Added a conv net notebook but looks like it is running awfully slow

Author: calvinfeng

.gitignore

@@ -1,2 +1,3 @@
 environment/
 *.pyc
+.ipynb_checkpoints/

convolutional_neural_net.ipynb

@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Convolutional Neural Network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X_val has shape: (1000, 3, 32, 32)\n",
+      "X_train has shape: (49000, 3, 32, 32)\n",
+      "X_test has shape: (1000, 3, 32, 32)\n",
+      "y_val has shape: (1000,)\n",
+      "y_train has shape: (49000,)\n",
+      "y_test has shape: (1000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from data_utils import get_preprocessed_CIFAR10\n",
+    "\n",
+    "# Let's get some data in first\n",
+    "feed_dict = get_preprocessed_CIFAR10('datasets/cifar-10-batches-py')\n",
+    "\n",
+    "for key, value in feed_dict.iteritems():\n",
+    "    print \"%s has shape: %s\" % (key, value.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(Iteration 1 / 1960) loss: 2.303022\n",
+      "(Epoch 0 / 4) train acc: 0.123000; val_acc: 0.111000\n",
+      "(Iteration 11 / 1960) loss: 2.291381\n",
+      "(Iteration 21 / 1960) loss: 2.257190\n",
+      "(Iteration 31 / 1960) loss: 2.203646\n",
+      "(Iteration 41 / 1960) loss: 2.176024\n",
+      "(Iteration 51 / 1960) loss: 2.069092\n",
+      "(Iteration 61 / 1960) loss: 2.036589\n",
+      "(Iteration 71 / 1960) loss: 1.912840\n",
+      "(Iteration 81 / 1960) loss: 1.929446\n",
+      "(Iteration 91 / 1960) loss: 1.831559\n",
+      "(Iteration 101 / 1960) loss: 1.787254\n",
+      "(Iteration 111 / 1960) loss: 1.776150\n",
+      "(Iteration 121 / 1960) loss: 1.642039"
+     ]
+    }
+   ],
+   "source": [
+    "from conv_net_model import ConvNetModel\n",
+    "from solver import Solver\n",
+    "import time\n",
+    "\n",
+    "# Define the model\n",
+    "model = ConvNetModel()\n",
+    "t0 = time.time()\n",
+    "solver = Solver(model, \n",
+    "                feed_dict, \n",
+    "                update_rule='sgd_momentum', \n",
+    "                num_epochs=4, \n",
+    "                batch_size=100, \n",
+    "                optim_config={'learning_rate': 1e-3},\n",
+    "                verbose=True)\n",
+    "solver.train()\n",
+    "tf = time.time()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}