Wines_Red_White_Classification.ipynb

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      "name": "Wines_Red_White_Classification.ipynb",
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      "source": [
        "# Wines_Red_White_Classification (Binary Classifier)\n",
        "# --------------------------------------------------\n",
        "\n",
        "# Import pandas \n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from keras.models import model_from_json\n",
        "\n",
        "# Read in white wine data \n",
        "white = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv\", sep=';')\n",
        "\n",
        "# Read in red wine data \n",
        "red = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\", sep=';')\n",
        "\n",
        "np.random.seed(570)\n",
        "\n",
        "# Add `type` column to `red` with value 1\n",
        "red['type'] = 1\n",
        "\n",
        "# Add `type` column to `white` with value 0\n",
        "white['type'] = 0\n",
        "\n",
        "# Append `white` to `red`\n",
        "wines = red.append(white, ignore_index=True)\n",
        "\n",
        "# Import `train_test_split` from `sklearn.model_selection`\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Specify the data \n",
        "X=wines.iloc[:,0:11]\n",
        "\n",
        "# Specify the target labels and flatten the array \n",
        "y=np.ravel(wines.type)\n",
        "\n",
        "# Split the data up in train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
        "\n",
        "# Import `StandardScaler` from `sklearn.preprocessing`\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Define the scaler \n",
        "scaler = StandardScaler().fit(X_train)\n",
        "\n",
        "# Scale the train set\n",
        "X_train = scaler.transform(X_train)\n",
        "\n",
        "# Scale the test set\n",
        "X_test = scaler.transform(X_test)\n",
        "\n",
        "# Import `Sequential` from `keras.models`\n",
        "from keras.models import Sequential\n",
        "\n",
        "# Import `Dense` from `keras.layers`\n",
        "from keras.layers import Dense\n",
        "\n",
        "# load json and create model\n",
        "json_file = open('model.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "model.load_weights(\"model.h5\")\n",
        "print(\"Loaded model from disk\")\n",
        "\n",
        "# Initialize the constructor\n",
        "model = Sequential()\n",
        "\n",
        "# Add an input layer \n",
        "model.add(Dense(12, activation='relu', input_shape=(11,)))\n",
        "\n",
        "# Add one hidden layer \n",
        "model.add(Dense(8, activation='relu'))\n",
        "\n",
        "# Add an output layer \n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "# Model output shape\n",
        "model.output_shape\n",
        "\n",
        "# Model summary\n",
        "model.summary()\n",
        "\n",
        "# Model config\n",
        "model.get_config()\n",
        "\n",
        "# List all weight tensors \n",
        "model.get_weights()\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "                   \n",
        "model.fit(X_train, y_train,epochs=20, batch_size=1, verbose=1)\n",
        "\n",
        "score = model.evaluate(X_test, y_test,verbose=1)\n",
        "\n",
        "print(score)\n",
        "\n",
        "# serialize model to JSON\n",
        "#model_json = model.to_json()\n",
        "#with open(\"model.json\", \"w\") as json_file:\n",
        "#    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "#model.save_weights(\"model.h5\")\n",
        "#print(\"Saved model to disk\")\n",
        "\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "print(y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    }
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