Anexo II - Entendiendo_batch_en_stateful_RNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Idea original modificada: http://philipperemy.github.io/keras-stateful-lstm/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestante:\n",
    "\n",
    "https://machinelearningmastery.com/use-different-batch-sizes-training-predicting-python-keras/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generamos una secuencia artificial:\n",
    "- Cada muestra es una secuencia binaria de 20 bits. \n",
    "- Para X: El primer bit es es 1 con una probabilidad de 0.5, el resto son todos ceros\n",
    "- Para y: La etiqueta es 1 si el primer bit es 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "N_samples = 1200\n",
    "N_bits = 20\n",
    "from numpy.random import choice\n",
    "one_indexes = choice(a=N_samples, size=int(N_samples / 2), replace=False)\n",
    "X = np.zeros((N_samples, N_bits))\n",
    "X[one_indexes, 0] = 1  # very long term memory.\n",
    "y = X[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N_train = 1000\n",
    "X_train = X[:N_train]\n",
    "y_train = y[:N_train]\n",
    "X_validation = X[N_train:]\n",
    "y_validation = y[N_train:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entrada:  [ 0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.]\n",
      "Salida: 0.0\n"
     ]
    }
   ],
   "source": [
    "idx = 1\n",
    "print('Entrada: ',X_train[idx])\n",
    "print('Salida:', y_train[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training\n",
      "(1000, 20)\n",
      "(1000,)\n",
      "Validation\n",
      "(200, 20)\n",
      "(200,)\n"
     ]
    }
   ],
   "source": [
    "print('Training')\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print('Validation')\n",
    "print(X_validation.shape)\n",
    "print(y_validation.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prepare_sequences(x_train, y_train, window_length):\n",
    "    windows = []\n",
    "    windows_y = []\n",
    "    for i, sequence in enumerate(x_train):\n",
    "        len_seq = len(sequence)\n",
    "        for window_start in range(0, len_seq - window_length + 1):\n",
    "            window_end = window_start + window_length\n",
    "            window = sequence[window_start:window_end]\n",
    "            windows.append(window)\n",
    "            windows_y.append(y_train[i])\n",
    "    return np.array(windows), np.array(windows_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Que pasa si partimos la secuencia para entrenar?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probar:\n",
    "- window_length = 10, 15, 20\n",
    "- batch_size = 1, 50, 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "window_length = 20\n",
    "X_train_split_1, y_train_split_1 = prepare_sequences(X_train, y_train, window_length)\n",
    "X_validation_split_1, y_validation_split_1 = prepare_sequences(X_validation, y_validation, window_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.]\n",
      "1.0\n",
      "1.0\n",
      "(1000, 20)\n"
     ]
    }
   ],
   "source": [
    "# Empieza el nuevo vector en (N_bits - window_length + 1)\n",
    "next_vect_delta = N_bits - window_length + 1\n",
    "print(X_train_split_1[next_vect_delta])\n",
    "# Los y's son todos unos si el primer valor del vector fue 1\n",
    "print(y_train_split_1[next_vect_delta])\n",
    "print(y_train_split_1[next_vect_delta+1])\n",
    "print(X_train_split_1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1000 samples, validate on 200 samples\n",
      "Epoch 1/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6932 - acc: 0.5040 - val_loss: 0.6932 - val_acc: 0.4800\n",
      "Epoch 2/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6931 - acc: 0.5040 - val_loss: 0.6932 - val_acc: 0.4800\n",
      "Epoch 3/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6928 - acc: 0.5040 - val_loss: 0.6924 - val_acc: 0.4800\n",
      "Epoch 4/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6893 - acc: 0.6780 - val_loss: 0.6818 - val_acc: 1.0000\n",
      "Epoch 5/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.6391 - acc: 1.0000 - val_loss: 0.5311 - val_acc: 1.0000\n",
      "Epoch 6/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.3949 - acc: 1.0000 - val_loss: 0.2889 - val_acc: 1.0000\n",
      "Epoch 7/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.2526 - acc: 1.0000 - val_loss: 0.2128 - val_acc: 1.0000\n",
      "Epoch 8/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1953 - acc: 1.0000 - val_loss: 0.1703 - val_acc: 1.0000\n",
      "Epoch 9/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1593 - acc: 1.0000 - val_loss: 0.1409 - val_acc: 1.0000\n",
      "Epoch 10/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1332 - acc: 1.0000 - val_loss: 0.1188 - val_acc: 1.0000\n",
      "Epoch 11/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.1129 - acc: 1.0000 - val_loss: 0.1011 - val_acc: 1.0000\n",
      "Epoch 12/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.0961 - acc: 1.0000 - val_loss: 0.0860 - val_acc: 1.0000\n",
      "Epoch 13/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.0815 - acc: 1.0000 - val_loss: 0.0726 - val_acc: 1.0000\n",
      "Epoch 14/15\n",
      "1000/1000 [==============================] - 2s 2ms/step - loss: 0.0681 - acc: 1.0000 - val_loss: 0.0600 - val_acc: 1.0000\n",
      "Epoch 15/15\n",
      "1000/1000 [==============================] - 1s 1ms/step - loss: 0.0560 - acc: 1.0000 - val_loss: 0.0497 - val_acc: 1.0000\n",
      "200/200 [==============================] - 2s 12ms/step\n",
