tensorflow2/007-keras-callback/keras-callbacks.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A practical introduction to Keras Callbacks\n",
    "\n",
    "This is a notebook for the medium article [A practical introduction to Keras Callbacks](https://medium.com/@bindiatwork/a-practical-introduction-to-keras-callbacks-in-tensorflow-2-705d0c584966)\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": 2,
   "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": 3,
   "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": 4,
   "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": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_test_accuracy(model, x_test, y_test):\n",
    "    test_loss, test_acc = model.evaluate(x=x_test, y=y_test, verbose=0)\n",
    "    print('accuracy: {acc:0.3f}'.format(acc=test_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fashion MMIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('uint8')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_full.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. EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stopping = EarlyStopping()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/50\n",
      "8000/8000 - 6s - loss: 1.5632 - accuracy: 0.5504 - val_loss: 1.1315 - val_accuracy: 0.6605\n",
      "Epoch 2/50\n",
      "8000/8000 - 3s - loss: 0.9598 - accuracy: 0.7025 - val_loss: 0.8737 - val_accuracy: 0.7045\n",
      "Epoch 3/50\n",
      "8000/8000 - 2s - loss: 0.7890 - accuracy: 0.7441 - val_loss: 0.7667 - val_accuracy: 0.7335\n",
      "Epoch 4/50\n",
      "8000/8000 - 2s - loss: 0.7075 - accuracy: 0.7679 - val_loss: 0.7185 - val_accuracy: 0.7500\n",
      "Epoch 5/50\n",
      "8000/8000 - 2s - loss: 0.6556 - accuracy: 0.7831 - val_loss: 0.6567 - val_accuracy: 0.7715\n",
      "Epoch 6/50\n",
      "8000/8000 - 2s - loss: 0.6142 - accuracy: 0.7977 - val_loss: 0.6287 - val_accuracy: 0.7790\n",
      "Epoch 7/50\n",
      "8000/8000 - 3s - loss: 0.5885 - accuracy: 0.8009 - val_loss: 0.6041 - val_accuracy: 0.7945\n",
      "Epoch 8/50\n",
      "8000/8000 - 2s - loss: 0.5635 - accuracy: 0.8094 - val_loss: 0.6000 - val_accuracy: 0.7935\n",
      "Epoch 9/50\n",
      "8000/8000 - 2s - loss: 0.5452 - accuracy: 0.8161 - val_loss: 0.5662 - val_accuracy: 0.8005\n",
      "Epoch 10/50\n",
      "8000/8000 - 2s - loss: 0.5283 - accuracy: 0.8213 - val_loss: 0.5539 - val_accuracy: 0.8170\n",
      "Epoch 11/50\n",
      "8000/8000 - 2s - loss: 0.5141 - accuracy: 0.8281 - val_loss: 0.5644 - val_accuracy: 0.7990\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=[early_stopping]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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   "source": [
    "plot_metric(history, 'loss')"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Customizing early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model\n",
    "model = create_model()"
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  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_early_stopping = EarlyStopping(\n",
    "    monitor='val_accuracy', \n",
    "    patience=3, \n",
    "    min_delta=0.001, \n",
    "    mode='max'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/50\n",
      "8000/8000 - 5s - loss: 1.5685 - accuracy: 0.5612 - val_loss: 1.1267 - val_accuracy: 0.6675\n",
      "Epoch 2/50\n",
      "8000/8000 - 2s - loss: 0.9486 - accuracy: 0.7104 - val_loss: 0.8624 - val_accuracy: 0.7200\n",
      "Epoch 3/50\n",
