
*[cltypes.uint(k) for k in (w, h, depth)],
)
"""
for row in img[::16,::16]:
for z in row:
print("@" if z > 0.5 else ".", end="")
print()
"""
print("\t{:.3f} sec: {}".format(time, os.path.split(src)[1]))
return (img, )
def __init__(self, dims):
self.width = dims[0]
self.height = dims[1]
import pyopencl as cl
from pyopencl import cltypes
import numpy as np
from matplotlib import cm
self.cm = cm
self.np = np
self.cl = cl
self.ctx = cl.Context([cl.get_platforms()[1].get_devices()[0]])
self.queue = cl.CommandQueue(self.ctx)
self.prg = cl.Program(
self.ctx, """
#pragma OPENCL EXTENSION cl_khr_byte_addressable_store : enable
__kernel void mandelbrot(__global float2 *q, __constant uchar4 *lut,
__global uchar4 *output, __global uint* output2, uint const maxiter)
{
const int gid = get_global_id(0);
float real = q[gid].x;
float imag = q[gid].y;
output[gid] = (uchar4)(0,0,0,0);
output2[gid] = 0;
for(uint curiter = 0; curiter < maxiter; curiter++) {
float real2 = real*real, imag2 = imag*imag;
if (real2 + imag2 > 4.0f) {
output[gid] = lut[curiter];
output2[gid] = curiter;
return;
}
imag = 2 * real*imag + q[gid].y;
real = real2 - imag2 + q[gid].x;
}
}
""").build()
import time
self.time = time
self.centerx = (-0.74877 + -0.74872) / 2
self.centery = (0.06505 + 0.06510) / 2
self.padding = 2
self.maxiter = cltypes.uint(64)
# self.xmin = -np.pi
# self.xmax = np.pi
# self.ymin = -np.pi
# self.ymax = np.pi
self.update_pos()
cmap = self.cm.get_cmap('gnuplot2', self.maxiter)
cols = [(np.array(cmap(i)[:-1]) * 255).astype(cl.cltypes.uchar)
for i in range(self.maxiter)]
self.lut = np.zeros((self.maxiter, ), cl.cltypes.uchar4)
for idx, i in enumerate(cols):
self.lut[idx][0] = i[0]
self.lut[idx][1] = i[1]
self.lut[idx][2] = i[2]
self.lut_opencl = cl.Buffer(self.ctx,
cl.mem_flags.READ_ONLY
| cl.mem_flags.COPY_HOST_PTR,
hostbuf=self.lut)
],
dtype=cltypes.float)
colors_buf = cl.Buffer(ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=colors)
color_width = 8.0
# Read source file
with open(src_path, "r") as f:
src = f.read()
# Build program
prg = cl.Program(ctx, src).build()
# Execute OpenCL kernel on the device
prg.render(queue, (width, height), None, image_buf, map_buf, colors_buf,
*[cltypes.uint(x) for x in [width, height, depth, ssf]],
cltypes.uint(len(colors)), cltypes.float(color_width))
# Copy rendered image to host
cl.enqueue_copy(queue, image, image_buf)
# Flush queue
queue.flush()
queue.finish()
# Draw low-resolution image in terminal
for row in np.mean(image[::32, ::16], axis=2):
for x in row:
print("@" if x > 1e-4 else ".", end="")
print()
def compute(self, recordNum, pattern, classification, learn, infer):
"""
Computes 1 step
:param recordNum:
:param pattern: indices of active columns in the TM layer
:param classification: dict of bucketIdx and actualValue
:param learn:
:param infer:
:return:
"""
if self.verbosity:
print(" recordNum:", recordNum)
print(" patternNZ (%d):" % len(pattern), pattern)
print(" classificationIn:", classification)
bucketIdx, actValue = classification['bucketIdx'], classification[
'actValue']
pattern = np.array(pattern).astype(cltypes.uint)
self._patternNZHistory.append((recordNum, pattern))
retval = None
if infer:
retval = self.infer(pattern, classification)
return retval
if learn and bucketIdx is not None:
cl_activeBitIdx = cl.Buffer(self._ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=pattern)
for learnRecordNum, learnPattern in self._patternNZHistory:
error = dict()
targetDist = np.zeros(self._numBuckets + 1,
dtype=cltypes.float)
targetDist[bucketIdx] = 1.0
for step, table in self._weights.iteritems():
# print("old table")
# self._show_table(table)
"""
int* activeBitIdx
float2 *table, // x=histogram, y=moving average
float const alpha, // moving average alpha
float const actualValue, // actual input value
int const bucketIdx, // bucket that actualValue falls into
int const bucketCount,
bool const learn,
bool const infer,
__global float *predictions
"""
new_table = table.copy()
cl_new_table = cl.Buffer(self._ctx,
mf.WRITE_ONLY | mf.COPY_HOST_PTR,
hostbuf=new_table)
cl_table = cl.Buffer(self._ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=table)
if learn:
cl_table = cl.Buffer(self._ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=table)
if infer:
predictions = np.zeros(self._numBuckets,
dtype=cl.array.vec.float2)
cl_predictions = cl.Buffer(self._ctx, mf.READ_WRITE,
predictions.nbytes)
else:
cl_predictions = cl.Buffer(self._ctx, mf.WRITE_ONLY, 1)
self._prg.infer_compute(self._queue, (pattern.shape[0], ),
None, cl_activeBitIdx, cl_table,
cl_new_table,
cltypes.float(self.actValueAlpha),
cltypes.float(actValue),
cltypes.uint(bucketIdx),
cltypes.uint(self._numBuckets),
cltypes.char(learn),
cltypes.char(infer), cl_predictions)
if learn:
cl.enqueue_copy(self._queue, self.steps[step],
cl_new_table).wait()
if infer:
cl.enqueue_copy(self._queue, predictions,
cl_predictions).wait()
print("Activations", self.bucket_activations)
multiStepPredictions[step] = predictions['y'] / len(
pattern) # the probability for each bucket
print("Actual Values", multiStepPredictions['actualValues'])
print("Probability", multiStepPredictions[step])
self.bucket_activations[bucketIdx] += 1
return multiStepPredictions
def train(self,
epochs: int,
loss: Loss,
optimizer: Optimizer,
x_train,
y_train,
x_test,
y_test,
x_validation=None,
y_validation=None,
batch_size: int = 1,
shuffle: bool = True,
validation_pct=None,
validation_method='cross-validation',
callbacks=[]):
"""
:param epochs: number of epochs to run
:param loss: a loss function
:param optimizer: the optimizer to use
:param x_train: a 2D array of shape (rows, features)
:param y_train: a 2d array of shape (rows, output features),
output_features is the number of values we want to predict
:param x_test: testing data inputs
:param y_test: testing data true values
:param validation_method: a string to determine which validation method to use: 'holdout','cross-validation'
:return: None
For example, our input might be:
x_train = [
[0,1,1],
[0,2,1],
[1,2,1],
[0,3,4],
]
That is 4 rows with 3 features each, we might do a binary classification on this:
y_train = [
[0,1],
[0,1],
[1,0],
[0,1]
]
That is, each training input maps to one of these
All this will be copied to the device
Validation methods are:
1. Specify x_validation,y_validation and the same provided dataset will be used to validate every epoch
2. Specify validation_pct to determine how much of the training set will be set aside as validation.
Specify validation_method to determine which method to use:
* holdout: the same subset of x_train is used to validate each epoch
* cross-validation: at the start of each epoch a random sample of x_train/y_train is set aside
"""
if validation_pct is not None and x_validation is not None and y_validation is not None:
raise ValueError(
"Please set either validation_pct or (x_validation,x_validation)"
)
if x_validation is not None != x_validation is not None:
raise ValueError("Please set both (x_validation and y_validation)")
x_train = x_train.astype(dtype)
y_train = y_train.astype(dtype)
if validation_pct:
# slice off the last validation_ct from x_train,y_train
if 0 <= validation_pct < 1:
training_samples = int(x_train.shape[0] * (1 - validation_pct))
validation_samples = int(x_train.shape[0] * validation_pct)
if validation_method == 'holdout':
print(
f"Holding out last {validation_samples} samples of training data for validation"
)
x_train = x_train[:training_samples]
y_train = y_train[:training_samples]
x_validation = x_train[training_samples:]
y_validation = y_train[training_samples:]
x_val_gpu = array.to_device(self.queue, x_validation)
y_val_gpu = array.to_device(self.queue, y_validation)
elif validation_method == 'cross-validation':
print(
f"Using cross-validation on last {validation_samples}")
else:
raise ValueError("Invalid validation method")
validation_user = False
else:
raise ValueError(
"Validation_pct must be in range 0 <= val% < 1")
elif x_validation is not None and y_validation is not None:
print("User provided validation")
x_validation = x_validation.astype(dtype)
y_validation = y_validation.astype(dtype)
x_val_gpu = array.to_device(self.queue, x_validation)
y_val_gpu = array.to_device(self.queue, y_validation)
validation_samples = len(x_validation)
training_samples = x_train.shape[0]
validation_user = True
else:
