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[Ch4Nb05] Installed dependencies using ch4-requirements.txt
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‎Ch4/05_DeepNN_Example.ipynb‎

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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "T4DNH1aoeEhE",
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"outputId": "5304f679-8132-4cc3-c50a-c73502c7d44c"
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"id": "eOJLveJqtEO3",
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"outputId": "067a74b2-c5df-464d-a3fa-3f4517a9090a"
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},
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"source": [
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"!pip install wget"
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"# To install only the requirements of this notebook, uncomment the lines below and run this cell\n",
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"\n",
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"# ===========================\n",
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"\n",
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"!pip install numpy==1.19.5\n",
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"!pip install wget==3.2\n",
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"!pip install tensorflow==1.14.0\n",
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"\n",
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"# ==========================="
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Collecting wget\n",
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"Requirement already satisfied: numpy==1.19.5 in /usr/local/lib/python3.7/dist-packages (1.19.5)\n",
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"Collecting wget==3.2\n",
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" Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n",
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"Building wheels for collected packages: wget\n",
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" Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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" Created wheel for wget: filename=wget-3.2-cp37-none-any.whl size=9675 sha256=ff413eaabaf2d63367837f0f054d0f5b2518684987287143f3db7927f5fdd75e\n",
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" Created wheel for wget: filename=wget-3.2-cp37-none-any.whl size=9675 sha256=0590de33e3a5654cc81a0a21cf66fa3e8af32bf31e65c5a543d101b6d3fba858\n",
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" Stored in directory: /root/.cache/pip/wheels/40/15/30/7d8f7cea2902b4db79e3fea550d7d7b85ecb27ef992b618f3f\n",
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"Successfully built wget\n",
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"Installing collected packages: wget\n",
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"Successfully installed wget-3.2\n"
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"Successfully installed wget-3.2\n",
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"Collecting tensorflow==1.14.0\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/f4/28/96efba1a516cdacc2e2d6d081f699c001d414cc8ca3250e6d59ae657eb2b/tensorflow-1.14.0-cp37-cp37m-manylinux1_x86_64.whl (109.3MB)\n",
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"\u001b[K |████████████████████████████████| 109.3MB 104kB/s \n",
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"\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.12.1)\n",
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"Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.34.1)\n",
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"Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488kB)\n",
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"\u001b[K |████████████████████████████████| 491kB 49.6MB/s \n",
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"\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
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"Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.19.5)\n",
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"Collecting tensorboard<1.15.0,>=1.14.0\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1MB)\n",
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"\u001b[K |████████████████████████████████| 3.2MB 33.6MB/s \n",
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"\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.1.2)\n",
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"Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.8.1)\n",
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"Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.2.0)\n",
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"Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.12.0)\n",
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"Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.15.0)\n",
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"Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (3.17.3)\n",
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"Collecting keras-applications>=1.0.6\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
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"\u001b[K |████████████████████████████████| 51kB 8.4MB/s \n",
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"\u001b[?25hRequirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.4.0)\n",
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"Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.36.2)\n",
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"Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (57.0.0)\n",
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"Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.3.4)\n",
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"Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (1.0.1)\n",
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"Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras-applications>=1.0.6->tensorflow==1.14.0) (3.1.0)\n",
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"Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (4.6.1)\n",
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"Requirement already satisfied: cached-property; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications>=1.0.6->tensorflow==1.14.0) (1.5.2)\n",
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"Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.5.0)\n",
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"Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.7.4.3)\n",
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"\u001b[31mERROR: kapre 0.3.5 has requirement tensorflow>=2.0.0, but you'll have tensorflow 1.14.0 which is incompatible.\u001b[0m\n",
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"Installing collected packages: tensorflow-estimator, tensorboard, keras-applications, tensorflow\n",
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" Found existing installation: tensorflow-estimator 2.5.0\n",
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" Uninstalling tensorflow-estimator-2.5.0:\n",
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" Successfully uninstalled tensorflow-estimator-2.5.0\n",
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" Found existing installation: tensorboard 2.5.0\n",
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" Uninstalling tensorboard-2.5.0:\n",
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" Successfully uninstalled tensorboard-2.5.0\n",
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" Found existing installation: tensorflow 2.5.0\n",
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" Uninstalling tensorflow-2.5.0:\n",
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" Successfully uninstalled tensorflow-2.5.0\n",
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"Successfully installed keras-applications-1.0.8 tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "Ixb_5zcYtEO5"
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},
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"source": [
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"# To install the requirements for the entire chapter, uncomment the lines below and run this cell\n",
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"\n",
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"# ===========================\n",
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"\n",
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"# try:\n",