      "8000/8000 - 2s - loss: 0.7890 - accuracy: 0.7434 - val_loss: 0.7680 - val_accuracy: 0.7525\n",
      "Epoch 4/50\n",
      "8000/8000 - 2s - loss: 0.7080 - accuracy: 0.7684 - val_loss: 0.7030 - val_accuracy: 0.7595\n",
      "Epoch 5/50\n",
      "8000/8000 - 2s - loss: 0.6551 - accuracy: 0.7856 - val_loss: 0.6591 - val_accuracy: 0.7745\n",
      "Epoch 6/50\n",
      "8000/8000 - 2s - loss: 0.6172 - accuracy: 0.7939 - val_loss: 0.6421 - val_accuracy: 0.7785\n",
      "Epoch 7/50\n",
      "8000/8000 - 2s - loss: 0.5886 - accuracy: 0.8037 - val_loss: 0.6062 - val_accuracy: 0.7985\n",
      "Epoch 8/50\n",
      "8000/8000 - 2s - loss: 0.5642 - accuracy: 0.8098 - val_loss: 0.5902 - val_accuracy: 0.7980\n",
      "Epoch 9/50\n",
      "8000/8000 - 2s - loss: 0.5462 - accuracy: 0.8180 - val_loss: 0.5729 - val_accuracy: 0.8030\n",
      "Epoch 10/50\n",
      "8000/8000 - 2s - loss: 0.5292 - accuracy: 0.8213 - val_loss: 0.5671 - val_accuracy: 0.8030\n",
      "Epoch 11/50\n",
      "8000/8000 - 3s - loss: 0.5150 - accuracy: 0.8224 - val_loss: 0.5435 - val_accuracy: 0.8200\n",
      "Epoch 12/50\n",
      "8000/8000 - 2s - loss: 0.5043 - accuracy: 0.8290 - val_loss: 0.5311 - val_accuracy: 0.8250\n",
      "Epoch 13/50\n",
      "8000/8000 - 3s - loss: 0.4936 - accuracy: 0.8332 - val_loss: 0.5310 - val_accuracy: 0.8155\n",
      "Epoch 14/50\n",
      "8000/8000 - 2s - loss: 0.4835 - accuracy: 0.8353 - val_loss: 0.5157 - val_accuracy: 0.8245\n",
      "Epoch 15/50\n",
      "8000/8000 - 2s - loss: 0.4757 - accuracy: 0.8397 - val_loss: 0.5299 - val_accuracy: 0.8060\n"
     ]
    }
   ],
   "source": [
    "history_custom = 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=[custom_early_stopping]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metric(history_custom, 'accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. CSVLogger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import CSVLogger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_log = CSVLogger(\"results.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model\n",
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/10\n",
      "8000/8000 - 7s - loss: 1.6322 - accuracy: 0.5404 - val_loss: 1.1908 - val_accuracy: 0.6285\n",
      "Epoch 2/10\n",
      "8000/8000 - 3s - loss: 0.9995 - accuracy: 0.6883 - val_loss: 0.9034 - val_accuracy: 0.6965\n",
      "Epoch 3/10\n",
      "8000/8000 - 3s - loss: 0.8190 - accuracy: 0.7281 - val_loss: 0.7913 - val_accuracy: 0.7280\n",
      "Epoch 4/10\n",
      "8000/8000 - 3s - loss: 0.7333 - accuracy: 0.7551 - val_loss: 0.7253 - val_accuracy: 0.7595\n",
      "Epoch 5/10\n",
      "8000/8000 - 2s - loss: 0.6788 - accuracy: 0.7725 - val_loss: 0.6813 - val_accuracy: 0.7810\n",
      "Epoch 6/10\n",
      "8000/8000 - 2s - loss: 0.6385 - accuracy: 0.7891 - val_loss: 0.6483 - val_accuracy: 0.7860\n",
      "Epoch 7/10\n",
      "8000/8000 - 2s - loss: 0.6062 - accuracy: 0.7968 - val_loss: 0.6201 - val_accuracy: 0.7900\n",
      "Epoch 8/10\n",
      "8000/8000 - 2s - loss: 0.5791 - accuracy: 0.8064 - val_loss: 0.6258 - val_accuracy: 0.7780\n",
      "Epoch 9/10\n",
      "8000/8000 - 2s - loss: 0.5590 - accuracy: 0.8100 - val_loss: 0.5932 - val_accuracy: 0.7945\n",
      "Epoch 10/10\n",
      "8000/8000 - 2s - loss: 0.5418 - accuracy: 0.8164 - val_loss: 0.5700 - val_accuracy: 0.8065\n"
     ]
    }
   ],
   "source": [
    "history_csv_logger = model.fit(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    epochs=10, \n",
    "    validation_split=0.20, \n",
    "    batch_size=64, \n",
