Update d3print method to write dot and html file

doc/d3printing/GSoC/150628_edit/index.ipynb

+{
+ "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": 5,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at p1.png\n",
+      "The output file is available at p1.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "pr.pydotprint(p_1, outfile='p1.png', var_with_name_simple=True)\n",
+    "d3p.d3print(p_1, 'p1.html')"
+   ]
+  },
+  {
+   "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": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at predict.png\n",
+      "The output file is available at predict.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "pr.pydotprint(predict, outfile='predict.png', var_with_name_simple=True)\n",
+    "d3p.d3print(predict, 'predict.html')"
+   ]
+  },
+  {
+   "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": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The output file is available at train.png\n",
+      "The output file is available at train.html\n"
+     ]
+    }
+   ],
+   "source": [
+    "pr.pydotprint(train, outfile='train.png', var_with_name_simple=True)\n",
+    "d3p.d3print(train, 'train.html')"
+   ]
+  },
+  {
+   "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/150628_edit/p1.dot

+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"]; } 
\ No newline at end of file

theano/d3printing/d3printing.py

@@ -3,6 +3,7 @@
 # Authors: Christof Angermueller <cangermueller@gmail.com>
 
 import os.path
+
 from theano.printing import pydotprint
 
 
@@ -46,8 +47,11 @@ def d3print(fct, outfile=None, return_html=False, print_message=True,
     :param *args, **kwargs: Parameters passed to pydotprint
     """
 
-    # Generate dot graph definition by calling pydotprint
+    # Generate dot graph by pydotprint and write to file
     dot_graph = d3dot(fct, *args, **kwargs)
+    dot_file = os.path.splitext(outfile)[0] + '.dot'
+    with open(dot_file, 'w') as f:
+        f.write(dot_graph)
 
     # Read template HTML file and replace variables
     template_file = os.path.join(os.path.dirname(os.path.realpath(__file__)),
@@ -56,7 +60,7 @@ def d3print(fct, outfile=None, return_html=False, print_message=True,
     template = f.read()
     f.close()
     replace = {
-        '%% DOT_GRAPH %%': dot_graph,
+        '%% DOT_FILE %%': dot_file,
     }
     html = replace_patterns(template, replace)