feat: basic regression: predict fuel efficiency

notebooks/{regression.ipynb → Predict-fuel-efficiency.ipynb}

@@ -2471,11 +2471,97 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {
     "id": "c0mhscXh2k36"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "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\">Model: \"sequential_3\"</span>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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",
+       "│ normalization (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Normalization</span>)   │ (<span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)                │            <span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ ?                      │   <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
+       "│ normalization (\u001b[38;5;33mNormalization\u001b[0m)   │ (\u001b[38;5;34m10\u001b[0m, \u001b[38;5;34m9\u001b[0m)                │            \u001b[38;5;34m19\u001b[0m │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                 │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                 │ ?                      │   \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> (80.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19\u001b[0m (80.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "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\">19</span> (80.00 B)\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m19\u001b[0m (80.00 B)\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "dnn_model = build_and_compile_model(normalizer)\n",
     "dnn_model.summary()"
@@ -2483,11 +2569,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {
     "id": "CXDENACl2tuW"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 5.18 s, sys: 515 ms, total: 5.69 s\n",
+      "Wall time: 5.6 s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "history = dnn_model.fit(\n",
@@ -2499,11 +2594,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {
     "id": "-9Dbj0fX23RQ"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plot_loss(history)"
    ]
@@ -2519,7 +2625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {
     "id": "-bZIa96W3c7K"
    },
@@ -2549,11 +2655,69 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {
     "id": "e5_ooufM5iH2"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mean absolute error [MPG]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>horsepower_model</th>\n",
+       "      <td>3.649566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>linear_model</th>\n",
+       "      <td>2.476359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dnn_horsepower_model</th>\n",
+       "      <td>2.927792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dnn_model</th>\n",
+       "      <td>1.714351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Mean absolute error [MPG]\n",
+       "horsepower_model                       3.649566\n",
+       "linear_model                           2.476359\n",
+       "dnn_horsepower_model                   2.927792\n",
+       "dnn_model                              1.714351"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
    ]
@@ -2580,11 +2744,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 53,
    "metadata": {
     "id": "Xe7RXH3N3CWU"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:5 out of the last 11 calls to <function TensorFlowTrainer.make_predict_function.<locals>.one_step_on_data_distributed at 0x30c8fc860> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
+      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "test_predictions = dnn_model.predict(test_features).flatten()\n",
     "\n",
@@ -2611,11 +2794,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 54,
    "metadata": {
     "id": "f-OHX4DiXd8x"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "error = test_predictions - test_labels\n",
     "plt.hist(error, bins=25)\n",
@@ -2634,7 +2828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 55,
    "metadata": {
     "id": "4-WwLlmfT-mb"
    },
@@ -2654,7 +2848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 56,
    "metadata": {
     "id": "dyyyj2zVT-mf"
    },
@@ -2668,11 +2862,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 57,
    "metadata": {
     "id": "f_GchJ2tg-2o"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Mean absolute error [MPG]</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>horsepower_model</th>\n",
+       "      <td>3.649566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>linear_model</th>\n",
+       "      <td>2.476359</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dnn_horsepower_model</th>\n",
+       "      <td>2.927792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dnn_model</th>\n",
+       "      <td>1.714351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>reloaded</th>\n",
+       "      <td>1.714351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      Mean absolute error [MPG]\n",
+       "horsepower_model                       3.649566\n",
+       "linear_model                           2.476359\n",
+       "dnn_horsepower_model                   2.927792\n",
+       "dnn_model                              1.714351\n",
+       "reloaded                               1.714351"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
    ]