Build network

Author: Sayed-Hossein-Hosseini

Audio_Classification_Using_CNNs.ipynb

@@ -378,27 +378,39 @@
       "cell_type": "code",
       "source": [
         "from keras.models import Sequential\n",
-        "from keras.layers import Conv2D, MaxPooling2D\n",
-        "from keras.layers import Flatten, Dense\n",
-        "'''\n",
-        "Hint:\n",
-        "  https://keras.io/api/models/sequential/\n",
-        "  https://keras.io/api/layers/convolution_layers/convolution2d/\n",
-        "  https://keras.io/api/layers/pooling_layers/max_pooling2d/\n",
-        "  https://keras.io/api/layers/reshaping_layers/flatten/\n",
-        "  https://keras.io/api/layers/core_layers/dense/\n",
+        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
+        "from keras.optimizers import Adam\n",
         "\n",
-        "finally compile the model with Adam optimizer and CE loss function\n",
-        "please consider to define the input_shape for first Conv layer which has a same rule as Input layer\n",
-        "'''\n",
+        "model = Sequential()\n",
         "\n",
+        "# Layer 1: Conv2D + MaxPool\n",
+        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))\n",
+        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
         "\n",
-        "##############################################\n",
-        "############# YOUR CODES GO HERE #############\n",
-        "model = ...\n",
+        "# Layer 2: Conv2D + MaxPool\n",
+        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
+        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
         "\n",
-        "model.summary()\n",
-        "##############################################"
+        "# Layer 3: Conv2D + MaxPool\n",
+        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
+        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+        "\n",
+        "# Layer 4: Conv2D + MaxPool\n",
+        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
+        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
+        "\n",
+        "# Flatten\n",
+        "model.add(Flatten())\n",
+        "\n",
+        "# Fully Connected Dense Layers\n",
+        "model.add(Dense(1024, activation='relu'))\n",
+        "model.add(Dense(4, activation='softmax'))  # 4 classes\n",
+        "\n",
+        "# Compile the model\n",
+        "model.compile(optimizer=Adam(), loss='categorical_crossentropy', metrics=['accuracy'])\n",
+        "\n",
+        "# Print model summary\n",
+        "model.summary()"
       ],
       "metadata": {
         "id": "dYmB0f5YR4hY"