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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Table of Contents\n",
"* [Model](#Model)\n",
"* [Example 1](#Example-1)\n",
"* [Example 2](#Example-2)\n",
"* [Example 3](#Example-3)\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Couldn't import dot_parser, loading of dot files will not be possible.\n"
]
}
],
"source": [
"import numpy\n",
"import theano\n",
"import theano.tensor as T\n",
"import theano.printing as pr\n",
"import theano.d3printing as d3p\n",
"rng = numpy.random"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Training data\n",
"N = 400\n",
"feats = 784\n",
"D = (rng.randn(N, feats).astype(theano.config.floatX), rng.randint(size=N,low=0, high=2).astype(theano.config.floatX))\n",
"training_steps = 10000\n",
"\n",
"# Declare Theano symbolic variables\n",
"x = T.matrix(\"x\")\n",
"y = T.vector(\"y\")\n",
"w = theano.shared(rng.randn(feats).astype(theano.config.floatX), name=\"w\")\n",
"b = theano.shared(numpy.asarray(0., dtype=theano.config.floatX), name=\"b\")\n",
"x.tag.test_value = D[0]\n",
"y.tag.test_value = D[1]\n",
"\n",
"# Construct Theano expression graph\n",
"p_1 = 1 / (1 + T.exp(-T.dot(x, w)-b)) # Probability of having a one\n",
"prediction = p_1 > 0.5 # The prediction that is done: 0 or 1\n",
"\n",
"# Compute gradients\n",
"xent = -y*T.log(p_1) - (1-y)*T.log(1-p_1) # Cross-entropy\n",
"cost = xent.mean() + 0.01*(w**2).sum() # The cost to optimize\n",
"gw,gb = T.grad(cost, [w,b])\n",
"\n",
"# Training and prediction function\n",
"train = theano.function(inputs=[x,y], outputs=[prediction, xent], updates=[[w, w-0.01*gw], [b, b-0.01*gb]], name = \"train\")\n",
"predict = theano.function(inputs=[x], outputs=prediction, name = \"predict\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 1 "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The output file is available at p1.png\n"
]
}
],
"source": [
"pr.pydotprint(p_1, outfile='p1.png', var_with_name_simple=True)\n",
"d3p.d3write(p_1, 'p1.dot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href='p1.html'><img src='p1.png'/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[open](./p1.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The output file is available at predict.png\n"
]
}
],
"source": [
"pr.pydotprint(predict, outfile='predict.png', var_with_name_simple=True)\n",
"d3p.d3write(predict, 'predict.dot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href='predict.html'><img src='predict.png'/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[open](./predict.html)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 3"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The output file is available at train.png\n"
]
}
],
"source": [
"pr.pydotprint(train, outfile='train.png', var_with_name_simple=True)\n",
"d3p.d3write(train, 'train.dot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href='train.html'><img src='train.png'/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[open](./train.html)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
doc/d3printing/GSoC/150620_layout/p1.dot 0 → 100644
浏览文件 @ 1f1f8eda
digraph G { graph [bb="0,0,719,672"]; "DimShuffle{x}" [height=0.5, pos="558,478", shape=ellipse, width=1.8374]; "Elemwise{sub,no_inplace}" [fillcolor="#FFAABB", height=0.5, pos="461,390", shape=ellipse, style=filled, width=3.0624]; "DimShuffle{x}" -> "Elemwise{sub,no_inplace}" [label="1 TensorType(float64, (True,))", lp="632.5,434", pos="e,506.93,406.57 553.52,459.62 549.99,448.82 544.12,435.29 535,426 529.47,420.37 522.85,415.53 515.89,411.41"]; "name=b TensorType(float64, scalar)" [fillcolor=limegreen, height=0.5, pos="558,566", shape=box, style=filled, width=3.0625]; "name=b TensorType(float64, scalar)" -> "DimShuffle{x}" [label="TensorType(float64, scalar)", lp="636,522", pos="e,558,496.08 558,547.6 558,535.75 558,519.82 558,506.29"]; dot [height=0.5, pos="363,566", shape=ellipse, width=0.75]; "Elemwise{neg,no_inplace}" [fillcolor="#FFAABB", height=0.5, pos="363,478", shape=ellipse, style=filled, width=3.0624]; dot -> "Elemwise{neg,no_inplace}" [label="TensorType(float64, vector)", lp="442,522", pos="e,363,496.08 363,547.6 363,535.75 363,519.82 363,506.29"]; "name=x TensorType(float64, matrix)" [fillcolor=limegreen, height=0.5, pos="241,654", shape=box, style=filled, width=3.1181]; "name=x TensorType(float64, matrix)" -> dot [label="0 TensorType(float64, matrix)", lp="378,610", pos="e,340.56,576.07 255.77,636 265.61,625.35 279.21,611.84 293,602 304.81,593.57 318.95,586.09 331.42,580.22"]; "name=w TensorType(float64, vector)" [fillcolor=limegreen, height=0.5, pos="485,654", shape=box, style=filled, width=3.1389]; "name=w TensorType(float64, vector)" -> dot [label="1 TensorType(float64, vector)", lp="559.5,610", pos="e,389.41,570.28 481.35,635.78 478.2,624.78 472.59,610.94 463,602 445.54,585.73 420.08,576.96 399.23,572.27"]; "DimShuffle{x} id=2" [height=0.5, pos="247,302", shape=ellipse, width=2.3721]; "Elemwise{add,no_inplace}" [fillcolor="#FFAABB", height=0.5, pos="369,194", shape=ellipse, style=filled, width=3.0624]; "DimShuffle{x} id=2" -> "Elemwise{add,no_inplace}" [label="0 TensorType(int8, (True,))", lp="334,248", pos="e,290.21,206.6 244.34,283.76 242.92,267.96 243.54,244.85 256,230 262.8,221.89 271.45,215.63 280.91,210.8"]; "val=1 TensorType(int8, scalar)" [fillcolor=limegreen, height=0.5, pos="161,390", shape=box, style=filled, width=2.6389]; "val=1 TensorType(int8, scalar)" -> "DimShuffle{x} id=2" [label="TensorType(int8, scalar)", lp="305.5,346", pos="e,242.7,320.2 201.81,371.98 210.24,367.09 218.52,361.12 225,354 231.25,347.13 235.92,338.22 239.3,329.76"]; "DimShuffle{x} id=3" [height=0.5, pos="85,248", shape=ellipse, width=2.3721]; "val=1 TensorType(int8, scalar)" -> "DimShuffle{x} id=3" [label="TensorType(int8, scalar)", lp="151.5,346", pos="e,78.947,266.23 101.23,371.9 93.625,367.28 86.827,361.42 82,354 66.992,330.94 70.718,298.58 76.308,275.9"]; "Elemwise{true_div,no_inplace}" [fillcolor="#FFAABB", height=0.5, pos="164,106", shape=ellipse, style=filled, width=3.5561]; "DimShuffle{x} id=3" -> "Elemwise{true_div,no_inplace}" [label="0 TensorType(int8, (True,))", lp="172,194", pos="e,140.09,123.74 84.103,229.75 84.024,214.78 85.627,192.93 94,176 102.79,158.23 118.02,142.37 132.04,130.36"]; "Elemwise{neg,no_inplace}" -> "Elemwise{sub,no_inplace}" [label="0 TensorType(float64, vector)", lp="446.5,434", pos="e,389.83,403.99 357.87,459.89 355.74,449.21 355.15,435.69 362,426 366.99,418.94 373.53,413.29 380.88,408.78"]; "Elemwise{exp,no_inplace}" [fillcolor="#FFAABB", height=0.5, pos="461,302", shape=ellipse, style=filled, width=3.0624]; "Elemwise{sub,no_inplace}" -> "Elemwise{exp,no_inplace}" [label="TensorType(float64, vector)", lp="540,346", pos="e,461,320.08 461,371.6 461,359.75 461,343.82 461,330.29"]; "Elemwise{exp,no_inplace}" -> "Elemwise{add,no_inplace}" [label="1 TensorType(float64, vector)", lp="526.5,248", pos="e,394.8,211.6 452.54,284.02 444.46,268.82 431.28,246.48 416,230 412.01,225.7 407.43,221.56 402.74,217.73"]; "Elemwise{add,no_inplace}" -> "Elemwise{true_div,no_inplace}" [label="1 TensorType(float64, vector)", lp="369.5,150", pos="e,202.57,123.18 330.92,177.03 297.33,162.93 248.22,142.33 211.96,127.12"]; "TensorType(float64, vector) id=12" [fillcolor=dodgerblue, height=0.5, pos="164,18", shape=box, style=filled, width=2.9236]; "Elemwise{true_div,no_inplace}" -> "TensorType(float64, vector) id=12" [label="TensorType(float64, vector)", lp="243,62", pos="e,164,36.084 164,87.597 164,75.746 164,59.817 164,46.292"]; }
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doc/d3printing/GSoC/150620_layout/p1.html 0 → 100644
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差异被折叠。
doc/d3printing/GSoC/150620_layout/predict.dot 0 → 100644
浏览文件 @ 1f1f8eda
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doc/d3printing/GSoC/150620_layout/predict.html 0 → 100644
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doc/d3printing/GSoC/150620_layout/train.dot 0 → 100644
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doc/d3printing/GSoC/150620_layout/train.html 0 → 100644
浏览文件 @ 1f1f8eda
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