tensorflow2/008-keras-custom-callback/keras-custom-callback.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras Custom callback \n",
    "\n",
    "This is a notebook for the medium article [Building Custom Callbacks with Keras and TensorFlow 2](https://bindichen.medium.com/building-custom-callbacks-with-keras-and-tensorflow-2-85e1b79915a3)\n",
    "\n",
    "Please check out article for instructions\n",
    "\n",
    "**License**: [BSD 2-Clause](https://opensource.org/licenses/BSD-2-Clause)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['KMP_DUPLICATE_LIB_OK']='True'\n",
    "\n",
    "from tensorflow import keras\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper function to plot metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "def plot_metric(history, metric):\n",
    "    train_metrics = history.history[metric]\n",
    "    val_metrics = history.history['val_'+metric]\n",
    "    epochs = range(1, len(train_metrics) + 1)\n",
    "    plt.plot(epochs, train_metrics)\n",
    "    plt.plot(epochs, val_metrics)\n",
    "    plt.title('Training and validation '+ metric)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([\"train_\"+metric, 'val_'+metric])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lr(history):\n",
    "    learning_rate = history.history['lr']\n",
    "    epochs = range(1, len(learning_rate) + 1)\n",
    "    plt.plot(epochs, learning_rate)\n",
    "    plt.title('Learning rate')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Learning rate')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fashion MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For faster training, let's use a subset 10,000\n",
    "X_train, y_train = X_train_full[:10000] / 255.0, y_train_full[:10000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a NN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Flatten\n",
    "\n",
    "def create_model(): \n",
    "    model = Sequential([\n",
    "        Flatten(input_shape=(28, 28)),\n",
    "        Dense(300, activation='relu'),\n",
    "        Dense(100, activation='relu'),\n",
    "        Dense(10, activation='softmax'),\n",
    "    ])\n",
    "    model.compile(\n",
    "        optimizer='sgd', \n",
    "        loss='sparse_categorical_crossentropy', \n",
    "        metrics=['accuracy']\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 300)               235500    \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               30100     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                1010      \n",
      "=================================================================\n",
      "Total params: 266,610\n",
      "Trainable params: 266,610\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = create_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Building a custom callback for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import Callback\n",
    "\n",
    "class TrainingCallback(Callback):\n",
    "    \n",
    "    def on_train_begin(self, logs=None):\n",
    "        print(\"Starting training...\")\n",
    "        \n",
    "    def on_epoch_begin(self, epoch, logs=None):\n",
    "        print(f\"Starting epoch {epoch}\")\n",
    "\n",
    "    def on_train_batch_begin(self, batch, logs=None):\n",
    "        print(f\"Training: Starting batch {batch}\")\n",
    "        \n",
    "    def on_train_batch_end(self, batch, logs=None):\n",
    "        print(f\"Training: Finished batch {batch}, loss is {logs['loss']}\")\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        print(f\"Finished epoch {epoch}, loss is {logs['loss']}, accuracy is {logs['accuracy']}\")\n",