    "    verbose=2,\n",
    "    callbacks=[csv_log]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>accuracy</th>\n",
       "      <th>loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>epoch</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.540375</td>\n",
       "      <td>1.632193</td>\n",
       "      <td>0.6285</td>\n",
       "      <td>1.190760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.688250</td>\n",
       "      <td>0.999455</td>\n",
       "      <td>0.6965</td>\n",
       "      <td>0.903381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.728125</td>\n",
       "      <td>0.818999</td>\n",
       "      <td>0.7280</td>\n",
       "      <td>0.791272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.755125</td>\n",
       "      <td>0.733289</td>\n",
       "      <td>0.7595</td>\n",
       "      <td>0.725264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.772500</td>\n",
       "      <td>0.678768</td>\n",
       "      <td>0.7810</td>\n",
       "      <td>0.681306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.789125</td>\n",
       "      <td>0.638452</td>\n",
       "      <td>0.7860</td>\n",
       "      <td>0.648267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.796750</td>\n",
       "      <td>0.606171</td>\n",
       "      <td>0.7900</td>\n",
       "      <td>0.620067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.806375</td>\n",
       "      <td>0.579147</td>\n",
       "      <td>0.7780</td>\n",
       "      <td>0.625771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.810000</td>\n",
       "      <td>0.559040</td>\n",
       "      <td>0.7945</td>\n",
       "      <td>0.593174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.816375</td>\n",
       "      <td>0.541813</td>\n",
       "      <td>0.8065</td>\n",
       "      <td>0.569976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       accuracy      loss  val_accuracy  val_loss\n",
       "epoch                                            \n",
       "0      0.540375  1.632193        0.6285  1.190760\n",
       "1      0.688250  0.999455        0.6965  0.903381\n",
       "2      0.728125  0.818999        0.7280  0.791272\n",
       "3      0.755125  0.733289        0.7595  0.725264\n",
       "4      0.772500  0.678768        0.7810  0.681306\n",
       "5      0.789125  0.638452        0.7860  0.648267\n",
       "6      0.796750  0.606171        0.7900  0.620067\n",
       "7      0.806375  0.579147        0.7780  0.625771\n",
       "8      0.810000  0.559040        0.7945  0.593174\n",
       "9      0.816375  0.541813        0.8065  0.569976"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the CSV\n",
    "import pandas as pd\n",
    "\n",
    "pd.read_csv(\"results.csv\", index_col='epoch')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_path = 'model_checkpoints/'\n",
    "checkpoint = ModelCheckpoint(\n",
    "    filepath=checkpoint_path,\n",
    "    save_freq='epoch',\n",
    "    save_weights_only=True,\n",
    "    verbose=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model\n",
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/10\n",
      "\n",
      "Epoch 00001: saving model to model_checkpoints/\n",
      "8000/8000 - 3s - loss: 1.5483 - accuracy: 0.5533 - val_loss: 1.1100 - val_accuracy: 0.6785\n",
      "Epoch 2/10\n",
      "\n",
      "Epoch 00002: saving model to model_checkpoints/\n",
      "8000/8000 - 1s - loss: 0.9443 - accuracy: 0.7212 - val_loss: 0.8616 - val_accuracy: 0.7140\n",
