U5.07 - Sequences generation using LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget --no-cache -O init.py -q https://raw.githubusercontent.com/jdariasl/DiplomadoEPM_2019/master/Modulo4/init.py\n",
    "from init import init; init(force_download=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "    print (\"setting tensorflow version in colab\")\n",
    "    %tensorflow_version 2.x\n",
    "    %load_ext tensorboard\n",
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sampling\n",
    "\n",
    "A recurrence Neural Network can be used as a Generative model once it was trained. Currently this is a common practice not only to study how well a model has learned a problem, but to learn more about the problem domain itself. In fact, this approach is being used for music generation and composition.\n",
    "\n",
    "The process of generation is explained in the picture below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {
      "image/png": {
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='local/imgs/dinos3.png', width=800)\n",
    "#<img src=\"local/imgs/dinos3.png\" style=\"width:500;height:300px;\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1467,
     "status": "ok",
     "timestamp": 1555975727588,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "L124Iiv3dcSl",
    "outputId": "18d268d6-4d4e-418e-9e8a-daae981918eb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Masking\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from keras.utils import np_utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 2042,
     "status": "ok",
     "timestamp": 1555975730643,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "sCLzUHaydf0L",
    "outputId": "d506a656-6af5-48b8-b3b5-4f0a98e8096e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package gutenberg to /home/julian/nltk_data...\n",
      "[nltk_data]   Package gutenberg is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('gutenberg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "77PUPiYEdkXx"
   },
   "outputs": [],
   "source": [
    "# load ascii text and covert to lowercase\n",
    "raw_text = nltk.corpus.gutenberg.raw('bible-kjv.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 55
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 377,
     "status": "ok",
     "timestamp": 1555975733415,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "PSxrzWNQdnwL",
    "outputId": "2951ff2e-d20e-4ca0-b028-cb57b33dde5f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Genesis\\n\\n\\n1:1 In the beginning God created the heaven and the earth.\\n\\n1:2 And the earth was without form, and void; and darkness was upon\\nthe face of the deep. And the Spirit of God moved upon the face of the\\nwaters.\\n\\n1:3 And God said, Let there be light: and there was light.\\n\\n1:4 And God saw the light, that it was good: and God divided the light\\nfrom the darkness.\\n\\n1:5 And God called the light Day, and the darkness he called Night.\\nAnd the evening and the morning were the first day.\\n\\n1:6 And God said, Let there be a firmament in the midst of the waters,\\nand let it divide the waters from the waters.\\n\\n1:7 And God made the firmament, and divided the waters which were\\nunder the firmament from the waters which were above the firmament:\\nand it was so.\\n\\n1:8 And God called the firmament Heaven. And the evening and the\\nmorning were the second day.\\n\\n1:9 And God said, Let the waters under the heav'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_text[100:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t4hStmi5dpyb"
   },
   "outputs": [],
   "source": [
    "# create mapping of unique chars to integers\n",
    "chars = sorted(list(set(raw_text)))\n",
    "char_to_int = dict((c, i) for i, c in enumerate(chars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1395
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 397,
     "status": "ok",
     "timestamp": 1555975737585,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "e0-Sf-QxdugV",
    "outputId": "c6ffd2bb-53c8-47ba-f873-96ba2c1cdb7e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'\\n': 0,\n",
