neu_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "444edfe6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
      "17464789/17464789 [==============================] - 24s 1us/step\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import imdb\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
    " num_words=10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8520f46e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "79f13261",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
      "1641221/1641221 [==============================] - 2s 1us/step\n"
     ]
    }
   ],
   "source": [
    "word_index = imdb.get_word_index() \n",
    "reverse_word_index = dict(\n",
    " [(value, key) for (key, value) in word_index.items()])\n",
    "decoded_review = \" \".join(\n",
    " [reverse_word_index.get(i - 3, \"?\") for i in train_data[0]]) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8dbb073c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88584"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(reverse_word_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6d3a871c",
   "metadata": {},
   "outputs": [],
   "source": [
    "decoded_review = \" \".join(\n",
    " [reverse_word_index.get(i-3, \"?\") for i in train_data[0]]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4aeee6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "\n",
    "def vectorize_sequences(sequences, dimension=10000): \n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        for j in sequence:\n",
    "            results[i, j] = 1.       \n",
    "    return results\n",
    "x_train = vectorize_sequences(train_data) \n",
    "x_test = vectorize_sequences(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d8ed384f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  6., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  7., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  2., ...,  0.,  0.,  0.],\n",
       "       ...,\n",
       "       [ 0.,  1., 14., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  5., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  1., 12., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "9e076a43",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = np.asarray(train_labels).astype(\"float32\")\n",
    "y_test = np.asarray(test_labels).astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5a2c00c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, ..., 0, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "ccc161c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras \n",
    "from tensorflow.keras import layers\n",
    "model = keras.Sequential([\n",
    " layers.Dense(16, activation=\"relu\"),\n",
    " layers.Dense(16, activation=\"relu\"),\n",
    " layers.Dense(1, activation=\"sigmoid\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "15af821e",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=\"rmsprop\",\n",
    " loss=\"binary_crossentropy\",\n",
    " metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7ae4d4db",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_val = x_train[:10000]\n",
    "partial_x_train = x_train[10000:]\n",
    "y_val = y_train[:10000]\n",
    "partial_y_train = y_train[10000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d3d426da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "30/30 [==============================] - 4s 81ms/step - loss: 0.5696 - accuracy: 0.7729 - val_loss: 0.4520 - val_accuracy: 0.8581\n",
      "Epoch 2/20\n",
      "30/30 [==============================] - 1s 40ms/step - loss: 0.3773 - accuracy: 0.8815 - val_loss: 0.3540 - val_accuracy: 0.8702\n",
      "Epoch 3/20\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.2850 - accuracy: 0.9051 - val_loss: 0.3013 - val_accuracy: 0.8853\n",
      "Epoch 4/20\n",
      "30/30 [==============================] - 1s 35ms/step - loss: 0.2308 - accuracy: 0.9209 - val_loss: 0.2821 - val_accuracy: 0.8873\n",
      "Epoch 5/20\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1957 - accuracy: 0.9317 - val_loss: 0.2758 - val_accuracy: 0.8881\n",
      "Epoch 6/20\n",
      "30/30 [==============================] - 1s 39ms/step - loss: 0.1675 - accuracy: 0.9450 - val_loss: 0.2779 - val_accuracy: 0.8859\n",
      "Epoch 7/20\n",
      "30/30 [==============================] - 1s 40ms/step - loss: 0.1464 - accuracy: 0.9523 - val_loss: 0.2860 - val_accuracy: 0.8832\n",
      "Epoch 8/20\n",
      "30/30 [==============================] - 1s 42ms/step - loss: 0.1279 - accuracy: 0.9614 - val_loss: 0.3151 - val_accuracy: 0.8755\n",
      "Epoch 9/20\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.1122 - accuracy: 0.9659 - val_loss: 0.3000 - val_accuracy: 0.8850\n",
      "Epoch 10/20\n",
      "30/30 [==============================] - 1s 41ms/step - loss: 0.0983 - accuracy: 0.9721 - val_loss: 0.3233 - val_accuracy: 0.8759\n",
      "Epoch 11/20\n",
      "30/30 [==============================] - 1s 36ms/step - loss: 0.0850 - accuracy: 0.9755 - val_loss: 0.3475 - val_accuracy: 0.8778\n",
      "Epoch 12/20\n",
      "30/30 [==============================] - 1s 35ms/step - loss: 0.0754 - accuracy: 0.9794 - val_loss: 0.3445 - val_accuracy: 0.8802\n",
      "Epoch 13/20\n",
      "30/30 [==============================] - 1s 31ms/step - loss: 0.0634 - accuracy: 0.9853 - val_loss: 0.4302 - val_accuracy: 0.8660\n",