      "Epoch 3/10\n",
      "\n",
      "Epoch 00003: saving model to model_checkpoints/\n",
      "8000/8000 - 1s - loss: 0.7846 - accuracy: 0.7509 - val_loss: 0.7594 - val_accuracy: 0.7530\n",
      "Epoch 4/10\n",
      "\n",
      "Epoch 00004: saving model to model_checkpoints/\n",
      "8000/8000 - 1s - loss: 0.7058 - accuracy: 0.7742 - val_loss: 0.7243 - val_accuracy: 0.7405\n",
      "Epoch 5/10\n",
      "\n",
      "Epoch 00005: saving model to model_checkpoints/\n",
      "8000/8000 - 1s - loss: 0.6549 - accuracy: 0.7880 - val_loss: 0.6744 - val_accuracy: 0.7640\n",
      "Epoch 6/10\n",
      "\n",
      "Epoch 00006: saving model to model_checkpoints/\n",
      "8000/8000 - 2s - loss: 0.6180 - accuracy: 0.7961 - val_loss: 0.6382 - val_accuracy: 0.7890\n",
      "Epoch 7/10\n",
      "\n",
      "Epoch 00007: saving model to model_checkpoints/\n",
      "8000/8000 - 1s - loss: 0.5897 - accuracy: 0.8051 - val_loss: 0.6090 - val_accuracy: 0.7895\n",
      "Epoch 8/10\n",
      "\n",
      "Epoch 00008: saving model to model_checkpoints/\n",
      "8000/8000 - 1s - loss: 0.5651 - accuracy: 0.8140 - val_loss: 0.5916 - val_accuracy: 0.7985\n",
      "Epoch 9/10\n",
      "\n",
      "Epoch 00009: saving model to model_checkpoints/\n",
      "8000/8000 - 1s - loss: 0.5490 - accuracy: 0.8167 - val_loss: 0.5698 - val_accuracy: 0.8070\n",
      "Epoch 10/10\n",
      "\n",
      "Epoch 00010: saving model to model_checkpoints/\n",
      "8000/8000 - 1s - loss: 0.5314 - accuracy: 0.8219 - val_loss: 0.5683 - val_accuracy: 0.8025\n"
     ]
    }
   ],
   "source": [
    "history_checkpoint = model.fit(\n",
    "    X_train, \n",
    "    y_train, \n",
    "    epochs=5, \n",
    "    validation_split=0.20, \n",
    "    batch_size=64, \n",
    "    verbose=2,\n",
    "    callbacks=[checkpoint]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.787\n"
     ]
    }
   ],
   "source": [
    "get_test_accuracy(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a new model and load weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model\n",
    "new_model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.086\n"
     ]
    }
   ],
   "source": [
    "# Without loading weight\n",
    "get_test_accuracy(new_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.779\n"
     ]
    }
   ],
   "source": [
    "# Load weights\n",
    "new_model.load_weights('model_checkpoints/')\n",
    "# Now test it and the accuracy should be the same as before\n",
    "get_test_accuracy(new_model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ReduceLROnPlateau"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_lr = ReduceLROnPlateau(\n",
    "    monitor='val_loss', \n",
    "    factor=0.2,   \n",
    "    patience=2, \n",
    "    min_lr=0.001,\n",
    "    verbose=2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model\n",
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "Epoch 1/50\n",
      "8000/8000 - 4s - loss: 1.6223 - accuracy: 0.5167 - val_loss: 1.1810 - val_accuracy: 0.6575\n",
      "Epoch 2/50\n",
      "8000/8000 - 1s - loss: 0.9956 - accuracy: 0.6915 - val_loss: 0.8968 - val_accuracy: 0.7085\n",
      "Epoch 3/50\n",
      "8000/8000 - 1s - loss: 0.8161 - accuracy: 0.7370 - val_loss: 0.7827 - val_accuracy: 0.7520\n",
      "Epoch 4/50\n",
      "8000/8000 - 1s - loss: 0.7271 - accuracy: 0.7640 - val_loss: 0.7191 - val_accuracy: 0.7550\n",
      "Epoch 5/50\n",
      "8000/8000 - 1s - loss: 0.6707 - accuracy: 0.7785 - val_loss: 0.6707 - val_accuracy: 0.7650\n",