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"# import google.colab\n",
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"# !curl https://raw.githubusercontent.com/practical-nlp/practical-nlp/master/Ch4/ch4-requirements.txt | xargs -n 1 -L 1 pip install\n",
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"# except ModuleNotFoundError:\n",
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"# !pip install -r \"ch4-requirements.txt\"\n",
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"\n",
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"# ==========================="
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],
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"execution_count": 2,
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"outputs": []
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},
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{
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"cell_type": "code",
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"metadata": {
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"from tensorflow.keras.models import Model, Sequential\n",
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"from tensorflow.keras.initializers import Constant"
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],
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"execution_count": 2,
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"execution_count": 3,
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"outputs": []
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},
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{
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"except ModuleNotFoundError:\n",
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" \n",
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" if not os.path.exists('Data/glove.6B'):\n",
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" os.mkdir('/Data/glove.6B')\n",
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" os.mkdir('Data/glove.6B')\n",
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" \n",
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" url='http://nlp.stanford.edu/data/glove.6B.zip' \n",
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" wget.download(url,'Data') \n",
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" if not os.path.exists('Data/aclImdb'):\n",
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" \n",
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" url='http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz' \n",
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" wget.download(url,path)\n",
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" wget.download(url,'Data')\n",
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" \n",
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" temp='Data/aclImdb_v1.tar.gz' \n",
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" tar = tarfile.open(temp, \"r:gz\")\n",
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" tar.extractall(path) \n",
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" tar.extractall('Data') \n",
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" tar.close()\n",
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" \n",
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" BASE_DIR = 'Data'"
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],
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},
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"TRAIN_DATA_DIR = os.path.join(BASE_DIR, 'aclImdb/train')\n",
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"TEST_DATA_DIR = os.path.join(BASE_DIR, 'aclImdb/test')"
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],
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"outputs": []
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{
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"#started off from: https://github.com/keras-team/keras/blob/master/examples/pretrained_word_embeddings.py\n",
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"#and from: https://github.com/keras-team/keras/blob/master/examples/imdb_lstm.py"
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],
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"execution_count": 6,
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"outputs": []
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"#print(test_texts[24999])\n",
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"#print(test_labels[24999])"
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"execution_count": 7,
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"outputs": []
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"base_uri": "https://localhost:8080/"
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"id": "QhhqM0Jdd7fs",
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"outputId": "87fa1f87-4a45-4921-b04f-3cec24e6a0c3"
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"outputId": "9b5b394e-bc52-4779-d85d-a0383446051d"
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"#Vectorize these text samples into a 2D integer tensor using Keras Tokenizer \n",
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"word_index = tokenizer.word_index \n",
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"print('Found %s unique tokens.' % len(word_index))"
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"base_uri": "https://localhost:8080/"
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},
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"id": "_e0V1-bBb5_d",
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"outputId": "349db20b-de34-450a-8053-be6aef52f790"
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"outputId": "d866429d-5bb6-43a7-c66e-ed5abbafc4cd"
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"source": [
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"#Converting this to sequences to be fed into neural network. Max seq. len is 1000 as set earlier\n",
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"#This is the data we will use for CNN and RNN training\n",
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"print('Splitting the train data into train and valid is done')"
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"base_uri": "https://localhost:8080/"
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"outputId": "295331a8-15a4-45de-9177-a88478639ff2"
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"outputId": "8387eda1-18f0-4254-9819-e63191b8fc04"
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"source": [
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"print('Preparing embedding matrix.')\n",
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" trainable=False)\n",
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"print(\"Preparing of embedding matrix is done\")"
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"base_uri": "https://localhost:8080/"
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},
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"id": "TTY-4K-Ob5_t",
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"outputId": "3f795778-06c6-41c5-a3f4-f2273016b020"
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"outputId": "836681ca-936e-400a-8973-0754759bb7cd"
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},
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"source": [
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"print('Define a 1D CNN model.')\n",
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"score, acc = cnnmodel.evaluate(test_data, test_labels)\n",
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"print('Test accuracy with CNN:', acc)"
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],
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"execution_count": 11,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Define a 1D CNN model.\n",
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"157/157 [==============================] - 23s 41ms/step - loss: 0.6748 - acc: 0.6068 - val_loss: 0.5651 - val_acc: 0.7040\n",
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"782/782 [==============================] - 5s 7ms/step - loss: 0.5735 - acc: 0.7004\n",
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"Test accuracy with CNN: 0.7003999948501587\n"
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"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
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"Instructions for updating:\n",
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"Call initializer instance with the dtype argument instead of passing it to the constructor\n",