      "Epoch 14/20\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.0575 - accuracy: 0.9857 - val_loss: 0.3780 - val_accuracy: 0.8777\n",
      "Epoch 15/20\n",
      "30/30 [==============================] - 1s 29ms/step - loss: 0.0499 - accuracy: 0.9887 - val_loss: 0.3953 - val_accuracy: 0.8768\n",
      "Epoch 16/20\n",
      "30/30 [==============================] - 1s 35ms/step - loss: 0.0416 - accuracy: 0.9920 - val_loss: 0.4702 - val_accuracy: 0.8614\n",
      "Epoch 17/20\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0386 - accuracy: 0.9919 - val_loss: 0.4356 - val_accuracy: 0.8750\n",
      "Epoch 18/20\n",
      "30/30 [==============================] - 1s 37ms/step - loss: 0.0296 - accuracy: 0.9955 - val_loss: 0.4556 - val_accuracy: 0.8744\n",
      "Epoch 19/20\n",
      "30/30 [==============================] - 1s 38ms/step - loss: 0.0257 - accuracy: 0.9966 - val_loss: 0.4913 - val_accuracy: 0.8682\n",
      "Epoch 20/20\n",
      "30/30 [==============================] - 1s 42ms/step - loss: 0.0238 - accuracy: 0.9969 - val_loss: 0.5253 - val_accuracy: 0.8650\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(partial_x_train,\n",
    " partial_y_train,\n",
    " epochs=20,\n",
    " batch_size=512,\n",
    " validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0d4f6735",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history_dict = history.history\n",
    "history_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d7778cf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "history_dict = history.history\n",
    "loss_values = history_dict[\"loss\"]\n",
    "val_loss_values = history_dict[\"val_loss\"]\n",
    "epochs = range(1, len(loss_values) + 1)\n",
    "plt.plot(epochs, loss_values, \"bo\", label=\"Training loss\")\n",
    "plt.plot(epochs, val_loss_values, \"b\", label=\"Validation loss\") \n",
    "plt.title(\"Training and validation loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "460d55ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history_dict[\"accuracy\"]\n",
    "val_acc = history_dict[\"val_accuracy\"]\n",
    "plt.plot(epochs, acc, \"bo\", label=\"Training acc\")\n",
    "plt.plot(epochs, val_acc, \"b\", label=\"Validation acc\")\n",
    "plt.title(\"Training and validation accuracy\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b3881a19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "49/49 [==============================] - 2s 27ms/step - loss: 0.4770 - accuracy: 0.8089\n",
      "Epoch 2/4\n",
      "49/49 [==============================] - 2s 31ms/step - loss: 0.2797 - accuracy: 0.9040\n",
      "Epoch 3/4\n",
      "49/49 [==============================] - 1s 30ms/step - loss: 0.2164 - accuracy: 0.9208\n",
      "Epoch 4/4\n",
      "49/49 [==============================] - 1s 23ms/step - loss: 0.1857 - accuracy: 0.9344\n",
      "782/782 [==============================] - 3s 3ms/step - loss: 0.2838 - accuracy: 0.8846\n"
     ]
    }
   ],
   "source": [
    "model1 = keras.Sequential([\n",
    " layers.Dense(16, activation=\"relu\"),\n",
    " layers.Dense(16, activation=\"relu\"),\n",
    " layers.Dense(1, activation=\"sigmoid\")\n",
    "])\n",
    "model1.compile(optimizer=\"rmsprop\",\n",
    " loss=\"binary_crossentropy\",\n",
    " metrics=[\"accuracy\"])\n",
    "model1.fit(x_train, y_train, epochs=4, batch_size=512)\n",
    "results = model1.evaluate(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1758fda3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "782/782 [==============================] - 2s 3ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[False],\n",
       "       [ True],\n",
       "       [ True],\n",
       "       ...,\n",
       "       [False],\n",
       "       [False],\n",
       "       [ True]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.predict(x_test)>0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a26e011b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded Sentence: tf.Tensor([2 9 7 6], shape=(4,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import TextVectorization\n",
    "\n",
    "# Create the TextVectorization layer\n",
    "text_vectorization = TextVectorization(output_mode=\"int\", standardize=\"lower_and_strip_punctuation\", max_tokens=1000)\n",
    "\n",
    "# Example dataset\n",
    "dataset = [\n",
    "    \"The quick brown fox jumps over the lazy dog!\",\n",
    "    \"THE QUICK BROWN FOX JUMPS OVER THE LAZY DOG.\"\n",
    "]\n",
    "\n",
    "# Adapt the TextVectorization layer to the dataset\n",
    "text_vectorization.adapt(dataset)\n",
    "\n",
    "# Transform a sentence using the TextVectorization layer\n",
    "input_sentence = \"The brown fox jumps\"\n",
    "encoded_sentence = text_vectorization(input_sentence)\n",
    "print(\"Encoded Sentence:\", encoded_sentence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "98fc97b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded Sentence: tf.Tensor([2 3 9 7 6 4 2 5 8], shape=(9,), dtype=int64)\n"
     ]
    }
   ],
   "source": [
    "input_sentence = \"The quick brown fox jumps over the lzy dog!\"\n",
    "encoded_sentence = text_vectorization(input_sentence)\n",
    "print(\"Encoded Sentence:\", encoded_sentence)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97ec98c9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}