From 9e22b43205f41cf6d424abb7f8a122a08030abf6 Mon Sep 17 00:00:00 2001
From: pswietojanski <p.swietojanski@gmail.com>
Date: Sun, 1 Nov 2015 19:29:42 +0000
Subject: [PATCH] lab 4

---
 03_MLP_Coursework1.ipynb | 65 ++++------------------------------------
 1 file changed, 5 insertions(+), 60 deletions(-)

diff --git a/03_MLP_Coursework1.ipynb b/03_MLP_Coursework1.ipynb
index 021b761..28be3a1 100644
--- a/03_MLP_Coursework1.ipynb
+++ b/03_MLP_Coursework1.ipynb
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
@@ -146,39 +146,7 @@
    },
    "outputs": [],
    "source": [
-    "# %load -s Sigmoid mlp/layers.py\n",
-    "class Sigmoid(Linear):\n",
-    "    def __init__(self,  idim, odim,\n",
-    "                 rng=None,\n",
-    "                 irange=0.1):\n",
-    "\n",
-    "        super(Sigmoid, self).__init__(idim, odim, rng, irange)\n",
-    "    \n",
-    "    def fprop(self, inputs):\n",
-    "        a = super(Sigmoid, self).fprop(inputs)\n",
-    "        h = 1.0/(1 + numpy.exp(-a))\n",
-    "        return h\n",
-    "    \n",
-    "    def bprop(self, h, igrads):\n",
-    "        dsigm = h*(1.0 - h)\n",
-    "        deltas = igrads*dsigm\n",
-    "        ___, ograds = super(Sigmoid, self).bprop(h=None, igrads=deltas)\n",
-    "        return deltas, ograds\n",
-    "\n",
-    "    def cost_bprop(self, h, igrads, cost):\n",
-    "        if cost is None or cost.get_name() == 'bce':\n",
-    "            return super(Sigmoid, self).bprop(h=h, igrads=igrads)\n",
-    "        else:\n",
-    "            raise NotImplementedError('Sigmoid.bprop_cost method not implemented '\n",
-    "                                      'for the %s cost' % cost.get_name())\n",
-    "\n",
-    "    def pgrads(self, inputs, deltas):\n",
-    "        \"Return list of gradients w.r.t parameters\"\n",
-    "        gparams = super(Sigmoid, self).pgrads(inputs, deltas)\n",
-    "        return gparams\n",
-    "\n",
-    "    def get_name(self):\n",
-    "        return 'sigmoid'\n"
+    "%load -s Sigmoid mlp/layers.py"
    ]
   },
   {
@@ -194,26 +162,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.0\n",
-      "0.0\n",
-      "0.0744177068753\n",
-      "[  4.571e-05   1.697e-03   9.877e-01   6.631e-04   1.194e-04   8.880e-04\n",
-      "   1.977e-04   8.671e-03]\n",
-      "[  4.571e-05   1.697e-03   9.877e-01   6.631e-04   1.194e-04   8.880e-04\n",
-      "   1.977e-04  -9.913e-01]\n",
-      "[-0.089  0.03   0.079  0.011  0.017  0.027]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from mlp.layers import Softmax\n",
     "\n",
@@ -251,15 +204,7 @@
    },
    "outputs": [],
    "source": [
-    "%load -s Softmax mlp/layers.py\n",
-    "1.0\n",
-    "-1.11022302463e-16\n",
-    "0.0744177068753\n",
-    "[  4.571e-05   1.697e-03   9.877e-01   6.631e-04   1.194e-04   8.880e-04\n",
-    "   1.977e-04   8.671e-03]\n",
-    "[  4.571e-05   1.697e-03   9.877e-01   6.631e-04   1.194e-04   8.880e-04\n",
-    "   1.977e-04  -9.913e-01]\n",
-    "[-0.089  0.03   0.079  0.011  0.017  0.027]"
+    "%load -s Softmax mlp/layers.py"
    ]
   },
   {