    "        \n",
    "    def on_train_end(self, logs=None):\n",
    "        print(\"Finished training\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Starting training...\n",
      "Starting epoch 0\n",
      "Epoch 1/2\n",
      "Training: Starting batch 0\n",
      "Training: Finished batch 0, loss is 2.4427168369293213\n",
      "Training: Starting batch 1\n",
      "Training: Finished batch 1, loss is 2.360092878341675\n",
      "Finished epoch 0, loss is 2.401404857635498, accuracy is 0.1262499988079071\n",
      "8000/8000 - 1s - loss: 2.4014 - accuracy: 0.1262 - val_loss: 2.3497 - val_accuracy: 0.1160\n",
      "Starting epoch 1\n",
      "Epoch 2/2\n",
      "Training: Starting batch 0\n",
      "Training: Finished batch 0, loss is 2.3291571140289307\n",
      "Training: Starting batch 1\n",
      "Training: Finished batch 1, loss is 2.291919469833374\n",
      "Finished epoch 1, loss is 2.3105382919311523, accuracy is 0.1368750035762787\n",
      "8000/8000 - 0s - loss: 2.3105 - accuracy: 0.1369 - val_loss: 2.2786 - val_accuracy: 0.1195\n",
      "Finished training\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    epochs=2, \n",
    "    validation_split=0.20, \n",
    "    batch_size=4000, \n",
    "    verbose=2,\n",
    "    callbacks=[TrainingCallback()]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Building a custom callback for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TestingCallback(Callback):\n",
    "    \n",
    "    def on_test_begin(self, logs=None):\n",
    "        print(\"Starting testing ...\")\n",
    "        \n",
    "    def on_test_batch_begin(self, batch, logs=None):\n",
    "        print(f\"Testing: Starting batch {batch}\")\n",
    "    \n",
    "    def on_test_batch_end(self, batch, logs=None):\n",
    "        print(f\"Testing: Finished batch {batch}\")\n",
    "        \n",
    "    def on_test_end(self, logs=None):\n",
    "        print(\"Finished testing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting testing ...\n",
      "Testing: Starting batch 0\n",
      "Testing: Finished batch 0\n",
      "Testing: Starting batch 1\n",
      "Testing: Finished batch 1\n",
      "Testing: Starting batch 2\n",
      "Testing: Finished batch 2\n",
      "Testing: Starting batch 3\n",
      "Testing: Finished batch 3\n",
      "Testing: Starting batch 4\n",
      "Testing: Finished batch 4\n",
      "Finished testing\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[121.49524536132813, 0.1332]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test, verbose=False, callbacks=[TestingCallback()], batch_size=2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Building a custom callback for prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PredictionCallback(Callback):\n",
    "    \n",
    "    def on_predict_begin(self, logs=None):\n",
    "        print(\"Starting prediction ...\")\n",
    "    \n",
    "    def on_predict_batch_begin(self, batch, logs=None):\n",
    "        print(f\"Prediction: Starting batch {batch}\")\n",
    "        \n",
    "    def on_predict_batch_end(self, batch, logs=None):\n",
    "        print(f\"Prediction: Finish batch {batch}\")\n",
    "    \n",
    "    def on_predict_end(self, logs=None):\n",
    "        print(\"Finished prediction\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting prediction ...\n",
      "Prediction: Starting batch 0\n",
      "Prediction: Finish batch 0\n",
      "Prediction: Starting batch 1\n",
      "Prediction: Finish batch 1\n",
      "Prediction: Starting batch 2\n",
      "Prediction: Finish batch 2\n",
      "Prediction: Starting batch 3\n",