training_samples = x_train.shape[0]
if len(x_train) != len(y_train):
raise ValueError("X and Y for test/train must be same length")
if training_samples % batch_size != 0:
raise ValueError(
"Training dataset must have rows divisible by batch size")
input_features = cltypes.uint(x_train.shape[1])
output_features = cltypes.uint(y_train.shape[1])
if input_features != self.layers[0].input_width:
raise ValueError(
f"Input features (provided={input_features}) must be the same as layer_0 input width (required={self.layers[0].input_width})"
)
# Just copy all training and all testing data to the device
for dn, ds in ("x_train",
x_train), ("y_train",
y_train), ("x_validation",
x_validation), ("y_validation",
y_validation):
try:
print("{}\n\tsize={}\n\tshape={}".format(
dn, humanize.naturalsize(ds.nbytes), ds.shape))
except AttributeError:
pass
# x_train_gpu = cl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf=x_train)
x_train_gpu = array.to_device(self.queue, x_train)
y_train_gpu = array.to_device(self.queue, y_train)
# should probably check that our data won't exceed available device memory,
# transparently queue up more data once it's been used
losses = {'batch': [], 'validation': [], 'testing': []}
for i in tqdm(range(epochs), desc='Epoch: ', position=0):
# shuffle the rows
if shuffle:
self.shuffle(x_train_gpu.data, y_train_gpu.data,
training_samples, input_features, output_features)
for idx in tqdm(range(training_samples // batch_size),
desc='Batch: ',
position=1,
unit=' batch'):
idx = cltypes.uint(idx)
# idx here is the batch number
batch_x_gpu = x_train_gpu[idx * batch_size:idx * batch_size +
batch_size]
batch_y_gpu = y_train_gpu[idx * batch_size:idx * batch_size +
batch_size]
# copy all of these to the device?
output = self.forward(batch_x_gpu, verbose=False)
loss_val = loss.cpu(batch_y_gpu, output)
# err = loss(batch_y_gpu, output, )
losses['batch'].append(loss_val)
# print(f"Mean Batch Loss={loss_val}")
optimizer(loss, self, batch_x_gpu, batch_y_gpu)
# if idx % 900 == 0:
# for c in callbacks:
# if c.batch_end:
# c(losses)
# run the network and get error for the validation set
# this should be a single batch of size validation_samples
# will need to allocate specific validation arrays
# if validation_user:
# # validate with user supplied validation data
# output = self.forward(x_val_gpu, 0) # should probably be done as a single batch,
# val_loss = loss(y_val_gpu, output, 0)
# else:
# # idx is the index of the validation set start position
# idx = len(x_train) - validation_samples
# output = self.forward(x_train_gpu, idx)
# val_loss = loss(y_train_gpu, output, idx)
# losses['validation'].append(val_loss)
# # collect metrics for training set
# output = self.forward(x_test, 0)
# test_loss = loss(y_test, output, 0)
# losses['testing'].append(test_loss)
for c in callbacks:
c(losses)
return losses
def __init__(self,
queue,
columnCount=2048,
globalInhibition=1,
inputWidth=500,
inputActive=33,
boostStrength=0.0,
numActiveColumnsPerInhArea=40,
potentialPct=.5,
stimulusThreshold=0,
seed=1956,
dutyCyclePeriod=1000,
spVerbosity=0,
spatialImp='cl',
synPermActiveInc=0.05,
synPermConnected=0.10,
synPermInactiveDec=0.008):
if spatialImp != 'cl':
raise ValueError(
'This implementation only supports OpenCL Temporal Memory')
if globalInhibition != 1:
raise ValueError(
'This implementation does not support local inhibition')
self.columnCount = cltypes.uint(columnCount)
self.globalInhibition = globalInhibition
self.inputWidth = inputWidth
self.boostStrength = boostStrength
self.numActiveColumnPerInhArea = cltypes.uint(
numActiveColumnsPerInhArea)
self.potentialPct = cltypes.float(potentialPct)
np.random.seed(seed)
self.verbosity = spVerbosity
self.synPermActiveInc = cltypes.float(synPermActiveInc)
self.synPermConnected = cltypes.float(synPermConnected)
self.synPermInactiveDec = cltypes.float(synPermInactiveDec)
# store the TM as an array of int, either on or off
self.columns = np.zeros(columnCount, dtype=cltypes.uint)
self.synapsesPerColumn = cltypes.uint(inputWidth * potentialPct)
self._stimulusThreshold = cltypes.uint(stimulusThreshold)
self._dutyCyclePeriod = cltypes.uint(dutyCyclePeriod)
