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hushell/SGD_vs_SVRG.py

Created April 25, 2017 20:40
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SGD_vs_SVRG.py
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"source": [
"import pandas as pd\n",
"import json\n",
"from bokeh.plotting import figure, output_notebook, show, ColumnDataSource\n",
"import numpy as np\n",
"from bokeh.charts.utils import cycle_colors\n",
"from bokeh.models import HoverTool, WheelPanTool, SaveTool, BoxZoomTool\n",
"import sweeper\n",
"import itertools\n",
"\n",
"output_notebook()\n",
"\n",
"def plotLogs(log_names, fields=[]):\n",
" def load_log(log):\n",
" if isinstance(log, list):\n",
" sublogs = [sweeper.loadLog(_log+'/log.txt') for _log in log]\n",
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" return pd.DataFrame(sweeper.loadLog(log+'/log.txt'))\n",
" \n",
" frames = [load_log(log) for log in log_names]\n",
" colors = ['red','blue','green','black','purple','orange','yellow','cyan']\n",
" params = sweeper.findSweepParams(frames) + [u'model']\n",
" if 'dataset' in params:\n",
" params.remove('dataset')\n",
" legends = [sweeper.generateLegend(frame, params) for frame in frames]\n",
"\n",
" fields_hover = ['test_acc', 'train_acc', 'save', 'depth', 'width', 'n_parameters']\n",
" for frame in frames:\n",
" frame['test_acc'] = 100 - frame['test_acc']\n",
" frame['train_acc'] = 100 - frame['train_acc']\n",
" hover = HoverTool(tooltips=[(field, \"@\"+field) for field in fields_hover])\n",
" p = figure(title='test error',\n",
" x_axis_label='epoch',\n",
" width=1000,\n",
" tools = [hover, WheelPanTool(), SaveTool(), BoxZoomTool()])\n",
" for i,frame in enumerate(frames):\n",
" p.line('epoch', 'test_acc', source = ColumnDataSource(data=frame),\n",
" color=colors[i], legend=legends[i])\n",
" p.line('epoch', 'train_acc', source = ColumnDataSource(data=frame),\n",
" color=colors[i], legend=legends[i], line_dash=[4,4])\n",
" show(p)"
]
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"\n",
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"\n",
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"}(this));"
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"source": [
"output_notebook()\n",
"def plotLogsTrain(log_names, fields=[]):\n",
" def load_log(log):\n",
" if isinstance(log, list):\n",
" sublogs = [sweeper.loadLog(_log+'/log.txt') for _log in log]\n",
" return pd.DataFrame(list(itertools.chain.from_iterable(sublogs)))\n",
" else:\n",
" return pd.DataFrame(sweeper.loadLog(log+'/log.txt'))\n",
" \n",
" frames = [load_log(log) for log in log_names]\n",
" colors = ['red','blue','green','black','purple','orange','yellow','cyan']\n",
" params = sweeper.findSweepParams(frames) + [u'model']\n",
" if 'dataset' in params:\n",
" params.remove('dataset')\n",
" legends = [sweeper.generateLegend(frame, params) for frame in frames]\n",
"\n",
" fields_hover = ['test_acc', 'train_acc', 'test_loss', 'train_loss']\n",
" for frame in frames:\n",
" frame['train_acc'] = 100 - frame['train_acc']\n",
" hover = HoverTool(tooltips=[(field, \"@\"+field) for field in fields_hover])\n",
" p = figure(title='train error',\n",
" x_axis_label='epoch',\n",
" width=1000,\n",
" tools = [hover, WheelPanTool(), SaveTool(), BoxZoomTool()])\n",
" for i,frame in enumerate(frames):\n",
" p.line('epoch', 'train_acc', source = ColumnDataSource(data=frame),\n",
" color=colors[i], legend=legends[i], line_dash=[4,4])\n",
" show(p)"
]
},
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" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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" \"output_notebook(resources=INLINE)\\n\"+\n",
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{
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"metadata": {
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},
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{
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" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" document.getElementById(\"7b3ec2f6-6a96-43eb-b601-49518dcba88b\").textContent = \"BokehJS successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"7b3ec2f6-6a96-43eb-b601-49518dcba88b\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid '7b3ec2f6-6a96-43eb-b601-49518dcba88b' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" (function() {\n",
" var fn = function() {\n",
" var docs_json = 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"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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}
},
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}
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