Vanilla LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import randint\n",
    "from numpy import array\n",
    "from numpy import argmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_sequence(length, n_features):\n",
    "    return [randint(0,n_features-1) for _ in range(length)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encode(sequence, n_features):\n",
    "    encoding = list()\n",
    "    for value in sequence:\n",
    "        vector = [0 for _ in range(n_features)]\n",
    "        vector[value] = 1\n",
    "        encoding.append(vector)\n",
    "    return array(encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_decode(encoded_seq):\n",
    "    return [argmax(vector) for vector in encoded_seq]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence = generate_sequence(25,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[35, 60, 17, 43, 73, 7, 17, 84, 54, 28, 47, 50, 80, 69, 77, 27, 24, 82, 34, 61, 1, 41, 43, 74, 13]\n"
     ]
    }
   ],
   "source": [
    "print(sequence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded = one_hot_encode(sequence,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\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]]\n"
     ]
    }
   ],
   "source": [
    "print(encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoded = one_hot_decode(encoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[35, 60, 17, 43, 73, 7, 17, 84, 54, 28, 47, 50, 80, 69, 77, 27, 24, 82, 34, 61, 1, 41, 43, 74, 13]\n"
     ]
    }
   ],
   "source": [
    "print(decoded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_example(length,n_features, out_index):\n",
    "    sequence = generate_sequence(length, n_features)\n",
    "    encoded = one_hot_encode(sequence, n_features)\n",
    "    X=encoded.reshape((1,length,n_features))\n",
    "    y=encoded[out_index].reshape(1,n_features)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = generate_example(25,100,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 25, 100)\n",
      "(1, 100)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0810 21:34:51.390451 139723892582208 module_wrapper.py:136] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py:163: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "W0810 21:34:52.023689 139723892582208 module_wrapper.py:136] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/util/module_wrapper.py:163: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm_1 (LSTM)                (None, 25)                3600      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                260       \n",
      "=================================================================\n",
      "Total params: 3,860\n",
      "Trainable params: 3,860\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense\n",
    "length = 5\n",
    "n_features=10\n",
    "out_index=2\n",
    "model=Sequential()\n",
    "model.add(LSTM(25,input_shape=(length,n_features)))\n",
    "model.add(Dense(n_features,activation='softmax'))\n",
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['acc'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0810 21:34:56.243633 139723892582208 deprecation.py:323] From /home/arvind/.local/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1423: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      " - 1s - loss: 2.4083 - acc: 0.0000e+00\n",
      "Epoch 1/1\n",
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      "Epoch 1/1\n",
      " - 0s - loss: 0.5552 - acc: 1.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      " - 0s - loss: 0.0105 - acc: 1.0000\n",
      "Epoch 1/1\n",
      " - 0s - loss: 0.0102 - acc: 1.0000\n",
      "Epoch 1/1\n",
      " - 0s - loss: 0.0402 - acc: 1.0000\n",
      "Epoch 1/1\n",
      " - 0s - loss: 1.0596 - acc: 0.0000e+00\n",
      "Epoch 1/1\n",
      " - 0s - loss: 0.0623 - acc: 1.0000\n",
      "Epoch 1/1\n",
      " - 0s - loss: 0.0637 - acc: 1.0000\n",
      "Epoch 1/1\n",
      " - 0s - loss: 0.9765 - acc: 0.0000e+00\n"
     ]
    }
   ],
   "source": [
    "for i in range(10000):\n",
    "    X,y = generate_example(length,n_features,out_index)\n",
    "    model.fit(X,y,epochs=1,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 99.000000\n"
     ]
    }
   ],
   "source": [
    "correct=0\n",
    "for i in range(100):\n",
    "    X,y = generate_example(length, n_features, out_index)\n",
    "    yhat=model.predict(X)\n",
    "    if one_hot_decode(yhat) == one_hot_decode(y):\n",
    "        correct+=1\n",
    "print('Accuracy: %f' %((correct/100)*100.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequence: [[4, 6, 8, 3, 5]]\n",
      "Expected: [8]\n",
      "Predicted: [8]\n"
     ]
    }
   ],
   "source": [
    "X,y = generate_example(length,n_features,out_index)\n",
    "yhat = model.predict(X)\n",
    "print('Sequence: %s'%[one_hot_decode(x) for x in X])\n",
    "print('Expected: %s' % one_hot_decode(y))\n",
    "print('Predicted: %s' % one_hot_decode(yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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