       " ' ': 1,\n",
       " '!': 2,\n",
       " \"'\": 3,\n",
       " '(': 4,\n",
       " ')': 5,\n",
       " ',': 6,\n",
       " '-': 7,\n",
       " '.': 8,\n",
       " '0': 9,\n",
       " '1': 10,\n",
       " '2': 11,\n",
       " '3': 12,\n",
       " '4': 13,\n",
       " '5': 14,\n",
       " '6': 15,\n",
       " '7': 16,\n",
       " '8': 17,\n",
       " '9': 18,\n",
       " ':': 19,\n",
       " ';': 20,\n",
       " '?': 21,\n",
       " 'A': 22,\n",
       " 'B': 23,\n",
       " 'C': 24,\n",
       " 'D': 25,\n",
       " 'E': 26,\n",
       " 'F': 27,\n",
       " 'G': 28,\n",
       " 'H': 29,\n",
       " 'I': 30,\n",
       " 'J': 31,\n",
       " 'K': 32,\n",
       " 'L': 33,\n",
       " 'M': 34,\n",
       " 'N': 35,\n",
       " 'O': 36,\n",
       " 'P': 37,\n",
       " 'Q': 38,\n",
       " 'R': 39,\n",
       " 'S': 40,\n",
       " 'T': 41,\n",
       " 'U': 42,\n",
       " 'V': 43,\n",
       " 'W': 44,\n",
       " 'Y': 45,\n",
       " 'Z': 46,\n",
       " '[': 47,\n",
       " ']': 48,\n",
       " 'a': 49,\n",
       " 'b': 50,\n",
       " 'c': 51,\n",
       " 'd': 52,\n",
       " 'e': 53,\n",
       " 'f': 54,\n",
       " 'g': 55,\n",
       " 'h': 56,\n",
       " 'i': 57,\n",
       " 'j': 58,\n",
       " 'k': 59,\n",
       " 'l': 60,\n",
       " 'm': 61,\n",
       " 'n': 62,\n",
       " 'o': 63,\n",
       " 'p': 64,\n",
       " 'q': 65,\n",
       " 'r': 66,\n",
       " 's': 67,\n",
       " 't': 68,\n",
       " 'u': 69,\n",
       " 'v': 70,\n",
       " 'w': 71,\n",
       " 'x': 72,\n",
       " 'y': 73,\n",
       " 'z': 74}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_to_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 354,
     "status": "ok",
     "timestamp": 1555975740042,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "E3QmRjcudwAV",
    "outputId": "e47e76f7-1173-47da-bb99-745c3c965ac9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Characters:  4332554\n",
      "Total Vocab:  75\n"
     ]
    }
   ],
   "source": [
    "n_chars = len(raw_text)\n",
    "n_vocab = len(chars)\n",
    "print(\"Total Characters: \", n_chars)\n",
    "print(\"Total Vocab: \", n_vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train the model we are going to use sequences of 60 characters and because of the data set is too large, we are going to use only the firs 200000 sequences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1076,
     "status": "ok",
     "timestamp": 1555975743874,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "PdIXZV0bdyav",
    "outputId": "16d75c9d-0ce4-4016-93ff-21655110c18b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Patterns:  66647\n"
     ]
    }
   ],
   "source": [
    "# prepare the dataset of input to output pairs encoded as integers\n",
    "seq_length = 60\n",
    "dataX = []\n",
    "dataY = []\n",
    "n_chars = 200000\n",
    "for i in range(0, n_chars - seq_length, 3):\n",
    "    seq_in = raw_text[i:i + seq_length]\n",
    "    seq_out = raw_text[i + seq_length]\n",
    "    dataX.append([char_to_int[char] for char in seq_in])\n",
    "    dataY.append(char_to_int[seq_out])\n",
    "n_patterns = len(dataX)\n",
    "print(\"Total Patterns: \", n_patterns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aB2M7vJHd68-"
   },
   "outputs": [],
   "source": [
    "# reshape X to be [samples, time steps, features]\n",
    "X = np.reshape(dataX, (n_patterns, seq_length, 1))\n",
    "# normalize\n",
    "X = X / float(n_vocab)\n",
    "# one hot encode the output variable\n",
    "y = np_utils.to_categorical(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xw2XJ482eASh"
   },
   "outputs": [],
   "source": [
    "int_to_char = dict((i, c) for i, c in enumerate(chars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "VnMaAPMeVbYq"
   },
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2])))\n",
    "model.add(Dense(y.shape[1], activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the entire dataset is used for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 825