      "Prediction: Finish batch 3\n",
      "Prediction: Starting batch 4\n",
      "Prediction: Finish batch 4\n",
      "Finished prediction\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.0000000e+00, 1.7347565e-15, 0.0000000e+00, ..., 3.5985607e-01,\n",
       "        3.0031477e-30, 0.0000000e+00],\n",
       "       [0.0000000e+00, 3.6086992e-32, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, ..., 8.8086944e-38,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       ...,\n",
       "       [0.0000000e+00, 1.4598069e-01, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, ..., 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00],\n",
       "       [9.8251455e-33, 2.9541528e-19, 0.0000000e+00, ..., 7.0404289e-20,\n",
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test, verbose=False, callbacks=[PredictionCallback()], batch_size=2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Examples of Keras callback applications\n",
    "Reference: https://keras.io/guides/writing_your_own_callbacks/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Real-time plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "z\n",
       "\" id=\"DejaVuSans-115\"/>\n",
       "     </defs>\n",
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       "      <use xlink:href=\"#DejaVuSans-108\"/>\n",
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       "     </g>\n",
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       " </g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "fig = plt.figure(figsize=(12,4))\n",
    "# Create plots inside the figures\n",
    "ax = fig.add_subplot()\n",
    "ax.set_xlabel('Epoch #')\n",
    "ax.set_ylabel('loss')\n",
    "\n",
    "class TrainingPlot(tf.keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        # Initialize the lists for holding losses\n",
    "        self.losses = []\n",
    "    \n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        # Append the losses to the lists\n",
    "        self.losses.append(logs['loss'])\n",
    "        \n",
    "        # Plot\n",
    "        epochs = np.arange(0, len(self.losses))\n",
    "        ax.plot(epochs, self.losses, \"b-\")\n",
    "        fig.canvas.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/20\n",
      "8000/8000 - 1s - loss: 1.5609 - accuracy: 0.5562 - val_loss: 1.1415 - val_accuracy: 0.6455\n",
      "Epoch 2/20\n",
      "8000/8000 - 1s - loss: 0.9738 - accuracy: 0.6841 - val_loss: 0.8817 - val_accuracy: 0.7055\n",
      "Epoch 3/20\n",
      "8000/8000 - 1s - loss: 0.8106 - accuracy: 0.7329 - val_loss: 0.7825 - val_accuracy: 0.7450\n",
      "Epoch 4/20\n",
      "8000/8000 - 1s - loss: 0.7269 - accuracy: 0.7590 - val_loss: 0.7181 - val_accuracy: 0.7645\n",
      "Epoch 5/20\n",
      "8000/8000 - 1s - loss: 0.6719 - accuracy: 0.7744 - val_loss: 0.6715 - val_accuracy: 0.7760\n",
      "Epoch 6/20\n",
      "8000/8000 - 1s - loss: 0.6320 - accuracy: 0.7896 - val_loss: 0.6364 - val_accuracy: 0.7875\n",
      "Epoch 7/20\n",
      "8000/8000 - 1s - loss: 0.6000 - accuracy: 0.8005 - val_loss: 0.6131 - val_accuracy: 0.7885\n",
      "Epoch 8/20\n",
      "8000/8000 - 1s - loss: 0.5745 - accuracy: 0.8069 - val_loss: 0.5862 - val_accuracy: 0.8030\n",
      "Epoch 9/20\n",
      "8000/8000 - 1s - loss: 0.5533 - accuracy: 0.8148 - val_loss: 0.5668 - val_accuracy: 0.8095\n",
      "Epoch 10/20\n",
      "8000/8000 - 1s - loss: 0.5360 - accuracy: 0.8179 - val_loss: 0.5583 - val_accuracy: 0.8140\n",
      "Epoch 11/20\n",
      "8000/8000 - 1s - loss: 0.5209 - accuracy: 0.8259 - val_loss: 0.5413 - val_accuracy: 0.8145\n",
      "Epoch 12/20\n",
