corrected convolution channel count

Author: sethjuarez

notebooks/kernels.ipynb

@@ -56,7 +56,8 @@
    "source": [
     "def conv2d(X, W, pad=1, stride=1):\n",
     "    # filter\\kernel size\n",
-    "    f, f, n_C = W.shape\n",
+    "    f, f, _ = W.shape\n",
+    "    n_C = 1\n",
     "    \n",
     "    # new output volume\n",
     "    n_H = int(np.floor(X.shape[0] - f + 2 * pad) / (stride * 1.)) + 1\n",
@@ -83,7 +84,7 @@
    "metadata": {},
    "source": [
     "# Max Pooling\n",
-    "Size reduction via max pooling!"
+    "Implements max pooling with the given pool size and stride."
    ]
   },
   {
@@ -94,8 +95,8 @@
    "source": [
     "def max_pooling2d(X, pool_size=2, stride=2):\n",
     "    # new output volume\n",
-    "    n_H = int(np.floor(X.shape[0] - pool_size + 2) / (stride * 1.)) #+ 1\n",
-    "    n_W = int(np.floor(X.shape[1] - pool_size + 2) / (stride * 1.)) #+ 1\n",
+    "    n_H = int(np.floor(X.shape[0] - pool_size) / (stride * 1.)) + 1\n",
+    "    n_W = int(np.floor(X.shape[1] - pool_size) / (stride * 1.)) + 1\n",
     "    n_C = X.shape[2]\n",
     "    \n",
     "    Z = np.zeros((n_H, n_W, n_C))\n",
@@ -125,12 +126,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "w = np.zeros((3, 3, 1))\n",
+    "w = np.zeros((3, 3, 3))\n",
     "t = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]]) / 3\n",
     "#t = np.array([[0, 0, 0], [0, 1., 0], [0, 0, 0]]) / 3\n",
     "w[:,:,0] = t\n",
-    "#w[:,:,1] = t\n",
-    "#w[:,:,2] = t"
+    "w[:,:,1] = t\n",
+    "w[:,:,2] = t"
    ]
   },
   {
@@ -147,7 +148,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "image = np.array(open_and_resize(\"fence.jpg\", resize=(400,400)))\n",
+    "image = np.array(open_and_resize(\"wedding.jpg\", resize=(400,400)))\n",
     "plt.imshow(image)"
    ]
   },