Added convolutional neural network

main.ipynb

@@ -164,7 +164,35 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from keras.models import Sequential\n",
+    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
+    "from keras.layers import Convolution2D, Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.utils import np_utils\n",
+    "from sklearn import metrics \n",
+    "\n",
+    "\n",
+    "num_labels = yy.shape[1]\n",
+    "\n",
+    "# Construct model \n",
+    "model = Sequential()\n",
+    "model.add(Conv2D(filters=16, kernel_size=2, input_shape=(513, 341, 2), activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=2))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "model.add(Conv2D(filters=32, kernel_size=2, activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=2))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "model.add(Conv2D(filters=64, kernel_size=2, activation='relu'))\n",
+    "model.add(MaxPooling2D(pool_size=2))\n",
+    "model.add(Dropout(0.2))\n",
+    "\n",
+    "model.add(GlobalAveragePooling2D())\n",
+    "\n",
+    "model.add(Dense(num_labels, activation='softmax'))"
+   ]
   }
  ],
  "metadata": {