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1106024,
     "status": "ok",
     "timestamp": 1555947067072,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "tIOTswjieGWA",
    "outputId": "5df072e0-e6a3-4582-ccb6-837293359743"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 1.0022\n",
      "Epoch 2/40\n",
      "521/521 [==============================] - 9s 16ms/step - loss: 0.9624\n",
      "Epoch 3/40\n",
      "521/521 [==============================] - 9s 17ms/step - loss: 0.9504\n",
      "Epoch 4/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.9471\n",
      "Epoch 5/40\n",
      "521/521 [==============================] - 9s 18ms/step - loss: 0.9419\n",
      "Epoch 6/40\n",
      "521/521 [==============================] - 9s 17ms/step - loss: 0.9356\n",
      "Epoch 7/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.9282\n",
      "Epoch 8/40\n",
      "521/521 [==============================] - 7s 13ms/step - loss: 0.9244\n",
      "Epoch 9/40\n",
      "521/521 [==============================] - 7s 14ms/step - loss: 0.9171\n",
      "Epoch 10/40\n",
      "521/521 [==============================] - 7s 14ms/step - loss: 0.9015\n",
      "Epoch 11/40\n",
      "521/521 [==============================] - 8s 16ms/step - loss: 0.8995\n",
      "Epoch 12/40\n",
      "521/521 [==============================] - 9s 17ms/step - loss: 0.8889\n",
      "Epoch 13/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.8891\n",
      "Epoch 14/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.8749\n",
      "Epoch 15/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.8700\n",
      "Epoch 16/40\n",
      "521/521 [==============================] - 9s 17ms/step - loss: 0.8557\n",
      "Epoch 17/40\n",
      "521/521 [==============================] - 8s 16ms/step - loss: 0.8497\n",
      "Epoch 18/40\n",
      "521/521 [==============================] - 8s 16ms/step - loss: 0.8518\n",
      "Epoch 19/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.8420\n",
      "Epoch 20/40\n",
      "521/521 [==============================] - 8s 16ms/step - loss: 0.8376\n",
      "Epoch 21/40\n",
      "521/521 [==============================] - 8s 16ms/step - loss: 0.8306\n",
      "Epoch 22/40\n",
      "521/521 [==============================] - 8s 16ms/step - loss: 0.8210\n",
      "Epoch 23/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.8234\n",
      "Epoch 24/40\n",
      "521/521 [==============================] - 8s 16ms/step - loss: 0.8141\n",
      "Epoch 25/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.8157\n",
      "Epoch 26/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.8094\n",
      "Epoch 27/40\n",
      "521/521 [==============================] - 7s 13ms/step - loss: 0.8010\n",
      "Epoch 28/40\n",
      "521/521 [==============================] - 7s 14ms/step - loss: 0.7984\n",
      "Epoch 29/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.7920\n",
      "Epoch 30/40\n",
      "521/521 [==============================] - 8s 16ms/step - loss: 0.7911\n",
      "Epoch 31/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.7839\n",
      "Epoch 32/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.7727\n",
      "Epoch 33/40\n",
      "521/521 [==============================] - 7s 14ms/step - loss: 0.7677\n",
      "Epoch 34/40\n",
      "521/521 [==============================] - 7s 14ms/step - loss: 0.7721\n",
      "Epoch 35/40\n",
      "521/521 [==============================] - 7s 14ms/step - loss: 0.7634\n",
      "Epoch 36/40\n",
      "521/521 [==============================] - 7s 14ms/step - loss: 0.7704\n",
      "Epoch 37/40\n",
      "521/521 [==============================] - 8s 14ms/step - loss: 0.7623\n",
      "Epoch 38/40\n",
      "521/521 [==============================] - 8s 15ms/step - loss: 0.7579\n",
      "Epoch 39/40\n",
      "521/521 [==============================] - 7s 13ms/step - loss: 0.7594\n",
      "Epoch 40/40\n",
      "521/521 [==============================] - 7s 13ms/step - loss: 0.7535\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f5ff8543450>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y, epochs=40, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qMekNHKIeOvl"
