Created using Colaboratory

Author: lmoroney

Lab5-Using-Convolutions-With-Complex-Images.ipynb

@@ -5,8 +5,7 @@
     "colab": {
       "name": "Lab5-Using-Convolutions-With-Complex-Images.ipynb",
       "provenance": [],
-      "collapsed_sections": [],
-      "toc_visible": true
+      "collapsed_sections": []
     },
     "kernelspec": {
       "name": "python3",
@@ -35,7 +34,7 @@
         "# See the License for the specific language governing permissions and\n",
         "# limitations under the License."
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -81,7 +80,7 @@
         "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/validation-horse-or-human.zip \\\n",
         "    -O /tmp/validation-horse-or-human.zip"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -113,7 +112,7 @@
         "zip_ref.extractall('/tmp/validation-horse-or-human')\n",
         "zip_ref.close()"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -154,7 +153,7 @@
         "# Directory with our training human pictures\n",
         "validation_human_dir = os.path.join('/tmp/validation-horse-or-human/humans')"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -181,7 +180,7 @@
         "train_human_names = os.listdir(train_human_dir)\n",
         "print(train_human_names[:10])"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -205,7 +204,7 @@
         "print('total training horse images:', len(os.listdir(train_horse_dir)))\n",
         "print('total training human images:', len(os.listdir(train_human_dir)))"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -238,7 +237,7 @@
         "# Index for iterating over images\n",
         "pic_index = 0"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -279,7 +278,7 @@
         "\n",
         "plt.show()\n"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -310,7 +309,7 @@
         "except Exception:\n",
         "  pass"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -324,7 +323,7 @@
         "import tensorflow as tf\n",
         "print(tf.__version__)"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -370,7 +369,7 @@
         "    tf.keras.layers.Dense(1, activation='sigmoid')\n",
         "])\n"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -393,7 +392,7 @@
       "source": [
         "model.summary()"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -432,7 +431,7 @@
         "              optimizer=RMSprop(lr=0.001),\n",
         "              metrics=['accuracy'])"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -482,7 +481,7 @@
         "        # Since we use binary_crossentropy loss, we need binary labels\n",
         "        class_mode='binary')\n"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -516,7 +515,7 @@
         "      validation_steps=8,\n",
         "      verbose=1)"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -572,7 +571,7 @@
         "    print(fn + \" is a horse\")\n",
         " "
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -639,7 +638,8 @@
         "      # Postprocess the feature to make it visually palatable\n",
         "      x = feature_map[0, :, :, i]\n",
         "      x -= x.mean()\n",
-        "      x /= x.std()\n",
+        "      if x>0:\n",
+        "        x /= x.std()\n",
         "      x *= 64\n",
         "      x += 128\n",
         "      x = np.clip(x, 0, 255).astype('uint8')\n",
@@ -652,7 +652,7 @@
         "    plt.grid(False)\n",
         "    plt.imshow(display_grid, aspect='auto', cmap='viridis')"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -691,7 +691,7 @@
         "import os, signal\n",
         "os.kill(os.getpid(), signal.SIGKILL)"
       ],
-      "execution_count": 0,
+      "execution_count": null,
       "outputs": []
     }
   ]