      "8000/8000 - 1s - loss: 0.5090 - accuracy: 0.8286 - val_loss: 0.5356 - val_accuracy: 0.8245\n",
      "Epoch 13/20\n",
      "8000/8000 - 1s - loss: 0.4955 - accuracy: 0.8322 - val_loss: 0.5305 - val_accuracy: 0.8250\n",
      "Epoch 14/20\n",
      "8000/8000 - 1s - loss: 0.4871 - accuracy: 0.8314 - val_loss: 0.5298 - val_accuracy: 0.8135\n",
      "Epoch 15/20\n",
      "8000/8000 - 1s - loss: 0.4775 - accuracy: 0.8355 - val_loss: 0.5097 - val_accuracy: 0.8265\n",
      "Epoch 16/20\n",
      "8000/8000 - 1s - loss: 0.4698 - accuracy: 0.8418 - val_loss: 0.5118 - val_accuracy: 0.8180\n",
      "Epoch 17/20\n",
      "8000/8000 - 1s - loss: 0.4606 - accuracy: 0.8407 - val_loss: 0.5089 - val_accuracy: 0.8150\n",
      "Epoch 18/20\n",
      "8000/8000 - 1s - loss: 0.4545 - accuracy: 0.8465 - val_loss: 0.4959 - val_accuracy: 0.8285\n",
      "Epoch 19/20\n",
      "8000/8000 - 1s - loss: 0.4467 - accuracy: 0.8469 - val_loss: 0.5025 - val_accuracy: 0.8215\n",
      "Epoch 20/20\n",
      "8000/8000 - 1s - loss: 0.4423 - accuracy: 0.8491 - val_loss: 0.4913 - val_accuracy: 0.8260\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    epochs=20, \n",
    "    validation_split=0.20, \n",
    "    batch_size=64, \n",
    "    verbose=2,\n",
    "    callbacks=[TrainingPlot()]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Early stopping at minimum loss\n",
    "\n",
    "**Note:** this example is originally from Keras guide [\"Writing your own callbacks\"](https://keras.io/guides/writing_your_own_callbacks/#examples-of-keras-callback-applications), please check out the official documentation for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "class EarlyStoppingAtMinLoss(keras.callbacks.Callback):\n",
    "    \"\"\"Stop training when the loss is at its min, i.e. the loss stops decreasing.\n",
    "\n",
    "  Arguments:\n",
    "      patience: Number of epochs to wait after min has been hit. After this\n",
    "      number of no improvement, training stops.\n",
    "  \"\"\"\n",
    "\n",
    "    def __init__(self, patience=0):\n",
    "        super(EarlyStoppingAtMinLoss, self).__init__()\n",
    "        self.patience = patience\n",
    "        # best_weights to store the weights at which the minimum loss occurs.\n",
    "        self.best_weights = None\n",
    "\n",
    "    def on_train_begin(self, logs=None):\n",
    "        # The number of epoch it has waited when loss is no longer minimum.\n",
    "        self.wait = 0\n",
    "        # The epoch the training stops at.\n",
    "        self.stopped_epoch = 0\n",
    "        # Initialize the best as infinity.\n",
    "        self.best = np.Inf\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        current = logs.get(\"loss\")\n",
    "        if np.less(current, self.best):\n",
    "            self.best = current\n",
    "            self.wait = 0\n",
    "            # Record the best weights if current results is better (less).\n",
    "            self.best_weights = self.model.get_weights()\n",
    "        else:\n",
    "            self.wait += 1\n",
    "            if self.wait >= self.patience:\n",
    "                self.stopped_epoch = epoch\n",
    "                self.model.stop_training = True\n",
    "                print(\"Restoring model weights from the end of the best epoch.\")\n",
    "                self.model.set_weights(self.best_weights)\n",
    "\n",
    "    def on_train_end(self, logs=None):\n",
    "        if self.stopped_epoch > 0:\n",
    "            print(\"Epoch %05d: early stopping\" % (self.stopped_epoch + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/50\n",