      "Epoch 6/50\n",
      "8000/8000 - 1s - loss: 0.6280 - accuracy: 0.7920 - val_loss: 0.6507 - val_accuracy: 0.7650\n",
      "Epoch 7/50\n",
      "8000/8000 - 1s - loss: 0.5994 - accuracy: 0.7994 - val_loss: 0.6285 - val_accuracy: 0.7845\n",
      "Epoch 8/50\n",
      "8000/8000 - 1s - loss: 0.5739 - accuracy: 0.8090 - val_loss: 0.5990 - val_accuracy: 0.7895\n",
      "Epoch 9/50\n",
      "8000/8000 - 1s - loss: 0.5556 - accuracy: 0.8146 - val_loss: 0.5936 - val_accuracy: 0.7960\n",
      "Epoch 10/50\n",
      "8000/8000 - 1s - loss: 0.5405 - accuracy: 0.8164 - val_loss: 0.5623 - val_accuracy: 0.8005\n",
      "Epoch 11/50\n",
      "8000/8000 - 1s - loss: 0.5225 - accuracy: 0.8250 - val_loss: 0.5737 - val_accuracy: 0.7950\n",
      "Epoch 12/50\n",
      "8000/8000 - 1s - loss: 0.5123 - accuracy: 0.8246 - val_loss: 0.5356 - val_accuracy: 0.8125\n",
      "Epoch 13/50\n",
      "8000/8000 - 1s - loss: 0.5011 - accuracy: 0.8282 - val_loss: 0.5314 - val_accuracy: 0.8190\n",
      "Epoch 14/50\n",
      "8000/8000 - 1s - loss: 0.4923 - accuracy: 0.8319 - val_loss: 0.5255 - val_accuracy: 0.8145\n",
      "Epoch 15/50\n",
      "8000/8000 - 1s - loss: 0.4855 - accuracy: 0.8345 - val_loss: 0.5258 - val_accuracy: 0.8140\n",
      "Epoch 16/50\n",
      "8000/8000 - 1s - loss: 0.4731 - accuracy: 0.8388 - val_loss: 0.5141 - val_accuracy: 0.8190\n",
      "Epoch 17/50\n",
      "8000/8000 - 1s - loss: 0.4640 - accuracy: 0.8406 - val_loss: 0.5136 - val_accuracy: 0.8265\n",
      "Epoch 18/50\n",
      "8000/8000 - 1s - loss: 0.4599 - accuracy: 0.8421 - val_loss: 0.5025 - val_accuracy: 0.8290\n",
      "Epoch 19/50\n",
      "8000/8000 - 1s - loss: 0.4533 - accuracy: 0.8475 - val_loss: 0.4994 - val_accuracy: 0.8195\n",
      "Epoch 20/50\n",
      "8000/8000 - 2s - loss: 0.4447 - accuracy: 0.8515 - val_loss: 0.4962 - val_accuracy: 0.8335\n",
      "Epoch 21/50\n",
      "8000/8000 - 1s - loss: 0.4394 - accuracy: 0.8515 - val_loss: 0.5011 - val_accuracy: 0.8200\n",
      "Epoch 22/50\n",
      "8000/8000 - 1s - loss: 0.4338 - accuracy: 0.8543 - val_loss: 0.4865 - val_accuracy: 0.8290\n",
      "Epoch 23/50\n",
      "8000/8000 - 1s - loss: 0.4274 - accuracy: 0.8533 - val_loss: 0.4785 - val_accuracy: 0.8340\n",
      "Epoch 24/50\n",
      "8000/8000 - 1s - loss: 0.4240 - accuracy: 0.8533 - val_loss: 0.4760 - val_accuracy: 0.8355\n",
      "Epoch 25/50\n",
      "8000/8000 - 1s - loss: 0.4178 - accuracy: 0.8549 - val_loss: 0.4771 - val_accuracy: 0.8360\n",
      "Epoch 26/50\n",
      "8000/8000 - 1s - loss: 0.4125 - accuracy: 0.8599 - val_loss: 0.4718 - val_accuracy: 0.8350\n",
      "Epoch 27/50\n",
      "8000/8000 - 1s - loss: 0.4108 - accuracy: 0.8624 - val_loss: 0.4747 - val_accuracy: 0.8325\n",
      "Epoch 28/50\n",
      "8000/8000 - 1s - loss: 0.4079 - accuracy: 0.8618 - val_loss: 0.4603 - val_accuracy: 0.8430\n",
      "Epoch 29/50\n",
      "8000/8000 - 1s - loss: 0.3994 - accuracy: 0.8656 - val_loss: 0.4617 - val_accuracy: 0.8360\n",
      "Epoch 30/50\n",
      "\n",
      "Epoch 00030: ReduceLROnPlateau reducing learning rate to 0.0019999999552965165.\n",
      "8000/8000 - 1s - loss: 0.3975 - accuracy: 0.8641 - val_loss: 0.4618 - val_accuracy: 0.8405\n",
      "Epoch 31/50\n",
      "8000/8000 - 1s - loss: 0.3826 - accuracy: 0.8712 - val_loss: 0.4584 - val_accuracy: 0.8400\n",
      "Epoch 32/50\n",
      "8000/8000 - 1s - loss: 0.3807 - accuracy: 0.8721 - val_loss: 0.4539 - val_accuracy: 0.8420\n",
      "Epoch 33/50\n",