      "0.0497054220736 1.0\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import SimpleRNN, Dense, LSTM\n",
    "from keras.models import Sequential\n",
    "batch_size = 50\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(10, input_shape=(window_length, 1), return_sequences=False, stateful=False))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.fit(X_train_split_1.reshape(-1,window_length,1), y_train_split_1, batch_size=batch_size, epochs=15,\n",
    "          validation_data=(X_validation_split_1.reshape(-1,window_length,1), y_validation_split_1), shuffle=False)\n",
    "score, acc = model.evaluate(X_validation_split_1.reshape(-1,window_length,1), y_validation_split_1, batch_size=1, verbose=1)\n",
    "print(score, acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Como cambia la velocidad con los distintos tamaños de batch?\n",
    "- Estudiar tiempos de convergencia\n",
    "- Converge para todos los casos de longitud de la secuencia?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usando Batch_size de 1, stateful y window_lenght = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "window_length = 1\n",
    "X_train_split_2, y_train_split_2 = prepare_sequences(X_train, y_train, window_length)\n",
    "X_validation_split_2, y_validation_split_2 = prepare_sequences(X_validation, y_validation, window_length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 20000 samples, validate on 4000 samples\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 138s 7ms/step - loss: 0.6901 - acc: 0.5547 - val_loss: 0.6742 - val_acc: 0.5750\n",
      "4000/4000 [==============================] - 7s 2ms/step\n",
      "0.67417420378 0.575\n"
     ]
    }
   ],
   "source": [
    "batch_size = 1\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(10, batch_input_shape=(1, 1, 1), return_sequences=False, stateful=True))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "\n",
    "model.fit(X_train_split_2.reshape(-1,window_length,1), y_train_split_2, batch_size=batch_size, epochs=1,\n",
    "          validation_data=(X_validation_split_2.reshape(-1,window_length,1), y_validation_split_2), shuffle=False)\n",
    "score, acc = model.evaluate(X_validation_split_2.reshape(-1,window_length,1), y_validation_split_2, batch_size=1, verbose=1)\n",
    "print(score, acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Que pasa en este caso? Por que no llega a mejorar la accuracy?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reseteo despues de cada secuencia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 0.],\n",
       "       [ 0.],\n",
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       "       [ 0.],\n",
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      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# N_bits es 20, la longitud de la secuencia\n",
    "i = 0\n",
    "X_validation_split_2[i*N_bits:(i+1)*N_bits]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.6769 - acc: 0.9500     \n",
      "1\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.6407 - acc: 1.0000     \n",
      "2\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.5962 - acc: 1.0000     \n",
      "3\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.5309 - acc: 1.0000     \n",
      "4\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.9544 - acc: 0.0000e+00     \n",
      "5\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.8377 - acc: 0.0000e+00     \n",
      "6\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.5987 - acc: 1.0000     \n",
      "7\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.5743 - acc: 1.0000     \n",
      "8\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.8442 - acc: 0.0000e+00     \n",
      "9\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.7937 - acc: 0.0000e+00     \n",
      "10\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.6191 - acc: 1.0000     \n",
      "11\n",
      "Epoch 1/1\n",
      "20/20 [==============================] - 0s - loss: 0.5963 - acc: 1.0000     \n",
      "12\n",
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      "20/20 [==============================] - 0s - loss: 0.5553 - acc: 1.0000     \n",
      "13\n",
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      "20/20 [==============================] - 0s - loss: 0.4983 - acc: 1.0000     \n",
      "14\n",
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      "20/20 [==============================] - 0s - loss: 0.9708 - acc: 0.0000e+00     \n",
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      "20/20 [==============================] - 0s - loss: 0.8571 - acc: 0.0000e+00     \n",
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     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'ProgbarLogger' object has no attribute 'log_values'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-4e8a964472f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m     model.fit(X_train_split_2[i*N_bits:(i+1)*N_bits].reshape(-1,window_length,1), \n\u001b[0;32m---> 11\u001b[0;31m               y_train_split_2[i*N_bits:(i+1)*N_bits], batch_size=batch_size, epochs=1, shuffle=False, verbose=1)\n\u001b[0m\u001b[1;32m     12\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_states\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0mscore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_validation_split_2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwindow_length\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_validation_split_2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/mllab/lib/python3.6/site-packages/keras/models.