      "8000/8000 - 1s - loss: 1.6033 - accuracy: 0.5206 - val_loss: 1.1432 - val_accuracy: 0.6560\n",
      "Epoch 2/50\n",
      "8000/8000 - 1s - loss: 0.9625 - accuracy: 0.6950 - val_loss: 0.8708 - val_accuracy: 0.7210\n",
      "Epoch 3/50\n",
      "8000/8000 - 1s - loss: 0.7882 - accuracy: 0.7384 - val_loss: 0.7783 - val_accuracy: 0.7370\n",
      "Epoch 4/50\n",
      "8000/8000 - 1s - loss: 0.7078 - accuracy: 0.7655 - val_loss: 0.7068 - val_accuracy: 0.7580\n",
      "Epoch 5/50\n",
      "8000/8000 - 1s - loss: 0.6555 - accuracy: 0.7801 - val_loss: 0.6661 - val_accuracy: 0.7845\n",
      "Epoch 6/50\n",
      "8000/8000 - 1s - loss: 0.6186 - accuracy: 0.7910 - val_loss: 0.6376 - val_accuracy: 0.7810\n",
      "Epoch 7/50\n",
      "8000/8000 - 1s - loss: 0.5909 - accuracy: 0.8018 - val_loss: 0.6124 - val_accuracy: 0.7910\n",
      "Epoch 8/50\n",
      "8000/8000 - 1s - loss: 0.5698 - accuracy: 0.8067 - val_loss: 0.5950 - val_accuracy: 0.7960\n",
      "Epoch 9/50\n",
      "8000/8000 - 1s - loss: 0.5472 - accuracy: 0.8152 - val_loss: 0.5729 - val_accuracy: 0.8045\n",
      "Epoch 10/50\n",
      "8000/8000 - 1s - loss: 0.5318 - accuracy: 0.8156 - val_loss: 0.5828 - val_accuracy: 0.7910\n",
      "Epoch 11/50\n",
      "8000/8000 - 1s - loss: 0.5170 - accuracy: 0.8229 - val_loss: 0.5822 - val_accuracy: 0.7995\n",
      "Epoch 12/50\n",
      "8000/8000 - 1s - loss: 0.5047 - accuracy: 0.8275 - val_loss: 0.5497 - val_accuracy: 0.8085\n",
      "Epoch 13/50\n",
      "8000/8000 - 1s - loss: 0.4960 - accuracy: 0.8300 - val_loss: 0.5289 - val_accuracy: 0.8210\n",
      "Epoch 14/50\n",
      "8000/8000 - 1s - loss: 0.4863 - accuracy: 0.8313 - val_loss: 0.5227 - val_accuracy: 0.8195\n",
      "Epoch 15/50\n",
      "8000/8000 - 1s - loss: 0.4780 - accuracy: 0.8356 - val_loss: 0.5366 - val_accuracy: 0.8160\n",
      "Epoch 16/50\n",
      "8000/8000 - 1s - loss: 0.4681 - accuracy: 0.8378 - val_loss: 0.5086 - val_accuracy: 0.8270\n",
      "Epoch 17/50\n",
      "8000/8000 - 1s - loss: 0.4627 - accuracy: 0.8350 - val_loss: 0.5044 - val_accuracy: 0.8245\n",
      "Epoch 18/50\n",
      "8000/8000 - 1s - loss: 0.4540 - accuracy: 0.8443 - val_loss: 0.5056 - val_accuracy: 0.8255\n",
      "Epoch 19/50\n",
      "8000/8000 - 1s - loss: 0.4471 - accuracy: 0.8476 - val_loss: 0.4957 - val_accuracy: 0.8340\n",
      "Epoch 20/50\n",
      "8000/8000 - 1s - loss: 0.4419 - accuracy: 0.8494 - val_loss: 0.4906 - val_accuracy: 0.8355\n",
      "Epoch 21/50\n",
      "8000/8000 - 1s - loss: 0.4341 - accuracy: 0.8550 - val_loss: 0.4886 - val_accuracy: 0.8335\n",
      "Epoch 22/50\n",
      "8000/8000 - 1s - loss: 0.4310 - accuracy: 0.8528 - val_loss: 0.4841 - val_accuracy: 0.8340\n",
      "Epoch 23/50\n",
      "8000/8000 - 1s - loss: 0.4262 - accuracy: 0.8540 - val_loss: 0.4857 - val_accuracy: 0.8315\n",
      "Epoch 24/50\n",
      "8000/8000 - 1s - loss: 0.4207 - accuracy: 0.8529 - val_loss: 0.4834 - val_accuracy: 0.8315\n",
      "Epoch 25/50\n",
      "8000/8000 - 1s - loss: 0.4137 - accuracy: 0.8550 - val_loss: 0.5292 - val_accuracy: 0.8080\n",
      "Epoch 26/50\n",
      "8000/8000 - 1s - loss: 0.4121 - accuracy: 0.8574 - val_loss: 0.4748 - val_accuracy: 0.8430\n",
      "Epoch 27/50\n",
      "8000/8000 - 1s - loss: 0.4052 - accuracy: 0.8609 - val_loss: 0.4619 - val_accuracy: 0.8420\n",
      "Epoch 28/50\n",
      "8000/8000 - 1s - loss: 0.4015 - accuracy: 0.8615 - val_loss: 0.4650 - val_accuracy: 0.8425\n",
      "Epoch 29/50\n",
      "8000/8000 - 1s - loss: 0.3984 - accuracy: 0.8602 - val_loss: 0.4595 - val_accuracy: 0.8395\n",
      "Epoch 30/50\n",
      "8000/8000 - 1s - loss: 0.3929 - accuracy: 0.8614 - val_loss: 0.4622 - val_accuracy: 0.8410\n",
