MPL_MultiClass_Classifiction(Simple).ipynb

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          "text": [
            "<ipython-input-1-d9ad25514f52>:8: FutureWarning: In a future version of pandas all arguments of read_csv except for the argument 'filepath_or_buffer' will be keyword-only.\n",
            "  df = pd.read_csv('/content/iris.csv', ',')\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(99, 4) (50, 4) (99,) (50,)\n",
            "Test Accuracy: 0.800\n",
            "1/1 [==============================] - 0s 123ms/step\n",
            "Predicted: [[9.9964619e-01 3.5133286e-04 2.4839660e-06]] (class=0)\n"
          ]
        }
      ],
      "source": [
        "# mlp for multiclass classification\n",
        "from numpy import argmax\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "df = pd.read_csv('/content/iris.csv', ',')\n",
        "X, y = df.values[:, :-1], df.values[:, -1]\n",
        "X = X.astype('float32')\n",
        "y = LabelEncoder().fit_transform(y)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
        "n_features = X_train.shape[1]\n",
        "# Define model\n",
        "model = Sequential()\n",
        "model.add(Dense(10, activation='relu', kernel_initializer='he_normal', input_shape=(n_features,)))\n",
        "model.add(Dense(8, activation='relu', kernel_initializer='he_normal'))\n",
        "model.add(Dense(3, activation='softmax'))\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "# Fit the model\n",
        "model.fit(X_train, y_train, epochs=150, batch_size=32, verbose=0)\n",
        "# Evaluate the model\n",
        "loss, acc = model.evaluate(X_test, y_test, verbose=0)\n",
        "print('Test Accuracy: %.3f' % acc)\n",
        "# Make a prediction\n",
        "row = [5.1,3.5,1.4,0.2]\n",
        "yhat = model.predict([row])\n",
        "print('Predicted: %s (class=%d)' % (yhat, argmax(yhat)))"
      ]
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