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15:10:38.984 [main] INFO org.nd4j.nativeblas.NativeOps - Number of threads used for NativeOps: 32 | |
15:10:48.878 [main] INFO org.nd4j.nativeblas.Nd4jBlas - Number of threads used for BLAS: 4 | |
15:11:58.989 [main] DEBUG org.reflections.Reflections - going to scan these urls: | |
jar:file:/simplex/simulator/arcadia-dealing-simulator.jar!/ | |
15:11:59.760 [main] INFO org.reflections.Reflections - Reflections took 766 ms to scan 1 urls, producing 29 keys and 172 values | |
15:12:05.823 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]... | |
15:12:05.826 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]... | |
15:12:05.826 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]... | |
15:12:05.827 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]... | |
15:12:05.827 [main] DEBUG o.n.j.a.c.impl.BasicContextPool - Creating new stream for thread: [1], device: [0]... |
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SLF4J: Failed to load class "org.slf4j.impl.StaticLoggerBinder". | |
SLF4J: Defaulting to no-operation (NOP) logger implementation | |
SLF4J: See http://www.slf4j.org/codes.html#StaticLoggerBinder for further details. | |
CudaGridExecutioner | |
Build model.... | |
Beginning training | |
Epoch = 0 | |
Batch 000 [150, 3] [150, 3, 32, 16] | |
CUDA error at /projects/skymind/libnd4j/blas/cuda/NativeOps.cu:4969 code=77(<unknown>) "result" |
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Multiple annotations found at this line: | |
- Missing artifact concurrent:concurrent:jar:1.3.4 | |
- Missing artifact jna:jna:jar:1.0 | |
- Missing artifact commons-net:commons-net:jar:2.2 | |
- Missing artifact com.github.lucarosellini.rJava:JRI:jar:0.9 | |
- Missing artifact org.apache.commons:commons-lang3:jar:3.2 | |
- Missing artifact poi-ooxml:poi-ooxml:jar:3.7 | |
- Missing artifact log4j:log4j:jar:1.2.14 | |
- Missing artifact simplex.galaxy.l3j:l3j:jar:1.1.80 | |
- Missing artifact mail:mail:jar:1.0.0 |
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""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
import numpy as np | |
import cPickle as pickle | |
import gym | |
# hyperparameters | |
H = 200 # number of hidden layer neurons | |
batch_size = 10 # every how many episodes to do a param update? | |
learning_rate = 1e-4 | |
gamma = 0.99 # discount factor for reward |
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function generate_batch(batch_size, num_skips, skip_window, data_index) | |
@assert batch_size % num_skips == 0 | |
@assert num_skips <= 2 * skip_window | |
batch = zeros(Int32, (batch_size)) | |
labels = zeros(Int32, (batch_size,1)) | |
span = 2 * skip_window + 1 | |
buffer = Int[] | |
for _ in 1:span | |
push!(buffer,data[data_index]) |
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using Dates | |
# set_default_plot_size(20cm, 10cm) | |
# simple Integrate and Fire Synapse Mode | |
type Synapse | |
Ω::Real # membrane resistance [MΩ] | |
τ::Real # membrane time constant [ms] | |
rV::Real # resting membrane potential [mV] | |
thV::Real # spike threshold [mV] | |
sV::Real # spike voltage [mV] |
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# This code is part of a presentation on streaming analytics in Julia | |
# It was inspired by a number of individuals and makes use of some of their ideas | |
# 1. FastML.com got me thinking about inline processing after | |
# reading his great Vowpal Wabbit posts | |
# 2. John Lanford and his fantastic Vowpal Wabbit library. | |
# Check out his NYU video course to learn more (see below) | |
# 3. John Myles White's presentation on online SDG and his StreamStats.jl library | |
# Thank you all! | |