      "Epoch 31/50\n",
      "8000/8000 - 1s - loss: 0.3890 - accuracy: 0.8698 - val_loss: 0.4598 - val_accuracy: 0.8390\n",
      "Epoch 32/50\n",
      "8000/8000 - 1s - loss: 0.3862 - accuracy: 0.8633 - val_loss: 0.4587 - val_accuracy: 0.8415\n",
      "Epoch 33/50\n",
      "8000/8000 - 1s - loss: 0.3808 - accuracy: 0.8696 - val_loss: 0.4567 - val_accuracy: 0.8425\n",
      "Epoch 34/50\n",
      "8000/8000 - 1s - loss: 0.3783 - accuracy: 0.8689 - val_loss: 0.4670 - val_accuracy: 0.8370\n",
      "Epoch 35/50\n",
      "8000/8000 - 1s - loss: 0.3762 - accuracy: 0.8689 - val_loss: 0.4466 - val_accuracy: 0.8440\n",
      "Epoch 36/50\n",
      "8000/8000 - 1s - loss: 0.3706 - accuracy: 0.8735 - val_loss: 0.4506 - val_accuracy: 0.8450\n",
      "Epoch 37/50\n",
      "8000/8000 - 1s - loss: 0.3692 - accuracy: 0.8760 - val_loss: 0.4503 - val_accuracy: 0.8440\n",
      "Epoch 38/50\n",
      "8000/8000 - 1s - loss: 0.3650 - accuracy: 0.8748 - val_loss: 0.4493 - val_accuracy: 0.8425\n",
      "Epoch 39/50\n",
      "8000/8000 - 1s - loss: 0.3624 - accuracy: 0.8735 - val_loss: 0.4541 - val_accuracy: 0.8400\n",
      "Epoch 40/50\n",
      "8000/8000 - 1s - loss: 0.3617 - accuracy: 0.8734 - val_loss: 0.4483 - val_accuracy: 0.8435\n",
      "Epoch 41/50\n",
      "8000/8000 - 1s - loss: 0.3562 - accuracy: 0.8785 - val_loss: 0.4600 - val_accuracy: 0.8410\n",
      "Epoch 42/50\n",
      "Restoring model weights from the end of the best epoch.\n",
      "8000/8000 - 1s - loss: 0.3576 - accuracy: 0.8754 - val_loss: 0.4453 - val_accuracy: 0.8420\n",
      "Epoch 00042: early stopping\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    epochs=50, \n",
    "    validation_split=0.20, \n",
    "    batch_size=64, \n",
    "    verbose=2,\n",
    "    callbacks=[EarlyStoppingAtMinLoss()]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Learning rate scheduler\n",
    "\n",
    "**Note:** this example is originally from Keras guide [\"Writing your own callbacks\"](https://keras.io/guides/writing_your_own_callbacks/#examples-of-keras-callback-applications), please check out the official documentation for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomLearningRateScheduler(Callback):\n",
    "    \"\"\"Learning rate scheduler which sets the learning rate according to schedule.\n",
    "\n",
    "  Arguments:\n",
    "      schedule: a function that takes an epoch index\n",
    "          (integer, indexed from 0) and current learning rate\n",
    "          as inputs and returns a new learning rate as output (float).\n",
    "  \"\"\"\n",
    "\n",
    "    def __init__(self, schedule):\n",
    "        super(CustomLearningRateScheduler, self).__init__()\n",
    "        self.schedule = schedule\n",
    "\n",
    "    def on_epoch_begin(self, epoch, logs=None):\n",
    "        if not hasattr(self.model.optimizer, \"lr\"):\n",
    "            raise ValueError('Optimizer must have a \"lr\" attribute.')\n",
    "        # Get the current learning rate from model's optimizer.\n",
    "        lr = float(tf.keras.backend.get_value(self.model.optimizer.learning_rate))\n",
    "        # Call schedule function to get the scheduled learning rate.\n",
    "        scheduled_lr = self.schedule(epoch, lr)\n",
    "        # Set the value back to the optimizer before this epoch starts\n",
    "        tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_lr)\n",
    "        print(\"\\nEpoch %05d: Learning rate is %6.4f.\" % (epoch, scheduled_lr))\n",
    "\n",
    "\n",
    "LR_SCHEDULE = [\n",
    "    # (epoch to start, learning rate) tuples\n",
    "    (3, 0.05),\n",