      "8000/8000 - 1s - loss: 0.3799 - accuracy: 0.8714 - val_loss: 0.4538 - val_accuracy: 0.8455\n",
      "Epoch 34/50\n",
      "8000/8000 - 1s - loss: 0.3786 - accuracy: 0.8714 - val_loss: 0.4529 - val_accuracy: 0.8435\n",
      "Epoch 35/50\n",
      "8000/8000 - 1s - loss: 0.3778 - accuracy: 0.8726 - val_loss: 0.4516 - val_accuracy: 0.8440\n",
      "Epoch 36/50\n",
      "8000/8000 - 1s - loss: 0.3769 - accuracy: 0.8725 - val_loss: 0.4521 - val_accuracy: 0.8430\n",
      "Epoch 37/50\n",
      "\n",
      "Epoch 00037: ReduceLROnPlateau reducing learning rate to 0.001.\n",
      "8000/8000 - 1s - loss: 0.3763 - accuracy: 0.8730 - val_loss: 0.4536 - val_accuracy: 0.8430\n",
      "Epoch 38/50\n",
      "8000/8000 - 1s - loss: 0.3746 - accuracy: 0.8733 - val_loss: 0.4526 - val_accuracy: 0.8445\n",
      "Epoch 39/50\n",
      "8000/8000 - 1s - loss: 0.3743 - accuracy: 0.8729 - val_loss: 0.4507 - val_accuracy: 0.8465\n",
      "Epoch 40/50\n",
      "8000/8000 - 1s - loss: 0.3730 - accuracy: 0.8752 - val_loss: 0.4536 - val_accuracy: 0.8445\n",
      "Epoch 41/50\n",
      "8000/8000 - 1s - loss: 0.3730 - accuracy: 0.8739 - val_loss: 0.4500 - val_accuracy: 0.8475\n",
      "Epoch 42/50\n",
      "8000/8000 - 1s - loss: 0.3727 - accuracy: 0.8745 - val_loss: 0.4507 - val_accuracy: 0.8495\n",
      "Epoch 43/50\n",
      "\n",
      "Epoch 00043: ReduceLROnPlateau reducing learning rate to 0.001.\n",
      "8000/8000 - 1s - loss: 0.3729 - accuracy: 0.8735 - val_loss: 0.4505 - val_accuracy: 0.8465\n",
      "Epoch 44/50\n",
      "8000/8000 - 1s - loss: 0.3720 - accuracy: 0.8735 - val_loss: 0.4498 - val_accuracy: 0.8460\n",
      "Epoch 45/50\n",
      "8000/8000 - 1s - loss: 0.3716 - accuracy: 0.8737 - val_loss: 0.4498 - val_accuracy: 0.8470\n",
      "Epoch 46/50\n",
      "\n",
      "Epoch 00046: ReduceLROnPlateau reducing learning rate to 0.001.\n",
      "8000/8000 - 1s - loss: 0.3713 - accuracy: 0.8735 - val_loss: 0.4501 - val_accuracy: 0.8480\n",
      "Epoch 47/50\n",
      "8000/8000 - 1s - loss: 0.3708 - accuracy: 0.8763 - val_loss: 0.4492 - val_accuracy: 0.8450\n",
      "Epoch 48/50\n",
      "8000/8000 - 1s - loss: 0.3702 - accuracy: 0.8751 - val_loss: 0.4482 - val_accuracy: 0.8445\n",
      "Epoch 49/50\n",
      "8000/8000 - 1s - loss: 0.3699 - accuracy: 0.8741 - val_loss: 0.4492 - val_accuracy: 0.8485\n",
      "Epoch 50/50\n",
      "\n",
      "Epoch 00050: ReduceLROnPlateau reducing learning rate to 0.001.\n",
      "8000/8000 - 1s - loss: 0.3696 - accuracy: 0.8742 - val_loss: 0.4483 - val_accuracy: 0.8455\n"
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   ],
   "source": [
    "history_reduce_lr = 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=[reduce_lr]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
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       "<Figure size 432x288 with 1 Axes>"
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   ],
   "source": [
    "plot_metric(history_reduce_lr, 'loss')"
   ]
  },
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   "metadata": {},
   "source": [
    "## 5. LearningRateScheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import LearningRateScheduler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lr_decay(epoch, lr):\n",
    "    if epoch != 0 and epoch % 5 == 0:\n",
    "        return lr * 0.2\n",
    "    return lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model\n",
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Train on 8000 samples, validate on 2000 samples\n",
      "\n",
