10_Advanced_Keras_model_checking_and_monitoring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ModelCheckpoint와 EarlyStopping 콜백"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cllbeack_list = [\n",
    "    \n",
    "    keras.callbacks.EarlyStopping(\n",
    "        monitor='val_acc',\n",
    "        patience=1,\n",
    "    ),\n",
    "    keras.callbacks.ModelCheckpoint(\n",
    "        filepath='datasets/my_model.h5',\n",
    "        monitor='val_loss',\n",
    "        save_best_only=True,\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['acc'])\n",
    "\n",
    "model.fit(x, y,\n",
    "          epochs=10,\n",
    "          batch_size=32,\n",
    "          callbacks=cllbeack_list,\n",
    "          validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ReduceLROnPlateau 콜백"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks_list = [\n",
    "    keras.callbacks.ReduceLROnPlateau(\n",
    "        monitor='val_loss',\n",
    "        factor=0.1,\n",
    "        patience=10,\n",
    "    )\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(x, y,\n",
    "          epochs=10,\n",
    "          batch_size=32,\n",
    "          callbacks=cllbeack_list,\n",
    "          validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 자신만의 콜백 만들기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras import layers\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ActivationLogger(keras.callbacks.Callback):\n",
    "    \n",
    "    def set_model(self, model):\n",
    "        self.model = model\n",
    "        layer_outputs = [layer.output for layer in model.layers]\n",
    "        self.activation_model = keras.models.Model(model.input,\n",
    "                                                   layer_outputs)\n",
    "        \n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        if self.validation_data is None:\n",
    "            raise RuntimeError('Requires validation_data.')\n",
    "            \n",
    "        validation_sample = self.validation_data[0][0:1]\n",
    "        activations = self.activation_model.predict(validation_sample)\n",
    "        f = open('activations_at_epoch_' + str(epoch) + '.npz', 'wb')\n",
    "        np.savez(f, activations)\n",
    "        f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 텐서보드"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras import layers\n",
    "from keras.datasets import imdb\n",
    "from keras.preprocessing import sequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embed (Embedding)            (None, 500, 128)          256000    \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 494, 32)           28704     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 98, 32)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 92, 32)            7200      \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 291,937\n",
      "Trainable params: 291,937\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "max_features = 2000\n",
    "max_len = 500\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\n",
    "x_train = sequence.pad_sequences(x_train, maxlen=max_len)\n",
    "x_test  = sequence.pad_sequences(x_test,  maxlen=max_len)\n",
    "\n",
    "model = keras.models.Sequential()\n",
    "model.add(layers.Embedding(max_features, 128,\n",
    "                           input_length=max_len,\n",
    "                           name='embed'))\n",
    "model.add(layers.Conv1D(32, 7, activation='relu'))\n",
    "model.add(layers.MaxPool1D(5))\n",
    "model.add(layers.Conv1D(32, 7, activation='relu'))\n",
    "model.add(layers.GlobalMaxPool1D())\n",
    "model.add(layers.Dense(1))\n",
    "model.summary()\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 20000 samples, validate on 5000 samples\n",
      "Epoch 1/20\n",
      "20000/20000 [==============================] - 3s 143us/step - loss: 0.1112 - acc: 0.2882 - val_loss: 1.0227 - val_acc: 0.3018\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "To visualize embeddings, embeddings_data must be provided.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-35-c2cf33d89363>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     11\u001b[0m                     \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m                     \u001b[0mvalidation_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m                     callbacks=callbacks)\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1037\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1038\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1039\u001b[1;33m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1040\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1041\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m    215\u001b[0m                         \u001b[1;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mval_outs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    216\u001b[0m                             \u001b[0mepoch_logs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'val_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ml\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 217\u001b[1;33m         \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    218\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback_model\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    219\u001b[0m             \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m     77\u001b[0m         \u001b[0mlogs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlogs\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     78\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 79\u001b[1;33m             \u001b[0mcallback\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mon_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tf_gpu\\lib\\site-packages\\keras\\callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[1;34m(self, epoch, logs)\u001b[0m\n\u001b[0;32m    913\u001b[0m                              \"provided, and cannot be a generator.\")\n\u001b[0;32m    914\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0membeddings_data\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0membeddings_freq\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 915\u001b[1;33m             raise ValueError(\"To visualize embeddings, embeddings_data must \"\n\u001b[0m\u001b[0;32m    916\u001b[0m                              \"be provided.\")\n\u001b[0;32m    917\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalidation_data\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhistogram_freq\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: To visualize embeddings, embeddings_data must be provided."
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "    keras.callbacks.TensorBoard(\n",
    "        log_dir='my_log_dir',\n",
    "        histogram_freq=1,\n",
    "        embeddings_freq=1,\n",
    "        #embeddings_data=x_train,\n",
    "    )\n",
    "]\n",
    "history = model.fit(x_train, y_train,\n",
    "                    epochs=20,\n",
    "                    batch_size=128,\n",
    "                    validation_split=0.2,\n",
    "                    callbacks=callbacks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_model(model, to_file='datasets/model.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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