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    861\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    862\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 863\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    864\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m~/anaconda3/envs/mllab/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m   1428\u001b[0m                               \u001b[0mval_f\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_f\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_ins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mval_ins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1429\u001b[0m                               \u001b[0mcallback_metrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallback_metrics\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1430\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1432\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/mllab/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)\u001b[0m\n\u001b[1;32m   1097\u001b[0m                         \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_outs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1098\u001b[0m                             \u001b[0mepoch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1099\u001b[0;31m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1100\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcallback_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1101\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/mllab/lib/python3.6/site-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0mlogs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m             \u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_epoch_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/mllab/lib/python3.6/site-packages/keras/callbacks.py\u001b[0m in \u001b[0;36mon_epoch_end\u001b[0;34m(self, epoch, logs)\u001b[0m\n\u001b[1;32m    307\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_values\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    308\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 309\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprogbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog_values\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    310\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'ProgbarLogger' object has no attribute 'log_values'"
     ]
    }
   ],
   "source": [
    "batch_size = 1\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(10, batch_input_shape=(1, 1, 1), return_sequences=False, stateful=True))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "for i in range(X_train_split_2.shape[0]):\n",
    "    print(i)\n",
    "    model.fit(X_train_split_2[i*N_bits:(i+1)*N_bits].reshape(-1,window_length,1), \n",
    "              y_train_split_2[i*N_bits:(i+1)*N_bits], batch_size=batch_size, epochs=1, shuffle=False, verbose=1)\n",
    "    model.reset_states()\n",
    "score, acc = model.evaluate(X_validation_split_2.reshape(-1,window_length,1), y_validation_split_2, batch_size=1, verbose=1)\n",
    "print(score, acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch size = 1000 y reshape del dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20000 20.0\n",
      "(20000, 1)\n",
      "(20000, 1, 1)\n"
     ]
    }
   ],
   "source": [
    "batch_size = 1000\n",
    "X_train_split, y_train_split = prepare_sequences(X_train, y_train, 1)\n",
    "N = len(X_train_split)\n",
    "frac = N/batch_size\n",
    "print(N, frac)\n",
    "X_train_split_reshaped = X_train_split.reshape(batch_size,int(frac)).T.reshape(N,1,1)\n",
    "y_train_split_reshaped = y_train_split.reshape(batch_size,int(frac)).T.reshape(N)\n",
    "print(X_train_split.shape)\n",
    "print(X_train_split_reshaped.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.layers import SimpleRNN, Dense, LSTM\n",
    "from keras.models import Sequential\n",
    "model = Sequential()\n",
    "model.add(LSTM(10, batch_input_shape=(batch_size, 1, 1), return_sequences=False, stateful=True))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 1s 34us/step - loss: 0.6904 - acc: 0.6605\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 8us/step - loss: 0.6888 - acc: 0.5635\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 7us/step - loss: 0.6872 - acc: 0.5635\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 9us/step - loss: 0.6849 - acc: 0.6120\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 8us/step - loss: 0.6807 - acc: 0.6362\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 8us/step - loss: 0.6707 - acc: 0.7090\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 8us/step - loss: 0.6360 - acc: 0.9030\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 8us/step - loss: 0.5109 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 9us/step - loss: 0.3562 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 9us/step - loss: 0.2666 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 9us/step - loss: 0.2112 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 10us/step - loss: 0.1793 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 8us/step - loss: 0.1570 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 7us/step - loss: 0.1402 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 7us/step - loss: 0.1270 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 8us/step - loss: 0.1161 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 7us/step - loss: 0.1069 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 7us/step - loss: 0.0992 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 8us/step - loss: 0.0927 - acc: 1.0000\n",