      "Epoch 00001: LearningRateScheduler reducing learning rate to 0.009999999776482582.\n",
      "Epoch 1/20\n",
      "8000/8000 - 3s - loss: 1.5567 - accuracy: 0.5614 - val_loss: 1.1245 - val_accuracy: 0.6720\n",
      "\n",
      "Epoch 00002: LearningRateScheduler reducing learning rate to 0.009999999776482582.\n",
      "Epoch 2/20\n",
      "8000/8000 - 1s - loss: 0.9460 - accuracy: 0.7106 - val_loss: 0.8574 - val_accuracy: 0.7265\n",
      "\n",
      "Epoch 00003: LearningRateScheduler reducing learning rate to 0.009999999776482582.\n",
      "Epoch 3/20\n",
      "8000/8000 - 1s - loss: 0.7757 - accuracy: 0.7487 - val_loss: 0.7673 - val_accuracy: 0.7380\n",
      "\n",
      "Epoch 00004: LearningRateScheduler reducing learning rate to 0.009999999776482582.\n",
      "Epoch 4/20\n",
      "8000/8000 - 1s - loss: 0.6925 - accuracy: 0.7740 - val_loss: 0.7008 - val_accuracy: 0.7545\n",
      "\n",
      "Epoch 00005: LearningRateScheduler reducing learning rate to 0.009999999776482582.\n",
      "Epoch 5/20\n",
      "8000/8000 - 1s - loss: 0.6433 - accuracy: 0.7851 - val_loss: 0.6497 - val_accuracy: 0.7720\n",
      "\n",
      "Epoch 00006: LearningRateScheduler reducing learning rate to 0.0019999999552965165.\n",
      "Epoch 6/20\n",
      "8000/8000 - 1s - loss: 0.6098 - accuracy: 0.7996 - val_loss: 0.6400 - val_accuracy: 0.7825\n",
      "\n",
      "Epoch 00007: LearningRateScheduler reducing learning rate to 0.001999999862164259.\n",
      "Epoch 7/20\n",
      "8000/8000 - 1s - loss: 0.6019 - accuracy: 0.8020 - val_loss: 0.6347 - val_accuracy: 0.7860\n",
      "\n",
      "Epoch 00008: LearningRateScheduler reducing learning rate to 0.001999999862164259.\n",
      "Epoch 8/20\n",
      "8000/8000 - 1s - loss: 0.5955 - accuracy: 0.8052 - val_loss: 0.6289 - val_accuracy: 0.7855\n",
      "\n",
      "Epoch 00009: LearningRateScheduler reducing learning rate to 0.001999999862164259.\n",
      "Epoch 9/20\n",
      "8000/8000 - 1s - loss: 0.5892 - accuracy: 0.8071 - val_loss: 0.6231 - val_accuracy: 0.7850\n",
      "\n",
      "Epoch 00010: LearningRateScheduler reducing learning rate to 0.001999999862164259.\n",
      "Epoch 10/20\n",
      "8000/8000 - 1s - loss: 0.5836 - accuracy: 0.8074 - val_loss: 0.6186 - val_accuracy: 0.7870\n",
      "\n",
      "Epoch 00011: LearningRateScheduler reducing learning rate to 0.0003999999724328518.\n",
      "Epoch 11/20\n",
      "8000/8000 - 1s - loss: 0.5780 - accuracy: 0.8100 - val_loss: 0.6155 - val_accuracy: 0.7925\n",
      "\n",
      "Epoch 00012: LearningRateScheduler reducing learning rate to 0.0003999999607913196.\n",
      "Epoch 12/20\n",
      "8000/8000 - 1s - loss: 0.5767 - accuracy: 0.8095 - val_loss: 0.6148 - val_accuracy: 0.7905\n",
      "\n",
      "Epoch 00013: LearningRateScheduler reducing learning rate to 0.0003999999607913196.\n",
      "Epoch 13/20\n",
      "8000/8000 - 1s - loss: 0.5757 - accuracy: 0.8104 - val_loss: 0.6136 - val_accuracy: 0.7910\n",
      "\n",
      "Epoch 00014: LearningRateScheduler reducing learning rate to 0.0003999999607913196.\n",
      "Epoch 14/20\n",
      "8000/8000 - 1s - loss: 0.5746 - accuracy: 0.8108 - val_loss: 0.6122 - val_accuracy: 0.7915\n",
      "\n",
      "Epoch 00015: LearningRateScheduler reducing learning rate to 0.0003999999607913196.\n",
      "Epoch 15/20\n",
      "8000/8000 - 1s - loss: 0.5734 - accuracy: 0.8111 - val_loss: 0.6118 - val_accuracy: 0.7940\n",
      "\n",
      "Epoch 00016: LearningRateScheduler reducing learning rate to 7.999999215826393e-05.\n",
      "Epoch 16/20\n",
