Adding 3rd dimension to mnist data

Author: DiogenesAnalytics

notebooks/demo/mnist_dataset.ipynb

@@ -58,6 +58,10 @@
     "x_train = x_train.astype(\"float32\") / 255.\n",
     "x_test = x_test.astype(\"float32\") / 255.\n",
     "\n",
+    "# add grayscale dimension\n",
+    "x_train = tf.expand_dims(x_train, axis=-1)\n",
+    "x_test = tf.expand_dims(x_test, axis=-1)\n",
+    "\n",
     "# convert to tf datasets\n",
     "train_ds = tf.data.Dataset.from_tensor_slices((x_train, x_train))\n",
     "test_ds = tf.data.Dataset.from_tensor_slices((x_test, x_test))\n",
@@ -92,10 +96,10 @@
     "\n",
     "# seupt config\n",
     "config = MinNDParams(\n",
-    "    l0={\"input_shape\": (28, 28)},\n",
+    "    l0={\"input_shape\": (28, 28, 1)},\n",
     "    l2={\"units\": 32 * 1},\n",
     "    l3={\"units\": 28 * 28 * 1},\n",
-    "    l4={\"target_shape\": (28, 28)},\n",
+    "    l4={\"target_shape\": (28, 28, 1)},\n",
     ")\n",
     "\n",
     "# get ae instance\n",
@@ -116,10 +120,10 @@
     "from keras.callbacks import EarlyStopping\n",
     "\n",
     "# create callback\n",
-    "early_stop_callback = EarlyStopping(monitor=\"val_loss\", patience=3)\n",
+    "early_stop_callback = EarlyStopping(monitor=\"val_loss\", patience=2)\n",
     "\n",
     "# compile ae\n",
-    "autoencoder.compile(optimizer=\"adam\", loss=\"binary_crossentropy\")\n",
+    "autoencoder.compile(optimizer=\"adam\", loss=\"mean_squared_error\")\n",
     "\n",
     "# begin model fit\n",
     "autoencoder.fit(\n",
@@ -204,7 +208,7 @@
     "from autoencoder.data.anomaly import AnomalyDetector\n",
     "\n",
     "# get instance\n",
-    "mnist_recon_error = AnomalyDetector(autoencoder, test_ds, axis=(1, 2))\n",
+    "mnist_recon_error = AnomalyDetector(autoencoder, test_ds, axis=(1, 2, 3))\n",
     "\n",
     "# calculate recon error\n",
     "mnist_recon_error.calculate_error()\n",