Single-layer-LSTM.ipynb

{
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   "cell_type": "code",
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   "id": "7233e664",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b06a120f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf                             # Tensorflow for building and training neural network models\n",
    "from tensorflow.keras.datasets import imdb          # IMDb movie review dataset - movie reviews and sentiment labels\n",
    "from tensorflow.keras.layers import Dense, Embedding, LSTM\n",
    "from tensorflow.keras.models import Sequential      # Linear stack of layers for building neural network\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import BinaryCrossentropy    # Calculate difference between predicted and actual values in binary classification\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences   # Pad sequences to a specified length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99562094",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_output_dimensions = 15    # Size of vector space in which words will be embedded\n",
    "# Define the maximum input length\n",
    "max_input_length = 500\n",
    "\n",
    "# Define the number of words in the vocabulary\n",
    "vocabulary_size = 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6f82d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=5000)   # Restrict the vocabulary size to 5000 words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f6120d60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pad all sequences\n",
    "padded_inputs = pad_sequences(x_train, maxlen=500, value = 0.0) # Pads sequences to maximum length with padding value 0\n",
    "padded_inputs_test = pad_sequences(x_test, maxlen=500, value = 0.0) # Pads sequences to maximum length with padding value 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "abae93b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Keras model\n",
    "model = Sequential()\n",
    "model.add(Embedding(vocabulary_size, embedding_output_dimensions, input_shape = (500,) ))  # Embedding - converts integer-encoded words into dense vectors of fixed size\n",
    "model.add(LSTM(10))    # LSTM layer with 10 units\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "04fbe384",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer=Adam(), loss=BinaryCrossentropy(), metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5755fb09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_5\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_5\"\u001b[0m\n"
      ]
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">75,000</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,040</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ embedding_4 (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m, \u001b[38;5;34m15\u001b[0m)        │        \u001b[38;5;34m75,000\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,040\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m11\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">76,051</span> (297.07 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m76,051\u001b[0m (297.07 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">76,051</span> (297.07 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m76,051\u001b[0m (297.07 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Give a summary of model architecture\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5d34db39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 76ms/step - accuracy: 0.5983 - loss: 0.6586 - val_accuracy: 0.8110 - val_loss: 0.4545\n",
      "Epoch 2/5\n",
      "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 94ms/step - accuracy: 0.8429 - loss: 0.4013 - val_accuracy: 0.8466 - val_loss: 0.3718\n",
      "Epoch 3/5\n",
      "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 99ms/step - accuracy: 0.8768 - loss: 0.3227 - val_accuracy: 0.7864 - val_loss: 0.4680\n",
      "Epoch 4/5\n",
      "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 99ms/step - accuracy: 0.8696 - loss: 0.3327 - val_accuracy: 0.8616 - val_loss: 0.3444\n",
      "Epoch 5/5\n",
      "\u001b[1m157/157\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 101ms/step - accuracy: 0.9049 - loss: 0.2593 - val_accuracy: 0.8580 - val_loss: 0.3342\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "history = model.fit(padded_inputs, y_train, batch_size=128, epochs=5, verbose=1, validation_split=0.2)\n",
    "\n",
    "# Verbose: It can be 0(Silent), 1(Progress bar), 2(Epoch number, loss and metrics in one line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a7c54b05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 12ms/step - accuracy: 0.8608 - loss: 0.3415\n",
      "Test results - Loss: 0.3380950689315796 - Accuracy: 86.26400232315063%\n"
     ]
    }
   ],
   "source": [
    "# Test the model after training\n",
    "test_results = model.evaluate(padded_inputs_test, y_test, verbose=1)\n",
    "print(f'Test results - Loss: {test_results[0]} - Accuracy: {100*test_results[1]}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "42078603-636d-4d08-a5bb-220fdf30500e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.text import Tokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "536795bf-084a-420b-bcbe-cfe8727a4535",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = Tokenizer(num_words = 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7079dae1-bee4-441d-884a-5ff0df352ab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter your text:  the movie is good\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step\n",
      "[[0.3770659]]\n"
     ]
    }
   ],
   "source": [
    "user_input = input(\"Enter your text: \")\n",
    "tokenizer.fit_on_texts([user_input])\n",
    "tokenized_input = tokenizer.texts_to_sequences([user_input])\n",
    "padded_input = pad_sequences(tokenized_input, maxlen=500, padding='post', truncating='post', value=0.0)\n",
    "predictions = model.predict(padded_input)\n",
    "\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2528c374-a5fe-409a-b179-67eab53e7bb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "Predicted sentiment: Positive\n"
     ]
    }
   ],
   "source": [
    "predicted_class = (predictions[0][0] > 0.5).astype(\"int32\")\n",
    "\n",
    "print(predicted_class)\n",
    "if predicted_class == 0:\n",
    "    print(\"Predicted sentiment: Positive\")\n",
    "else:\n",
    "    print(\"Predicted sentiment: Negative\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d294d3f7-1594-4361-8219-1c912b7c89a5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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