   },
   "outputs": [],
   "source": [
    "int_to_char = dict((i, c) for i, c in enumerate(chars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4422,
     "status": "ok",
     "timestamp": 1555947905172,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "YDxojdGrt_HJ",
    "outputId": "7f6d35e7-85bc-4a1d-ab28-f764efc87625"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed:\n",
      "\" d, Let me now leave with thee some of the folk that\n",
      "are with \"\n",
      " mim \n",
      "and he the land of Cgnaan. and sett she tane of the minse of the garten  and bote tot brethrrn, and thet hi tie land of the LORD.\n",
      "\n",
      "11:1 And ht came to pass, thet the wai moree of the garthn of the garten of the garth, and bote tot whee anl the garth. \n",
      "1::1 And Goa said, Tets tetu br th toe aorl bf hotde fn the fard of the earth,\n",
      "\n",
      "1::1 And Goa said, Tets tetu br th toe aorl bf Godhanh wu ham, \n",
      "2::1 And Goa sand unto his  T wile toon thye the eroteng of the LORD.\n",
      "\n",
      "11:1 And ht came to pass, thet the wai moree of the garthn of the garten of the garth, and bote tot whee anl the garth. \n",
      "1::1 And Goa said, Tets tetu br th toe aorl bf hotde fn the fard of the earth,\n",
      "\n",
      "1::1 And Goa said, Tets tetu br th toe aorl bf Godhanh wu ham, \n",
      "2::1 And Goa sand unto his  T wile toon thye the eroteng of the LORD.\n",
      "\n",
      "11:1 And ht came to pass, thet the wai moree of the garthn of the garten of the garth, and bote tot whee anl the garth. \n",
      "1::1 And Goa said, Tets tetu br th toe aorl bf hotde fn the fard of th\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# pick a random seed\n",
    "start = np.random.randint(0, len(dataX)-1)\n",
    "pattern = dataX[start]\n",
    "print(\"Seed:\")\n",
    "print(\"\\\"\", ''.join([int_to_char[value] for value in pattern]), \"\\\"\")\n",
    "# generate characters\n",
    "for i in range(1000):\n",
    "    x = np.reshape(pattern, (1, len(pattern), 1))\n",
    "    x = x / float(n_vocab)\n",
    "    prediction = model.predict(x, verbose=0)\n",
    "    index = np.argmax(prediction)\n",
    "    result = int_to_char[index]\n",
    "    seq_in = [int_to_char[value] for value in pattern]\n",
    "    sys.stdout.write(result)\n",
    "    pattern.append(index)\n",
    "    pattern = pattern[1:len(pattern)]\n",
    "print(\"\\nDone.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The result is not what we expected mainly because of three resons:\n",
    "\n",
    "- The model requires to be trained with a larger dataset in order to better capture the dynamics of the language.\n",
    "- During validation it is not recommendable to select the output with maximum probability but to use the output distribution as parameters to sample from a multinomial distribution. This avoid the model to get stuck in a loop.\n",
    "- A more flexible model with more data could get better results. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "R_fcPxZ0Ysqq"
   },
   "outputs": [],
   "source": [
    "def sample(preds, temperature=1.0):\n",
    "    # helper function to sample an index from a probability array\n",
    "    preds = np.asarray(preds).astype('float64')\n",
    "    preds = np.log(preds) / temperature\n",
    "    exp_preds = np.exp(preds)\n",
    "    preds = exp_preds / np.sum(exp_preds)\n",
    "    probas = np.random.multinomial(1, preds, 1)\n",
    "    return np.argmax(probas)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MRer0n2iV25k"
   },
   "source": [
    "## Using a more complex model with the whole dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This problem is complex computationally speaking, so the next model was run using GPU. The LSTM layers were replaced by CuDNNLSTM that are suitable to GPU training. These layers were removed in the new alpha version of tensor flow to improve compatibility."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from tensorflow.compat.v1.keras.layers import CuDNNLSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BoJ24gB1eC23"