self._activeDutyCycles = np.zeros(self.columnCount, cltypes.uint)
self._overlapDutyCycles = np.zeros(self.columnCount, cltypes.uint)
self._minOverlapDutyCycles = np.zeros(self.columnCount, cltypes.uint)
self._boostFactors = np.ones(self.columnCount, dtype=cltypes.float)
self._queue = queue
self._ctx = queue.context
self._updatePeriod = 50
synapse_struct = np.dtype([('permanence', cltypes.float),
('bitIdx', cltypes.uint)])
synapse_struct, synapse_struct_c_decl = cl.tools.match_dtype_to_c_struct(
self._ctx.devices[0], "synapse_struct", synapse_struct)
synapse_struct = cl.tools.get_or_register_dtype(
'synapse_struct', synapse_struct)
overlap_struct = np.dtype([('overlap', cltypes.uint),
('boosted', cltypes.uint)])
overlap_struct, overlap_struct_c_decl = cl.tools.match_dtype_to_c_struct(
self._ctx.devices[0], "overlap_struct", overlap_struct)
self.overlap_struct = cl.tools.get_or_register_dtype(
'overlap_struct', overlap_struct)
self.synapses = np.zeros(
(columnCount * self.synapsesPerColumn),
dtype=synapse_struct) # x is permanence value, y is input bit idx
if spVerbosity >= 1:
print(
'------------CL SpatialPooler Parameters ------------------')
# print("Synapse Struct", synapse_struct_c_decl)
print("Synapses\t", self.synapses.size)
print("Columns\t", self.columnCount)
print("Input Width\t", self.inputWidth)
print("Synapses Per Column\t", self.synapsesPerColumn)
print("Synapse Connection Threshold\t", self.synPermConnected)
self.synPermMin_ = 0.0
self.synPermMax_ = 1.0
self.synapses['permanence'] = np.clip(
np.random.normal(synPermConnected,
(self.synPermMax_ - self.synPermMin_) / 10,
size=self.synapses.shape[0]).astype(np.float32),
0, 1)
self.synapses_no_bit = self.synapses['permanence']
input_synapses = np.arange(0, inputWidth)
for column in range(self.columnCount):
idx = column * self.synapsesPerColumn
self.synapses['bitIdx'][idx:idx +
self.synapsesPerColumn] = np.random.choice(
input_synapses, self.synapsesPerColumn,
False)
bits, counts = np.unique(self.synapses['bitIdx'], return_counts=True)
# array mapping each input bit to it's synapses indexes
max_count = np.max(counts)
self.max_input_to_synapse = cltypes.int(max_count)
self.input_bitIdx = np.full((len(counts) * max_count),
-1,
dtype=cltypes.int)
for inputBitIdx in xrange(inputWidth):
idx = inputBitIdx * max_count
synapseIndexes = np.where(
self.synapses['bitIdx'] == inputBitIdx)[0]
self.input_bitIdx[idx:idx + synapseIndexes.size] = synapseIndexes
# print("Connected synapses: ", np.where(self.synapses['permanence'] > synPermConnected)[0].size / float(
# self.synapses['permanence'].size))
# each column connects to exactly columnCount*potentialPct inputs
src = ''.join(
[synapse_struct_c_decl, overlap_struct_c_decl, kernel_src])
self.prog = cl.Program(self._ctx, src).build()
# print (map(lambda x: x.get_info(pyopencl.kernel_info.FUNCTION_NAME), self.prog.all_kernels()))
self._iterationNum = 0
self._iterationLearnNum = 0
self._inhibitionRadius = self.columnCount
self.synapseCount = self.synapsesPerColumn * self.columnCount
if spVerbosity >= 1:
# self._show_synapses()
pass
# initialise host buffers for commonly used things
# we only copy stuff between host and device when we need to
self.overlap = np.zeros(
self.columnCount,
dtype=cltypes.uint2) # array of overlap and boosted overlap scores
self.cl_boost_factors = cl.Buffer(self._ctx,
mf.READ_WRITE | mf.COPY_HOST_PTR,
hostbuf=self._boostFactors)
self.cl_overlap = cl.Buffer(self._ctx, mf.READ_WRITE,
self.overlap.nbytes)
encoding_temp = np.empty(
inputWidth,
dtype=cltypes.uchar) # output is a np.uint8 == cltypes.uchar
self.cl_encoding = cl.Buffer(self._ctx,
mf.READ_ONLY,
size=encoding_temp.nbytes)
self.active_bits = np.zeros(inputActive, dtype=cltypes.long)
self.inputActive = inputActive
self.cl_active_bits = cl.Buffer(self._ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=self.active_bits)
self.cl_synapses = cl.Buffer(self._ctx,
mf.READ_WRITE | mf.COPY_HOST_PTR,
hostbuf=self.synapses)
self.cl_input_bitIdx = cl.Buffer(self._ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=self.input_bitIdx)
self.cl_synapses_no_bit = cl.Buffer(self._ctx,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=self.synapses_no_bit)
