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from __future__ import print_function | |
import numpy as np | |
import os | |
import gzip | |
import sys | |
import time | |
from collections import OrderedDict | |
floatX = "float32" | |
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// | |
// Created by alex on 18/01/16. | |
// | |
// Compiled with | |
// MKL_NUM_THREADS=4 OMP_NUM_THREADS=4 g++ -O3 -std=c++11 \ | |
// -I/opt/eigen -I/opt/intel/mkl/include -Wall -L/opt/intel/mkl/lib/intel64 \ | |
// -Werror=return-type -Wno-unused-variable -Wno-narrowing -Wl,--no-as-needed \ | |
// -m64 -fopenmp -lmkl_rt -lpthread -lcurl -lm eigen.cpp | |
#define EIGEN_USE_MKL_ALL | |
#include "Eigen/Core" |
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// | |
// Created by alex on 26/04/16. | |
// | |
#include "arrayfire.h" | |
#include "myreduce.h" | |
#include "stdio.h" | |
#include "iostream" | |
static const int n = 100; | |
static const int M = 200; |
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#include <iostream> | |
#include <vector> | |
#include <map> | |
#include <algorithm> | |
#include <iterator> | |
#include "arrayfire.h" | |
class AbstractDataSource{ | |
protected: | |
unsigned instance_dim; |
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// | |
// Created by alex on 17/06/16. | |
// | |
#include "arrayfire.h" | |
#include "iomanip" | |
class BinaryCrossEntropy{ | |
public: | |
inline | |
af::array func(af::array prediction, af::array targets){ |
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<!doctype html> | |
<html> | |
<head> | |
<title>MNIST</title> | |
<!-- jQuery --> | |
<script src="http://code.jquery.com/jquery-2.0.3.min.js"></script> | |
<!-- Cytoscape --> | |
<script src="http://cytoscape.github.io/cytoscape.js/api/cytoscape.js-latest/cytoscape.min.js"></script> | |
<!-- Dagre --> | |
<script src="https://cdn.rawgit.com/cpettitt/dagre/v0.7.4/dist/dagre.min.js"></script> |
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use std::collections::HashMap; | |
extern crate symints; | |
use symints::*; | |
type SymInt = Polynomial<String, i64, u8>; | |
type Shape = (SymInt, SymInt, SymInt, SymInt); | |
enum ConvolutionMode { |
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from __future__ import absolute_import, print_function, division | |
import numpy as np | |
import theano | |
from theano import Op | |
import theano.tensor as T | |
from theano.gpuarray.basic_ops import (gpu_contiguous, as_gpuarray_variable, | |
infer_context_name) | |
from theano.gpuarray.type import get_context |
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import numpy as np | |
import argparse | |
import theano | |
import theano.tensor as T | |
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams | |
from theano.gradient import zero_grad | |
import lasagne.layers as L |
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