Merge pull request fastai#145 from bckenstler/master

Minor fixes to notebooks
chriii · Aug 16, 2017 · afb6c4f · afb6c4f
2 parents cbafbf3 + 714c9f2
commit afb6c4f
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Showing 14 changed files with 126 additions and 116 deletions.
diff --git a/deeplearning2/DCGAN.ipynb b/deeplearning2/DCGAN.ipynb
@@ -287,7 +287,7 @@
     }
    ],
    "source": [
-    "dl,gl = train(MLP_D, MLP_G, m, 4000)"
+    "dl,gl = train(MLP_D, MLP_G, MLP_m, 8000)"
    ]
   },
   {
@@ -568,7 +568,7 @@
     }
    ],
    "source": [
-    "dl,gl = train(CNN_D, CNN_G, CNN_m, 250)"
+    "dl,gl = train(CNN_D, CNN_G, CNN_m, 2500)"
    ]
   },
   {

diff --git a/deeplearning2/babi-memnn.ipynb b/deeplearning2/babi-memnn.ipynb
@@ -1448,21 +1448,21 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 2",
    "language": "python",
-   "name": "python3"
+   "name": "python2"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 3
+    "version": 2
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "pygments_lexer": "ipython2",
+   "version": "2.7.13"
   }
  },
  "nbformat": 4,

diff --git a/deeplearning2/densenet-keras.ipynb b/deeplearning2/densenet-keras.ipynb
@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -180,7 +182,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
@@ -569,6 +573,7 @@
    "cell_type": "code",
    "execution_count": 21,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -649,7 +654,9 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -697,6 +704,7 @@
    "cell_type": "code",
    "execution_count": 26,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -779,6 +787,7 @@
    "cell_type": "code",
    "execution_count": 28,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -900,6 +909,7 @@
    "cell_type": "code",
    "execution_count": 30,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -981,6 +991,7 @@
    "cell_type": "code",
    "execution_count": 32,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1047,6 +1058,7 @@
    "cell_type": "code",
    "execution_count": 34,
    "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -1123,7 +1135,9 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {},
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1174,7 +1188,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

diff --git a/deeplearning2/imagenet_process.ipynb b/deeplearning2/imagenet_process.ipynb
@@ -184,7 +184,7 @@
    },
    "outputs": [],
    "source": [
-    "model = word2vec.Word2Vec.load_word2vec_format(w2v_path+'.bin', binary=True)\n",
+    "model = word2vec.KeyedVectors.load_word2vec_format(w2v_path+'.bin', binary=True)\n",
     "model.save_word2vec_format(w2v_path+'.txt', binary=False)"
    ]
   },
@@ -1421,19 +1421,6 @@
     "lm.compile('adam','cosine_proximity')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "collapsed": false,
-    "deletable": true,
-    "editable": true
-   },
-   "outputs": [],
-   "source": [
-    "v = np.concatenate([vecs, vecs])"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 68,
@@ -1455,7 +1442,7 @@
     }
    ],
    "source": [
-    "lm.evaluate(features_last, v, verbose=0)"
+    "lm.evaluate(features_last, vecs, verbose=0)"
    ]
   },
   {

diff --git a/deeplearning2/kmeans_test.ipynb b/deeplearning2/kmeans_test.ipynb
@@ -1156,7 +1156,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1170,7 +1170,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

diff --git a/deeplearning2/meanshift.ipynb b/deeplearning2/meanshift.ipynb
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false,
     "deletable": true,
@@ -40,21 +40,19 @@
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
-    "editable": true,
-    "heading_collapsed": true
+    "editable": true
    },
    "source": [
     "## Create data"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true,
     "deletable": true,
-    "editable": true,
-    "hidden": true
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -66,21 +64,19 @@
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
-    "editable": true,
-    "hidden": true
+    "editable": true
    },
    "source": [
     "To generate our data, we're going to pick 6 random points, which we'll call centroids, and for each point we're going to generate 250 random points about it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 3,
    "metadata": {
     "collapsed": true,
     "deletable": true,
-    "editable": true,
-    "hidden": true
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -94,8 +90,7 @@
    "cell_type": "markdown",
    "metadata": {
     "deletable": true,
-    "editable": true,
-    "hidden": true
+    "editable": true
    },
    "source": [
     "Below we can see each centroid marked w/ X, and the coloring associated to each respective cluster."
@@ -107,8 +102,7 @@
    "metadata": {
     "collapsed": true,
     "deletable": true,
-    "editable": true,
-    "hidden": true
+    "editable": true
    },
    "outputs": [],
    "source": [
@@ -127,8 +121,7 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true,
-    "hidden": true
+    "editable": true
    },
    "outputs": [
     {
@@ -840,7 +833,7 @@
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [default]",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -854,7 +847,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

diff --git a/deeplearning2/neural-sr.ipynb b/deeplearning2/neural-sr.ipynb
@@ -358,7 +358,8 @@
    "metadata": {
     "collapsed": false,
     "deletable": true,
-    "editable": true
+    "editable": true,
+    "scrolled": true
    },
    "outputs": [
     {
@@ -376,6 +377,13 @@
     "its = len(arr_hr)//16; its"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NOTE: Batch size must be a multiple of chunk length."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 34,
@@ -763,7 +771,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.0"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,