add ipython notebooks

.gitignore

@@ -52,3 +52,6 @@ docs/_build/
 
 # PyBuilder
 target/
+
+#ipython notebook
+.ipynb_checkpoints/

README.md

@@ -2,3 +2,6 @@ Theano-Tutorials
 ================
 
 Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.
+
+See [ipython notebooks](http://nbviewer.ipython.org/github/udibr/Theano-Tutorials/blob/master/notebooks/index.ipynb)
+

load.py

@@ -13,19 +13,19 @@ def one_hot(x,n):
 
 def mnist(ntrain=60000,ntest=10000,onehot=True):
 	data_dir = os.path.join(datasets_dir,'mnist/')
-	fd = open(os.path.join(data_dir,'train-images.idx3-ubyte'))
+	fd = open(os.path.join(data_dir,'train-images-idx3-ubyte'))
 	loaded = np.fromfile(file=fd,dtype=np.uint8)
 	trX = loaded[16:].reshape((60000,28*28)).astype(float)
 
-	fd = open(os.path.join(data_dir,'train-labels.idx1-ubyte'))
+	fd = open(os.path.join(data_dir,'train-labels-idx1-ubyte'))
 	loaded = np.fromfile(file=fd,dtype=np.uint8)
 	trY = loaded[8:].reshape((60000))
 
-	fd = open(os.path.join(data_dir,'t10k-images.idx3-ubyte'))
+	fd = open(os.path.join(data_dir,'t10k-images-idx3-ubyte'))
 	loaded = np.fromfile(file=fd,dtype=np.uint8)
 	teX = loaded[16:].reshape((10000,28*28)).astype(float)
 
-	fd = open(os.path.join(data_dir,'t10k-labels.idx1-ubyte'))
+	fd = open(os.path.join(data_dir,'t10k-labels-idx1-ubyte'))
 	loaded = np.fromfile(file=fd,dtype=np.uint8)
 	teY = loaded[8:].reshape((10000))
 
@@ -45,4 +45,4 @@ def mnist(ntrain=60000,ntest=10000,onehot=True):
 		trY = np.asarray(trY)
 		teY = np.asarray(teY)
 
-	return trX,teX,trY,teY
+	return trX,teX,trY,teY

notebooks/0_multiply.ipynb

@@ -0,0 +1,143 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:f13eb3b414e3e34aa7531d120d38fcd6602672de65630585a7952395a63c3f7e"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "import Theano"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "tensor is the section of theano that deals with data (like numpy)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import theano\n",
+      "from theano import tensor as T"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "create Theano symbolic variables"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a = T.scalar()\n",
+      "b = T.scalar()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "our model"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "y = a * b"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 3
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "compile to a python function (to CPU or GPU depending on configuration)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "multiply = theano.function(inputs=[a, b], outputs=y)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 4
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "and use the function"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "multiply(1, 2)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 5,
+       "text": [
+        "array(2.0)"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "multiply(3, 3)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 6,
+       "text": [
+        "array(9.0)"
+       ]
+      }
+     ],
+     "prompt_number": 6
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}