    "    (6, 0.01),\n",
    "    (9, 0.005),\n",
    "    (12, 0.001),\n",
    "]\n",
    "\n",
    "\n",
    "def lr_schedule(epoch, lr):\n",
    "    \"\"\"Helper function to retrieve the scheduled learning rate based on epoch.\"\"\"\n",
    "    if epoch < LR_SCHEDULE[0][0] or epoch > LR_SCHEDULE[-1][0]:\n",
    "        return lr\n",
    "    for i in range(len(LR_SCHEDULE)):\n",
    "        if epoch == LR_SCHEDULE[i][0]:\n",
    "            return LR_SCHEDULE[i][1]\n",
    "    return lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "\n",
      "Epoch 00000: Learning rate is 0.0100.\n",
      "Epoch 1/15\n",
      "8000/8000 - 2s - loss: 1.5985 - accuracy: 0.5309 - val_loss: 1.1865 - val_accuracy: 0.6410\n",
      "\n",
      "Epoch 00001: Learning rate is 0.0100.\n",
      "Epoch 2/15\n",
      "8000/8000 - 1s - loss: 0.9859 - accuracy: 0.6966 - val_loss: 0.8957 - val_accuracy: 0.7055\n",
      "\n",
      "Epoch 00002: Learning rate is 0.0100.\n",
      "Epoch 3/15\n",
      "8000/8000 - 1s - loss: 0.8006 - accuracy: 0.7490 - val_loss: 0.7839 - val_accuracy: 0.7435\n",
      "\n",
      "Epoch 00003: Learning rate is 0.0500.\n",
      "Epoch 4/15\n",
      "8000/8000 - 1s - loss: 0.7236 - accuracy: 0.7545 - val_loss: 0.6243 - val_accuracy: 0.7855\n",
      "\n",
      "Epoch 00004: Learning rate is 0.0500.\n",
      "Epoch 5/15\n",
      "8000/8000 - 1s - loss: 0.5873 - accuracy: 0.7924 - val_loss: 0.7281 - val_accuracy: 0.7435\n",
      "\n",
      "Epoch 00005: Learning rate is 0.0500.\n",
      "Epoch 6/15\n",
      "8000/8000 - 1s - loss: 0.5293 - accuracy: 0.8164 - val_loss: 0.5451 - val_accuracy: 0.8090\n",
      "\n",
      "Epoch 00006: Learning rate is 0.0100.\n",
      "Epoch 7/15\n",
      "8000/8000 - 1s - loss: 0.4573 - accuracy: 0.8444 - val_loss: 0.4993 - val_accuracy: 0.8225\n",
      "\n",
      "Epoch 00007: Learning rate is 0.0100.\n",
      "Epoch 8/15\n",
      "8000/8000 - 1s - loss: 0.4477 - accuracy: 0.8475 - val_loss: 0.4924 - val_accuracy: 0.8265\n",
      "\n",
      "Epoch 00008: Learning rate is 0.0100.\n",
      "Epoch 9/15\n",
      "8000/8000 - 1s - loss: 0.4402 - accuracy: 0.8512 - val_loss: 0.4905 - val_accuracy: 0.8300\n",
      "\n",
      "Epoch 00009: Learning rate is 0.0050.\n",
      "Epoch 10/15\n",
      "8000/8000 - 1s - loss: 0.4317 - accuracy: 0.8521 - val_loss: 0.4879 - val_accuracy: 0.8260\n",
      "\n",
      "Epoch 00010: Learning rate is 0.0050.\n",
      "Epoch 11/15\n",
      "8000/8000 - 1s - loss: 0.4282 - accuracy: 0.8559 - val_loss: 0.4836 - val_accuracy: 0.8315\n",
      "\n",
      "Epoch 00011: Learning rate is 0.0050.\n",
      "Epoch 12/15\n",
      "8000/8000 - 1s - loss: 0.4259 - accuracy: 0.8580 - val_loss: 0.4769 - val_accuracy: 0.8390\n",
      "\n",
      "Epoch 00012: Learning rate is 0.0010.\n",
      "Epoch 13/15\n",
      "8000/8000 - 1s - loss: 0.4206 - accuracy: 0.8594 - val_loss: 0.4760 - val_accuracy: 0.8405\n",
      "\n",
      "Epoch 00013: Learning rate is 0.0010.\n",
      "Epoch 14/15\n",
      "8000/8000 - 1s - loss: 0.4200 - accuracy: 0.8584 - val_loss: 0.4766 - val_accuracy: 0.8380\n",
      "\n",
      "Epoch 00014: Learning rate is 0.0010.\n",
      "Epoch 15/15\n",
      "8000/8000 - 1s - loss: 0.4193 - accuracy: 0.8589 - val_loss: 0.4760 - val_accuracy: 0.8370\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    epochs=15, \n",
    "    validation_split=0.20, \n",
    "    batch_size=64, \n",
    "    verbose=2,\n",
    "    callbacks=[CustomLearningRateScheduler(lr_schedule)]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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