Generate training and validation sets

main.ipynb

@@ -100,7 +100,34 @@
     "#featuresdf = pd.DataFrame(features, columns=['feature','class_label'])\n",
     "#np.save('features.npy', features)\n",
     "\n",
-    "print('Finished feature extraction from ', len(features), ' files')"
+    "print('Finished feature extraction from ', len(features), ' files')\n",
+    "\n",
+    "X = np.asarray(features)\n",
+    "y = np.asarray(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = (features)\n",
+    "y = (labels)\n",
+    "\n",
+    "# Encode the classification labels\n",
+    "le = LabelEncoder()\n",
+    "yy = to_categorical(le.fit_transform(y)) \n",
+    "\n",
+    "# split the dataset \n",
+    "from sklearn.model_selection import train_test_split \n",
+    "\n",
+    "x_train, x_test, y_train, y_test = train_test_split(X, yy, test_size=0.2, random_state = 42)\n",
+    "\n",
+    "#x_train = x_train.reshape(x_train.shape[0], x_train[0].shape[0], x_train[0].shape[1], x_train[0].shape[2])\n",
+    "#x_test = x_test.reshape(x_test.shape[0], x_test[0].shape[0], x_test[0].shape[1], x_test[0].shape[2])\n",
+    "\n",
+    "#print(x_train[0].shape)"
    ]
   }
  ],