0x12-Building a Regression MLP Using the Sequential API.ipynb

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   "source": [
    "# Building a Regression MLP Using the Sequential API."
   ]
  },
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   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
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   "source": [
    "## Loading the Data"
   ]
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   "source": [
    "housing = fetch_california_housing()\n",
    "\n",
    "X_train_full, X_test, y_train_full, y_test = train_test_split(\n",
    "    housing.data, housing.target)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_train_full, y_train_full)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_valid = scaler.transform(X_valid)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "af725a38-979b-4ad0-aaae-d33972548b0c",
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      "C:\\Users\\arnol\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: 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__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
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     "text": [
      "Epoch 1/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 1.2699 - val_loss: 0.6057\n",
      "Epoch 2/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.5365 - val_loss: 0.5247\n",
      "Epoch 3/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4715 - val_loss: 0.4330\n",
      "Epoch 4/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4362 - val_loss: 0.4156\n",
      "Epoch 5/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4114 - val_loss: 0.4028\n",
      "Epoch 6/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - loss: 0.3762 - val_loss: 0.4008\n",
      "Epoch 7/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3843 - val_loss: 0.4010\n",
      "Epoch 8/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3874 - val_loss: 0.3961\n",
      "Epoch 9/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3936 - val_loss: 0.3865\n",
      "Epoch 10/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3982 - val_loss: 0.3866\n",
      "Epoch 11/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3762 - val_loss: 0.3830\n",
      "Epoch 12/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3736 - val_loss: 0.3825\n",
      "Epoch 13/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3615 - val_loss: 0.3877\n",
      "Epoch 14/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.3618 - val_loss: 0.3863\n",
      "Epoch 15/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3699 - val_loss: 0.3721\n",
      "Epoch 16/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step - loss: 0.3586 - val_loss: 0.3827\n",
      "Epoch 17/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - loss: 0.3744 - val_loss: 0.3843\n",
      "Epoch 18/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3565 - val_loss: 0.3747\n",
      "Epoch 19/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3547 - val_loss: 0.3918\n",
      "Epoch 20/20\n",
      "\u001b[1m363/363\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step - loss: 0.3490 - val_loss: 0.3695\n",
      "\u001b[1m162/162\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.3727\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 198ms/step\n"
     ]
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     "data": {
      "text/plain": [
       "array([[1.7499189],\n",
       "       [1.9974766],\n",
       "       [3.9450617]], dtype=float32)"
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     "execution_count": 6,
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   "source": [
    "# The difference with the classification MLP is that the output layer has only one neuron, uses no activation function and the loss function,\n",
    "# is the mean squared error.\n",
    "\n",
    "# Since the data is noisy we'll use a single hidden layer with a fewer neurons than before to avoid overfitting.\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Dense(30, activation=\"relu\", input_shape=X_train.shape[1:]),\n",
    "    keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mean_squared_error\", optimizer=\"sgd\")\n",
    "history = model.fit(X_train, y_train, epochs=20, validation_data=(X_valid, y_valid))\n",
    "mse_test = model.evaluate(X_test, y_test)\n",
    "X_new = X_test[:3] # pretend these are new instances.\n",
    "y_pred = model.predict(X_new)\n",
    "y_pred"
   ]
  },
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   "id": "be8e48ca-4644-442b-8dc3-d292746fa3b9",
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   "source": []
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