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net = models.vgg16(pretrained=True)
net.classifier[6] = nn.Linear(in_features=4096, out_features=2)

params_to_update = []

update_param_names = ["classifier.6.weight", "classifier.6.bias"]

for name, param in net.named_parameters():
    if name in update_param_names:
        param.requires_grad = True

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params_to_update_1 = []
params_to_update_2 = []
params_to_update_3 = []

update_param_names_1 = ['features']
update_param_names_2 = ['classifier.0.weight',
                        'classifier.0.bias',
                        'classifier.3.weight',
                        'classifier.3.bias']
update_param_names_3 = ['classifier.6.weight',

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    var outerWidth = 300;
    var outerHeight = 250;
    var rMin = 5;
    var rMax = 20;
    var xColumn = "sepal_length";
    var yColumn = "petal_length";
    var rColumn = "sepal_width";
    var colorColumn = "variety";
    
    var svg = d3.select("body").append("svg")

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var svgWidth = 600, svgHeight = 500;
var svg = d3.select("svg")
    .attr("width", svgWidth)
    .attr("height", svgHeight)
    .attr("class", "svg-container");

var line = svg.append("line")
    .attr("x1", 100)
    .attr("x2", 500)
    .attr("y1", 50)

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var dataset = [80, 100, 56, 120, 180, 30, 40, 120, 160];
// var dataset = [1, 2, 3, 4, 5];

var svgWidth = 500, svgHeight = 300, barPadding = 5;
var barWidth = (svgWidth / dataset.length);  // 各バーの幅

var svg = d3.select('svg')
    .attr('width', svgWidth)
    .attr('height', svgHeight);

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var dataset = [80, 100, 56, 120, 180, 30, 40, 120, 160];

var svgWidth = 500, svgHeight = 300, barPadding = 5;
var barWidth = (svgWidth / dataset.length);  // 各バーの幅

var svg = d3.select('svg')
    .attr('width', svgWidth)
    .attr('height', svgHeight);

var barChart = svg.selectAll('rect')

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var dataset = [1, 2, 3, 4, 5, 6];

d3.select('body')
    .selectAll('p')
    .data(dataset)  // datasetを指定
    .enter()        // 読み込む？
    .append('p')
    .text(function(dd) { return '(' + dd + ')'; });

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d3.select('h1').style('color', 'red')
    .attr('class', 'heading')
    .text('Updated h1 tag');

d3.select('body').append('p').text('First paragraph');
d3.select('body').append('p').text('Second paragraph');
d3.select('body').append('p').text('Third paragraph');

d3.selectAll('p').style('color', 'blue');

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FROM nvidia/cuda:8.0-cudnn5-devel-ubuntu16.04
 
RUN apt-get update
RUN apt-get install -y git wget curl emacs
# pyenv
ENV PYENV_ROOT $HOME/.pyenv
ENV PATH $PYENV_ROOT/shims:$PYENV_ROOT/bin:$PATH
ENV PYTHON_VERSION anaconda3-4.2.0
 
RUN git clone https://github.com/pyenv/pyenv.git $PYENV_ROOT

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'''Train CIFAR10 with PyTorch.'''
from __future__ import print_function

import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn

import torchvision
import torchvision.transforms as transforms