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "model = Sequential()\n",
    "model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))\n",
    "model.add(Dropout(rate=0.2))\n",
    "model.add(LSTM(256))\n",
    "model.add(Dropout(rate=0.2))\n",
    "model.add(Dense(y.shape[1], activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More data could produce memory erros so we have to create a data_generator function for the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qZQ6cWqt389J"
   },
   "outputs": [],
   "source": [
    "class KerasBatchGenerator(object):\n",
    "\n",
    "    def __init__(self, data, num_steps, batch_size, vocabulary, skip_step=1):\n",
    "        self.data = data\n",
    "        self.num_steps = num_steps\n",
    "        self.batch_size = batch_size\n",
    "        self.vocabulary = vocabulary\n",
    "        # this will track the progress of the batches sequentially through the\n",
    "        # data set - once the data reaches the end of the data set it will reset\n",
    "        # back to zero\n",
    "        self.current_idx = 0\n",
    "        # skip_step is the number of words which will be skipped before the next\n",
    "        # batch is skimmed from the data set\n",
    "        self.skip_step = skip_step\n",
    "        \n",
    "    def generate(self):\n",
    "        x = np.zeros((self.batch_size, self.num_steps, 1))\n",
    "        y = np.zeros((self.batch_size, self.vocabulary))\n",
    "        while True:\n",
    "            for i in range(self.batch_size):\n",
    "                if self.current_idx + self.num_steps >= len(self.data):\n",
    "                    # reset the index back to the start of the data set\n",
    "                    self.current_idx = 0\n",
    "                seq_in = self.data[self.current_idx:self.current_idx + self.num_steps]\n",
    "                x[i, :, 0] = np.array([char_to_int[char] for char in seq_in])/ float(n_vocab)\n",
    "                seq_out = self.data[self.current_idx + self.num_steps]\n",
    "                temp_y = char_to_int[seq_out]\n",
    "                # convert all of temp_y into a one hot representation\n",
    "                y[i, :] = np_utils.to_categorical(temp_y, num_classes=self.vocabulary)\n",
    "                self.current_idx += self.skip_step\n",
    "            yield x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mYDrqTnX3-ma"
   },
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "train_data_generator = KerasBatchGenerator(raw_text, seq_length, batch_size, n_vocab, skip_step=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66647, 60, 1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1137
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1034414,
     "status": "ok",
     "timestamp": 1555974566404,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "SHuCgh4_uDYY",
    "outputId": "12d7c041-da3e-4bb0-e5e9-2d7f7bcffa12"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "2083/2083 [==============================] - 28s 13ms/step - loss: 2.8154\n",
      "Epoch 2/40\n",
      "2083/2083 [==============================] - 23s 11ms/step - loss: 2.4619\n",
      "Epoch 3/40\n",
      "2083/2083 [==============================] - 24s 11ms/step - loss: 2.3297\n",
      "Epoch 4/40\n",
      "2083/2083 [==============================] - 32s 15ms/step - loss: 2.2317\n",
      "Epoch 5/40\n",
      "2083/2083 [==============================] - 32s 16ms/step - loss: 2.1439\n",
      "Epoch 6/40\n",
      "2083/2083 [==============================] - 32s 16ms/step - loss: 2.0725\n",
      "Epoch 7/40\n",
      "2083/2083 [==============================] - 41s 20ms/step - loss: 2.0042\n",
      "Epoch 8/40\n",
      "2083/2083 [==============================] - 35s 17ms/step - loss: 1.9463\n",
      "Epoch 9/40\n",
      "2083/2083 [==============================] - 34s 16ms/step - loss: 1.9100\n",
      "Epoch 10/40\n",