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"Train on 20000 samples, validate on 5000 samples\n",
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"20000/20000 [==============================] - 156s 8ms/sample - loss: 0.6706 - acc: 0.5972 - val_loss: 0.5116 - val_acc: 0.7512\n",
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"25000/25000 [==============================] - 67s 3ms/sample - loss: 0.5239 - acc: 0.7415\n",
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"Test accuracy with CNN: 0.74152\n"
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],
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"name": "stdout"
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}
@@ -431,7 +509,7 @@
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"base_uri": "https://localhost:8080/"
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},
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"id": "zI0bISwRb5_w",
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"outputId": "f34c3d27-a2fc-4996-fd11-5027d2bc170a"
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"outputId": "d7697504-dacb-415c-b131-b89d6b10c771"
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},
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"source": [
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"print(\"Defining and training a CNN model, training embedding layer on the fly instead of using pre-trained embeddings\")\n",
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"score, acc = cnnmodel.evaluate(test_data, test_labels)\n",
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"print('Test accuracy with CNN:', acc)"
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],
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"execution_count": 11,
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"execution_count": 12,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Defining and training a CNN model, training embedding layer on the fly instead of using pre-trained embeddings\n",
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"157/157 [==============================] - 14s 82ms/step - loss: 0.5217 - acc: 0.7037 - val_loss: 0.3567 - val_acc: 0.8542\n",
467-
"782/782 [==============================] - 6s 8ms/step - loss: 0.3553 - acc: 0.8464\n",
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"Test accuracy with CNN: 0.8463600277900696\n"
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"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/initializers.py:119: calling RandomUniform.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
545+
"Instructions for updating:\n",
546+
"Call initializer instance with the dtype argument instead of passing it to the constructor\n",
547+
"Train on 20000 samples, validate on 5000 samples\n",
548+
"20000/20000 [==============================] - 234s 12ms/sample - loss: 0.5323 - acc: 0.6927 - val_loss: 0.3179 - val_acc: 0.8644\n",
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"25000/25000 [==============================] - 84s 3ms/sample - loss: 0.3409 - acc: 0.8495\n",
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"Test accuracy with CNN: 0.84948\n"
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],
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"name": "stdout"
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}
@@ -487,7 +569,7 @@
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"base_uri": "https://localhost:8080/"
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},
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"id": "SvBt2Brib5_4",
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"outputId": "08bdb448-ee92-4a7c-d65c-0259730cf340"
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"outputId": "008fe9fa-13bf-4127-ba46-67916426ddbe"
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},
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"source": [
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"print(\"Defining and training an LSTM model, training embedding layer on the fly\")\n",
@@ -510,17 +592,20 @@
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" batch_size=32)\n",
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"print('Test accuracy with RNN:', acc)"
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],
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"execution_count": 12,
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"execution_count": 13,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Defining and training an LSTM model, training embedding layer on the fly\n",
519-
"WARNING:tensorflow:Layer lstm will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
601+
"WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
602+
"Instructions for updating:\n",
603+
"Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
520604
"Training the RNN\n",
521-
"625/625 [==============================] - 1343s 2s/step - loss: 0.4818 - accuracy: 0.7686 - val_loss: 0.3970 - val_accuracy: 0.8374\n",
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"782/782 [==============================] - 219s 280ms/step - loss: 0.3980 - accuracy: 0.8356\n",
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"Test accuracy with RNN: 0.8356000185012817\n"
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"Train on 20000 samples, validate on 5000 samples\n",
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"20000/20000 [==============================] - 1365s 68ms/sample - loss: 0.4997 - acc: 0.7506 - val_loss: 0.3839 - val_acc: 0.8403\n",
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"25000/25000 [==============================] - 198s 8ms/sample - loss: 0.3962 - acc: 0.8300\n",
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"Test accuracy with RNN: 0.82998\n"
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],
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"name": "stdout"
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}
@@ -542,7 +627,7 @@
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"base_uri": "https://localhost:8080/"
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},
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"id": "Eymx0IyCb5_-",
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"outputId": "8bdfbaa9-51b9-4fcf-aae8-c3460a7df533"
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"outputId": "da0fa303-a4c4-4b92-ff42-54f1a1d51e45"
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},
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"source": [
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"print(\"Defining and training an LSTM model, using pre-trained embedding layer\")\n",
@@ -564,17 +649,17 @@
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" batch_size=32)\n",
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"print('Test accuracy with RNN:', acc)"
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],
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"execution_count": 13,
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"execution_count": 14,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"Defining and training an LSTM model, using pre-trained embedding layer\n",
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"WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n",
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"Training the RNN\n",
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"625/625 [==============================] - 1151s 2s/step - loss: 0.6257 - accuracy: 0.6520 - val_loss: 0.5683 - val_accuracy: 0.7186\n",
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"782/782 [==============================] - 218s 279ms/step - loss: 0.5713 - accuracy: 0.7166\n",
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"Test accuracy with RNN: 0.7166399955749512\n"
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"Train on 20000 samples, validate on 5000 samples\n",
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"20000/20000 [==============================] - 1156s 58ms/sample - loss: 0.6122 - acc: 0.6602 - val_loss: 0.4538 - val_acc: 0.8017\n",
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"25000/25000 [==============================] - 200s 8ms/sample - loss: 0.4666 - acc: 0.7930\n",
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"Test accuracy with RNN: 0.793\n"
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],
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"name": "stdout"
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}

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