Bidirectional-Stacked-LSTM.ipynb

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   "id": "6b9f4094-a7e7-4ec6-beb6-0333318abe34",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow\n",
    "from tensorflow.keras.layers import Embedding, Bidirectional, Dense, LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c7ca30a1-95ca-4571-8733-f13ddc998aa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = tensorflow.keras.Input(shape=(None,), dtype=\"int32\")   # Input for variable length sequences of integers\n",
    "\n",
    "x = Embedding(20000, 128)(inputs)\n",
    "x = Bidirectional(LSTM(64, return_sequences=True))(x)\n",
    "x = Bidirectional(LSTM(64))(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fe1f1d82-44a8-45ec-9258-a192e37e3010",
   "metadata": {},
   "outputs": [
    {
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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: \"functional_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_1\"\u001b[0m\n"
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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\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
       "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,560,000</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ bidirectional (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ bidirectional_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Bidirectional</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">98,816</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\">129</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",
       "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │     \u001b[38;5;34m2,560,000\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ bidirectional (\u001b[38;5;33mBidirectional\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m98,816\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ bidirectional_1 (\u001b[38;5;33mBidirectional\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m98,816\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;34m129\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
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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\">2,757,761</span> (10.52 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,757,761\u001b[0m (10.52 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,757,761</span> (10.52 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,757,761\u001b[0m (10.52 MB)\n"
      ]
     },
     "metadata": {},
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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\"> 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": [
    "# Add a classifier\n",
    "outputs = Dense(1, activation=\"sigmoid\")(x)\n",
    "model = tensorflow.keras.Model(inputs, outputs)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11e67b06-39f2-4288-b4a3-3e40d6dcb716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25000 Training sequences\n",
      "25000 Validation sequences\n"
     ]
    }
   ],
   "source": [
    "(x_train, y_train), (x_val, y_val) = tensorflow.keras.datasets.imdb.load_data(\n",
    "    num_words=20000\n",
    ")\n",
    "print(len(x_train), \"Training sequences\")\n",
    "print(len(x_val), \"Validation sequences\")\n",
    "# Use pad_sequence to standardize sequence length:\n",
    "# this will truncate sequences longer than 200 words and zero-pad sequences shorter than 200 words.\n",
    "x_train = tensorflow.keras.utils.pad_sequences(x_train, maxlen=200)\n",
    "x_val = tensorflow.keras.utils.pad_sequences(x_val, maxlen=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2c0401d1-8e67-4c11-85c6-1f0a005ef7bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 236ms/step - accuracy: 0.7442 - loss: 0.4983 - val_accuracy: 0.8301 - val_loss: 0.3755\n",
      "Epoch 2/2\n",
      "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 260ms/step - accuracy: 0.9014 - loss: 0.2624 - val_accuracy: 0.8672 - val_loss: 0.3171\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x20cb79b4390>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])\n",
    "model.fit(x_train, y_train, batch_size=32, epochs=2, validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "39d6513f-628a-400c-830e-16747b914f1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter your text:  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 79ms/step\n",
      "[[0.6378386]]\n",
      "Predicted sentiment: Positive\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.text import Tokenizer\n",
    "#from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "tokenizer = Tokenizer(num_words=5000)\n",
    "\n",
    "user_input = input(\"Enter your text: \")\n",
    "\n",
    "tokenizer.fit_on_texts([user_input])\n",
    "tokenized_input = tokenizer.texts_to_sequences([user_input])\n",
    "\n",
    "padded_input = tensorflow.keras.utils.pad_sequences(tokenized_input, maxlen=200, padding='post', truncating='post', value=0.0)\n",
    "\n",
    "predictions = model.predict(padded_input)\n",
    "\n",
    "predicted_class = (predictions[0][0] > 0.5).astype(\"int32\")\n",
    "\n",
    "print(predictions)\n",
    "if predicted_class == 1:\n",
    "    print(\"Predicted sentiment: Positive\")\n",
    "else:\n",
    "    print(\"Predicted sentiment: Negative\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b79f4237-ceb9-4820-bdbc-f920e4fd2f80",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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