      "2083/2083 [==============================] - 32s 15ms/step - loss: 1.8445\n",
      "Epoch 11/40\n",
      "2083/2083 [==============================] - 31s 15ms/step - loss: 1.8024\n",
      "Epoch 12/40\n",
      "2083/2083 [==============================] - 32s 15ms/step - loss: 1.7589\n",
      "Epoch 13/40\n",
      "2083/2083 [==============================] - 24s 11ms/step - loss: 1.7182\n",
      "Epoch 14/40\n",
      "2083/2083 [==============================] - 26s 12ms/step - loss: 1.6768\n",
      "Epoch 15/40\n",
      "2083/2083 [==============================] - 23s 11ms/step - loss: 1.6426\n",
      "Epoch 16/40\n",
      "2083/2083 [==============================] - 23s 11ms/step - loss: 1.6070\n",
      "Epoch 17/40\n",
      "2083/2083 [==============================] - 27s 13ms/step - loss: 1.5736\n",
      "Epoch 18/40\n",
      "2083/2083 [==============================] - 27s 13ms/step - loss: 1.5389\n",
      "Epoch 19/40\n",
      "2083/2083 [==============================] - 29s 14ms/step - loss: 1.5151\n",
      "Epoch 20/40\n",
      "2083/2083 [==============================] - 28s 13ms/step - loss: 1.4838\n",
      "Epoch 21/40\n",
      "2083/2083 [==============================] - 24s 11ms/step - loss: 1.4597\n",
      "Epoch 22/40\n",
      "2083/2083 [==============================] - 28s 13ms/step - loss: 1.4324\n",
      "Epoch 23/40\n",
      "2083/2083 [==============================] - 34s 17ms/step - loss: 1.4076\n",
      "Epoch 24/40\n",
      "2083/2083 [==============================] - 36s 17ms/step - loss: 1.3821\n",
      "Epoch 25/40\n",
      "2083/2083 [==============================] - 37s 18ms/step - loss: 1.3634\n",
      "Epoch 26/40\n",
      "2083/2083 [==============================] - 31s 15ms/step - loss: 1.3366\n",
      "Epoch 27/40\n",
      "2083/2083 [==============================] - 30s 14ms/step - loss: 1.3185\n",
      "Epoch 28/40\n",
      "2083/2083 [==============================] - 28s 13ms/step - loss: 1.3004\n",
      "Epoch 29/40\n",
      "2083/2083 [==============================] - 29s 14ms/step - loss: 1.2830\n",
      "Epoch 30/40\n",
      "2083/2083 [==============================] - 30s 15ms/step - loss: 1.2651\n",
      "Epoch 31/40\n",
      "2083/2083 [==============================] - 21s 10ms/step - loss: 1.2459\n",
      "Epoch 32/40\n",
      "2083/2083 [==============================] - 26s 12ms/step - loss: 1.2310\n",
      "Epoch 33/40\n",
      "2083/2083 [==============================] - 23s 11ms/step - loss: 1.2149\n",
      "Epoch 34/40\n",
      "2083/2083 [==============================] - 23s 11ms/step - loss: 1.2034\n",
      "Epoch 35/40\n",
      "2083/2083 [==============================] - 26s 13ms/step - loss: 1.1897\n",
      "Epoch 36/40\n",
      "2083/2083 [==============================] - 26s 12ms/step - loss: 1.1827\n",
      "Epoch 37/40\n",
      "2083/2083 [==============================] - 22s 11ms/step - loss: 1.1742\n",
      "Epoch 38/40\n",
      "2083/2083 [==============================] - 25s 12ms/step - loss: 1.1543\n",
      "Epoch 39/40\n",
      "2083/2083 [==============================] - 28s 13ms/step - loss: 1.1388\n",
      "Epoch 40/40\n",
      "2083/2083 [==============================] - 29s 14ms/step - loss: 1.1336\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f603478a490>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#model.fit_generator(train_data_generator.generate(), epochs=30, steps_per_epoch=n_chars/batch_size)\n",
    "model.fit(X, y, epochs=40, batch_size=32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we saw in previous classes, the model trained using batch_input_shape requires a similar batch for validation, so in order to evaluate the model using a single sequence, we have to create a new model with a batch_size = 1 and pass on the learnt weights of the first model to the new one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U92_XqnRibev"
   },
   "outputs": [],
   "source": [
    "# re-define the batch size\n",
    "n_batch = 1\n",
    "# re-define model\n",
    "new_model = Sequential()\n",
