MNIST_TESTMODEL.ipynb

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        "<a href=\"https://colab.research.google.com/github/jojoneku/test_neural-network_model/blob/main/MNIST_TESTMODEL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "\u001b[1m11490434/11490434\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
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            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
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              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)                 │         \u001b[38;5;34m100,480\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)                  │           \u001b[38;5;34m1,290\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">784</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                 │         <span style=\"color: #00af00; text-decoration-color: #00af00\">100,480</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,290</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
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              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 KB)\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,770</span> (397.54 KB)\n",
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            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m101,770\u001b[0m (397.54 KB)\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">101,770</span> (397.54 KB)\n",
              "</pre>\n"
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          },
          "metadata": {}
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          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
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      ],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "# Load and preprocess the MNIST dataset\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "y_train, y_test = to_categorical(y_train), to_categorical(y_test)\n",
        "\n",
        "# Build the neural network\n",
        "model = Sequential([\n",
        "    Flatten(input_shape=(28, 28)),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Display the model summary\n",
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model with SGD optimizer\n",
        "history_sgd = model.fit(x_train, y_train, epochs=10, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Sxwi-cepG82",
        "outputId": "72a3928f-1994-44ad-b2af-3f719f83e3de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 3ms/step - accuracy: 0.7136 - loss: 1.0933 - val_accuracy: 0.8967 - val_loss: 0.3808\n",
            "Epoch 2/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.8942 - loss: 0.3828 - val_accuracy: 0.9117 - val_loss: 0.3134\n",
            "Epoch 3/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 3ms/step - accuracy: 0.9084 - loss: 0.3224 - val_accuracy: 0.9193 - val_loss: 0.2828\n",
            "Epoch 4/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step - accuracy: 0.9168 - loss: 0.2903 - val_accuracy: 0.9251 - val_loss: 0.2614\n",
            "Epoch 5/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9232 - loss: 0.2666 - val_accuracy: 0.9297 - val_loss: 0.2440\n",
            "Epoch 6/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - accuracy: 0.9286 - loss: 0.2469 - val_accuracy: 0.9358 - val_loss: 0.2291\n",
            "Epoch 7/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9338 - loss: 0.2300 - val_accuracy: 0.9398 - val_loss: 0.2160\n",
            "Epoch 8/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - accuracy: 0.9387 - loss: 0.2152 - val_accuracy: 0.9433 - val_loss: 0.2045\n",
            "Epoch 9/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - accuracy: 0.9425 - loss: 0.2019 - val_accuracy: 0.9457 - val_loss: 0.1945\n",
            "Epoch 10/10\n",
            "\u001b[1m1500/1500\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 4ms/step - accuracy: 0.9456 - loss: 0.1901 - val_accuracy: 0.9487 - val_loss: 0.1857\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from tensorflow.keras.datasets import mnist\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "\n",
        "# Load and preprocess the MNIST dataset\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "y_train, y_test = to_categorical(y_train), to_categorical(y_test)\n",
        "\n",
        "# Define a function to build and compile the model with different optimizers\n",
        "def build_and_train_model(optimizer):\n",
        "    model = Sequential([\n",
        "        Flatten(input_shape=(28, 28)),\n",
        "        Dense(128, activation='relu'),\n",
        "        Dense(10, activation='softmax')\n",
        "    ])\n",
        "\n",
        "    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "    history = model.fit(x_train, y_train, epochs=10, validation_split=0.2, verbose=0)\n",
        "    return history\n",
        "\n",
        "# Train models with different optimizers\n",
        "history_adam = build_and_train_model('adam')\n",
        "history_rmsprop = build_and_train_model('rmsprop')\n",
        "\n",
        "# Optionally, you can print the history to check training performance\n",
        "print(f\"Adam Optimizer: {history_adam.history['accuracy'][-1]:.4f}, Validation Accuracy: {history_adam.history['val_accuracy'][-1]:.4f}\")\n",
        "print(f\"RMSprop Optimizer: {history_rmsprop.history['accuracy'][-1]:.4f}, Validation Accuracy: {history_rmsprop.history['val_accuracy'][-1]:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QDP621qjypxq",
        "outputId": "d53b5791-c54a-41ff-89af-822f16418dfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adam Optimizer: 0.9966, Validation Accuracy: 0.9744\n",
            "RMSprop Optimizer: 0.9937, Validation Accuracy: 0.9723\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Function to plot training history\n",
        "def plot_history(histories, title):\n",
        "    plt.figure(figsize=(12, 6))\n",
        "    for name, history in histories.items():\n",
        "        plt.plot(history.history['val_accuracy'], label=name)\n",
        "    plt.title(title)\n",
        "    plt.xlabel('Epochs')\n",
        "    plt.ylabel('Validation Accuracy')\n",
        "    plt.legend()\n",
        "    plt.show()\n",
        "\n",
        "# Plot the validation accuracy for each optimizer\n",
        "histories = {'SGD': history_sgd, 'Adam': history_adam, 'RMSprop': history_rmsprop}\n",
        "plot_history(histories, 'Validation Accuracy Comparison')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "pLQj_6PF0HAo",
        "outputId": "ff2b7444-1f5b-448b-bda7-f29a050281c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}