      "Epoch 1/1\n",
      "20000/20000 [==============================] - 0s 7us/step - loss: 0.0867 - acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "for i in range(N_bits):\n",
    "    model.fit(X_train_split_reshaped, y_train_split_reshaped, epochs=1, batch_size=batch_size, verbose=1, shuffle=False)\n",
    "    model.reset_states()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Para poder evaluar tengo que recargar el modelo con batch_size=200 para que sea eficiente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_val_split, y_val_split = prepare_sequences(X_validation, y_validation, 1)\n",
    "X_val_split_reshaped = X_val_split.reshape(200,20).T.reshape(4000,1,1)\n",
    "y_val_split_reshaped = y_val_split.reshape(200,20).T.reshape(4000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_model = Sequential()\n",
    "new_model.add(LSTM(10, batch_input_shape=(200, 1, 1), return_sequences=False, stateful=True))\n",
    "new_model.add(Dense(1, activation='sigmoid'))\n",
    "new_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "old_weights = model.get_weights()\n",
    "new_model.set_weights(old_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4000/4000 [==============================] - 0s 48us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.10879546809010207, 1.0]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_model.evaluate(X_val_split_reshaped, y_val_split_reshaped, batch_size=200, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch size = 100\n",
    "\n",
    "Hay que tomar de a 100 del total de 1000, hacer un reshape a (100,20) y luego trasponer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_split, y_train_split = prepare_sequences(X_train, y_train, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  1., ...,  0.,  1.,  1.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Asi es como veria los datos el modelo de training\n",
    "X_train_split.reshape(1000,20)[:100].T #.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_split.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_split_reshaped = np.zeros((X_train_split.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "model = Sequential()\n",
    "model.add(LSTM(10, batch_input_shape=(batch_size, 1, 1), return_sequences=False, stateful=True))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 1s - loss: 0.6914 - acc: 0.5535     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6893 - acc: 0.5300     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6974 - acc: 0.4300     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6889 - acc: 0.5650     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6907 - acc: 0.5140     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6866 - acc: 0.7360     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6844 - acc: 0.7305     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6774 - acc: 0.7060     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6853 - acc: 0.5230     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6682 - acc: 1.0000     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.6448 - acc: 0.6240     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.5710 - acc: 0.5770     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.3937 - acc: 1.0000     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.3577 - acc: 1.0000     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.2023 - acc: 1.0000     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.1869 - acc: 1.0000     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.1420 - acc: 1.0000     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.1262 - acc: 1.0000     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.1141 - acc: 1.0000     \n",
      "Epoch 1/1\n",
      "2000/2000 [==============================] - 0s - loss: 0.1037 - acc: 1.0000     \n"
     ]
    }
   ],
   "source": [
    "n_batches = int(len(X_train_split_reshaped)/batch_size)\n",
    "print(n_batches)\n",
    "for k in range(20):\n",
    "    i = k%10\n",
    "    X_to_train = (X_train_split.reshape(1000,20)[i*batch_size:(i+1)*batch_size].T.flatten()).reshape(2000,1,1)\n",
    "    y_to_train = y_train_split.reshape(1000,20)[i*batch_size:(i+1)*batch_size].T.flatten()\n",
    "    model.fit(X_to_train, y_to_train, epochs=1, batch_size=batch_size, verbose=1, shuffle=False)\n",
    "    model.reset_states()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 200/4000 [>.............................] - ETA: 12s"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.096351571194827554, 1.0]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val_split, y_val_split = prepare_sequences(X_validation, y_validation, 1)\n",
    "X_val_split_reshaped = X_val_split.reshape(200,20).T.reshape(4000,1,1)\n",
    "y_val_split_reshaped = y_val_split.reshape(200,20).T.reshape(4000)\n",
    "\n",
    "new_model = Sequential()\n",
    "new_model.add(LSTM(10, batch_input_shape=(200, 1, 1), return_sequences=False, stateful=True))\n",
    "new_model.add(Dense(1, activation='sigmoid'))\n",
    "new_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "old_weights = model.get_weights()\n",
    "new_model.set_weights(old_weights)\n",
    "\n",
    "new_model.evaluate(X_val_split_reshaped, y_val_split_reshaped, batch_size=200, verbose=1)"
   ]
  }
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