    "new_model.add(LSTM(256, batch_input_shape=(n_batch, X.shape[1], X.shape[2]), return_sequences=True))\n",
    "new_model.add(Dropout(rate=0.2))\n",
    "new_model.add(LSTM(256))\n",
    "new_model.add(Dropout(rate=0.2))\n",
    "new_model.add(Dense(y.shape[1], activation='softmax'))\n",
    "# copy weights\n",
    "old_weights = model.get_weights()\n",
    "new_model.set_weights(old_weights)\n",
    "# compile model\n",
    "new_model.compile(loss='categorical_crossentropy', optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 202
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 4818,
     "status": "ok",
     "timestamp": 1555974762187,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "Lodj51rr7KB8",
    "outputId": "3e59171c-45c4-468f-f5df-ce833c993fa6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Seed:\n",
      "\" g, The soul of my son Shechem\n",
      "longeth for your daughter: I p \"\n",
      "ray thee, that I may eet iine thee, and they beait to thee.\n",
      "\n",
      "36:66 And the LORD said, The hath doendnted them fooo and dautle, and she father of the LORD that were aecanse of the dauth, 11:31 And he said, Iet uhe woman of God said, Gehold, the LORD hath done hero mege of thee.\n",
      "\n",
      "24:20 And they sook them ala baue uhe tors of Harah, and cate uo os mor shat were briad in the land of Cgypt.\n",
      "\n",
      "41:5 And they sook ao the land of Egypt, and the monet of the LORD. \n",
      "21:16 And they sook them aateh the sors of Zilrt, and the montt,\n",
      "and the crses of Egypt,\n",
      "and the koutt of the LORD.\n",
      "\n",
      "21:18 And they sook them ala totend and the tiod that has of his secr, and the LORD hath bllssed him\n",
      "ao the land of Cgypt.\n",
      "\n",
      "41:26 And they said onto the soos of Neme: Euhah s sin was cattle with him, and said, The sors of my son's moute, thet I may eet thell bome uoto to, that I may eet ii the min; and he said, I am the GORD heth mem io the land. \n",
      "27:23 And they said unto him, Thos seade Iacob and the morsi, thet shey hi\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "# pick a random seed\n",
    "start = np.random.randint(0, len(dataX)-1)\n",
    "pattern = dataX[start]\n",
    "print(\"Seed:\")\n",
    "print(\"\\\"\", ''.join([int_to_char[value] for value in pattern]), \"\\\"\")\n",
    "# generate characters\n",
    "for i in range(1000):\n",
    "    x = np.reshape(pattern, (1, seq_length, 1))\n",
    "    x = x / float(n_vocab)\n",
    "    prediction = new_model.predict(x, verbose=0)[0]\n",
    "    index = sample(prediction, 0.3)\n",
    "    result = int_to_char[index]\n",
    "    seq_in = [int_to_char[value] for value in pattern]\n",
    "    sys.stdout.write(result)\n",
    "    pattern.append(index)\n",
    "    pattern = pattern[1:len(pattern)]\n",
    "print(\"\\nDone.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1404,
     "status": "ok",
     "timestamp": 1555975635504,
     "user": {
      "displayName": "JULIAN DAVID ARIAS LONDOÑO",
      "photoUrl": "https://lh4.googleusercontent.com/-QOhj8seXZ4M/AAAAAAAAAAI/AAAAAAAAAKs/jFWlR3Fk460/s64/photo.jpg",
      "userId": "14990390101324121504"
     },
     "user_tz": 300
    },
    "id": "vR9a_xKFiJ2a",
    "outputId": "4175aea9-4942-479c-adde-cfd647cdedd1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved model to disk\n"
     ]
    }
   ],
   "source": [
    "# serialize model to JSON\n",
    "model_json = new_model.to_json()\n",
    "with open(\"modelgenCuDNNLSTM.json\", \"w\") as json_file:\n",
    "    json_file.write(model_json)\n",
    "# serialize weights to HDF5\n",
    "new_model.save_weights(\"modelgenCuDNNLSTM.h5\")\n",
    "print(\"Saved model to disk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HeZJho6ytwxd"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "TPU",
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   "name": "Textgeneration.ipynb",
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   "version": "0.3.2"
  },
  "kernelspec": {
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