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# notebook.sh | |
#!/bin/sh | |
jupyter-notebook --ip=0.0.0.0 2>&1 | tee notebook_output.log | sed -n "/^ *http.*8888/s/ *http.*token=//p" | |
# note_on_docker.sh | |
#!/bin/bash | |
pushd ~/ClassSim; | |
docker run -it --rm --publish 52688:8888 --publish 6006:6006 -v $(pwd):/work arino/tfcpu2 /work/notebook.sh |
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#include "util.h" | |
#define TILE_WIDTH 16 | |
// Compute C = A * B | |
__global__ void matrixMultiplyShared(float* A, float* B, float* C, | |
int numARows, int numACols, | |
int numBRows, int numBCols, | |
int numCRows, int numCCols) | |
{ |
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__global__ void linear(float* x, | |
int numXRows, int numXCols) | |
{ | |
__shared__ float W [TILE_WIDTH][TILE_WIDTH]; | |
__shared__ float b [TILE_WIDTH][TILE_WIDTH]; | |
// x*W+b | |
} |
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<us-patent-grant lang="EN" dtd-version="v4.2 2006-08-23" file="US08160929-20120417.XML" status="PRODUCTION" id="us-patent-grant" country="US" date-produced="20120403" date-publ="20120417"> | |
<us-bibliographic-data-grant> | |
<publication-reference> | |
<document-id> | |
<country>US</country> | |
<doc-number>08160929</doc-number> | |
<kind>B1</kind> | |
<date>20120417</date> | |
</document-id> | |
</publication-reference> |
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<!DOCTYPE us-patent-application SYSTEM "us-patent-application-v44-2014-04-03.dtd" [ ]> | |
<us-patent-application lang="EN" dtd-version="v4.4 2014-04-03" file="US20170347517A1-20171207.XML" status="PRODUCTION" id="us-patent-application" country="US" date-produced="20171121" date-publ="20171207"> | |
<us-bibliographic-data-application lang="EN" country="US"> | |
<publication-reference> | |
<document-id> | |
<country>US</country> | |
<doc-number>20170347517</doc-number> | |
<kind>A1</kind> | |
<date>20171207</date> | |
</document-id> |
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#include <stdio.h> | |
#include <string.h> | |
#include <stdlib.h> | |
#include <sys/mman.h> | |
#include "parser.h" | |
#include "test_util.h" | |
extern int eval(int r0, int r1, char *str); |
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DROPOUT_RATE=0.5 | |
L2_REGULARIZATION_RATE=0.1 | |
FEATURE_EXTRACTER_KERNEL_SIZE=7 | |
FILTER_NUM=128 | |
KERNEL_SIZE=5 | |
# model_small |
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DROPOUT_RATE=0.5 | |
L2_REGULARIZATION_RATE=0.1 | |
EXTRACTED_FETURE_DIM=256 | |
FEATURE_EXTRACTER_KERNEL_SIZE=7 | |
GRU_HIDDEN=256 | |
# tpu_model_fn also modify for update moving avg. | |
def fe_conv1d(filternum, kernelsize, x): |
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char_names = ["A","B","C","E","F","G","H","I","L","M","N", | |
"P","R", "S","T","V","X","Y", | |
"a","b", "c","d", "e", "f","g","h", "i", | |
"j","k","l","m", "n","o","p","q","r","s","t", "u", | |
"v","w","x","y", "z", | |
"\\alpha","\\beta","\\gamma","\\phi","\\mu", | |
"\\lambda","\\sigma", "\\pi", "\\theta"] | |
binop_names = ["+", "-", "\\times", "\\div", "\\pm"] | |
eqop_names = ["\\geq", ">", "\\leq", "<", "="] | |
nonzero_num_names = [str(i) for i in range(1, 10)] |
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VOCAB_SIZE=115 | |
MAX_ONE_STROKE_LEN=50 | |
MAX_STROKE_NUM=22 | |
MAX_TOKEN_LEN=10+2 | |
# must match to trained dim | |
EXTRACTED_FEATURE_DIM=256 | |
FE_DROPOUT_RATE=0.5 | |
FE_L2_REGULARIZATION_RATE=0.01 |