      "8000/8000 - 1s - loss: 0.5724 - accuracy: 0.8115 - val_loss: 0.6114 - val_accuracy: 0.7925\n",
      "\n",
      "Epoch 00017: LearningRateScheduler reducing learning rate to 7.99999907030724e-05.\n",
      "Epoch 17/20\n",
      "8000/8000 - 2s - loss: 0.5721 - accuracy: 0.8112 - val_loss: 0.6112 - val_accuracy: 0.7920\n",
      "\n",
      "Epoch 00018: LearningRateScheduler reducing learning rate to 7.99999907030724e-05.\n",
      "Epoch 18/20\n",
      "8000/8000 - 1s - loss: 0.5719 - accuracy: 0.8116 - val_loss: 0.6109 - val_accuracy: 0.7915\n",
      "\n",
      "Epoch 00019: LearningRateScheduler reducing learning rate to 7.99999907030724e-05.\n",
      "Epoch 19/20\n",
      "8000/8000 - 1s - loss: 0.5717 - accuracy: 0.8125 - val_loss: 0.6107 - val_accuracy: 0.7920\n",
      "\n",
      "Epoch 00020: LearningRateScheduler reducing learning rate to 7.99999907030724e-05.\n",
      "Epoch 20/20\n",
      "8000/8000 - 1s - loss: 0.5714 - accuracy: 0.8116 - val_loss: 0.6104 - val_accuracy: 0.7920\n"
     ]
    }
   ],
   "source": [
    "history_lr_schedule = 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=[LearningRateScheduler(lr_decay, verbose=1)]\n",
    ")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
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   "source": [
    "plot_lr(history_lr_schedule)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. LambdaCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import LambdaCallback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_callback = LambdaCallback(\n",
    "    on_epoch_begin=lambda epoch,logs: print('Starting Epoch {}!'.format(epoch+1))\n",
    ")\n",
    "\n",
    "batch_loss_callback = LambdaCallback(\n",
    "    on_batch_end=lambda batch,logs: print('\\n After batch {}, the loss is {:7.2f}.'.format(batch, logs['loss']))\n",
    ")\n",
    "\n",
    "train_finish_callback = LambdaCallback(\n",
    "    on_train_end=lambda logs: print('Training finished!')\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new model\n",
    "model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Epoch 1!\n",
      "\n",
      " After batch 0, the loss is    0.41.\n",
      "\n",
      " After batch 1, the loss is    0.41.\n",
      "\n",
      " After batch 2, the loss is    0.42.\n",
      "\n",
      " After batch 3, the loss is    0.39.\n",
      "Starting Epoch 2!\n",
      "\n",
      " After batch 0, the loss is    0.40.\n",
      "\n",
      " After batch 1, the loss is    0.42.\n",
      "\n",
      " After batch 2, the loss is    0.41.\n",
      "\n",
      " After batch 3, the loss is    0.41.\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "history_lambda_callback = model.fit(\n",
    "    X_train, \n",
    "    y_train,\n",
    "    epochs=2,                  # change epoch to 2 for demo purpose \n",
    "    validation_split=0.20, \n",
    "    batch_size=2000,           # change to 2000 for demo purpose\n",
    "    verbose=False,\n",
    "    callbacks=[epoch_callback, batch_loss_callback, train_finish_callback]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## That's it\n",
    "\n",
    "Thanks\n",
    "\n",
    "This is a notebook for the medium article [A practical introduction to Keras Callbacks](https://medium.com/@bindiatwork/a-practical-introduction-to-keras-callbacks-in-tensorflow-2-705d0c584966)\n",
    "\n",
    "Please check out article for instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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