{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#This is an example of performing Video Activity Recognition using LSTM\n", "Modified from \"Hands-on Computer Vision with TensorFlow 2\" by B. Planche and E. Andres" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: opencv-python in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages\n", "Requirement already satisfied: numpy>=1.11.3 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from opencv-python)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using pip version 9.0.3, however version 19.3.1 is available.\n", "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: matplotlib in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages\n", "Requirement already satisfied: numpy>=1.7.1 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from matplotlib)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib)\n", "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib)\n", "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib)\n", "Requirement already satisfied: pytz in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib)\n", "Requirement already satisfied: six>=1.10 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from matplotlib)\n", "Requirement already satisfied: setuptools in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using pip version 9.0.3, however version 19.3.1 is available.\n", "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tqdm in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using pip version 9.0.3, however version 19.3.1 is available.\n", "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: scikit-learn in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages\n", "Requirement already satisfied: numpy>=1.11.0 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from scikit-learn)\n", "Requirement already satisfied: scipy>=0.17.0 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from scikit-learn)\n", "Requirement already satisfied: joblib>=0.11 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from scikit-learn)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using pip version 9.0.3, however version 19.3.1 is available.\n", "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" ] } ], "source": [ "# STEP 1: Install packages in the current environment\n", "import sys\n", "!{sys.executable} -m pip install opencv-python \n", "!{sys.executable} -m pip install matplotlib\n", "!{sys.executable} -m pip install tqdm\n", "!{sys.executable} -m pip install scikit-learn\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: tensorflow==1.14 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages\n", "Requirement already satisfied: grpcio>=1.8.6 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: wrapt>=1.11.1 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: astor>=0.6.0 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: keras-preprocessing>=1.0.5 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: gast>=0.2.0 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: six>=1.10.0 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: keras-applications>=1.0.6 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: google-pasta>=0.1.6 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: numpy<2.0,>=1.14.5 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: protobuf>=3.6.1 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: wheel>=0.26 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: absl-py>=0.7.0 in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: termcolor>=1.1.0 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorflow==1.14)\n", "Requirement already satisfied: werkzeug>=0.11.15 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14)\n", "Requirement already satisfied: setuptools>=41.0.0 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14)\n", "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\lynne\\anaconda3\\envs\\tensorflow\\lib\\site-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14)\n", "Requirement already satisfied: h5py in c:\\users\\lynne\\appdata\\roaming\\python\\python36\\site-packages (from keras-applications>=1.0.6->tensorflow==1.14)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "You are using pip version 9.0.3, however version 19.3.1 is available.\n", "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n" ] } ], "source": [ "!{sys.executable} -m pip install tensorflow==1.14\n", "#!{sys.executable} -m pip install --user tensorflow==2.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Step 2: import modules" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\framework\\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n", "C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorboard\\compat\\tensorflow_stub\\dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n", " np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "hi\n", "Tensorflow verison 1.14.0\n", "Tensorflow version \n" ] } ], "source": [ "import tensorflow as tf\n", "import os\n", "import cv2\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import tqdm\n", "from sklearn.preprocessing import LabelBinarizer\n", "print(\"hi\")\n", "print(\"Tensorflow verison \"+ str(tf.version.VERSION))\n", "print(\"Tensorflow version \" + str(tf.version))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Step 3: setup variables" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "module 'tensorflow' has no attribute 'Version'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m#tf.enable_eager_execution()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m#tf.executing_eagerly()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVersion\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\util\\deprecation_wrapper.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_dw_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 105\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Accessing local variables before they are created.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m \u001b[0mattr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dw_wrapped_module\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 107\u001b[0m if (self._dw_warning_count < _PER_MODULE_WARNING_LIMIT and\n\u001b[0;32m 108\u001b[0m name not in self._dw_deprecated_printed):\n", "\u001b[1;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'Version'" ] } ], "source": [ "# enable eager execution to guarantee the tensors have discrete instead of symbolic values, \n", "# so you can do operations on them\n", "#tf.enable_eager_execution()\n", "#tf.executing_eagerly()\n", "print(tf.Version.version)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\UCF-101\\**\\*.avi\n" ] } ], "source": [ "#location of where YOU have installed the data set UCF-101 located\n", "# at \n", "#BASE_PATH = '../data/UCF-101'\n", "#change the base path to location YOU installed UCF-101 dataset\n", "#BASE_PATH = 'C:/Grewe/Classes/CS663/Mat/LSTM/data/UCF-101'\n", "BASE_PATH = 'C:\\\\Grewe\\\\Classes\\\\CS663\\\\Mat\\\\LSTM\\\\data\\\\UCF-101'\n", "VIDEOS_PATH = os.path.join(BASE_PATH, '**','*.avi')\n", "\n", "#this specifies the sequence length will process by LSTM\n", "SEQUENCE_LENGTH = 40\n", "print(VIDEOS_PATH)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 4: sample the video --do not process every frame\n", "PART 1: define function frame_generator() that creates Sequence_length samples by taking every Kth sample were K= num_frames_in_video / SEQUENCE LENGTH PART 2: you load the DataSet and specify the output will be frames of size 299x299 x3(rgb) AND you create batches of 16 together at a time" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\data\\ops\\dataset_ops.py:494: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "tf.py_func is deprecated in TF V2. Instead, there are two\n", " options available in V2.\n", " - tf.py_function takes a python function which manipulates tf eager\n", " tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n", " an ndarray (just call tensor.numpy()) but having access to eager tensors\n", " means `tf.py_function`s can use accelerators such as GPUs as well as\n", " being differentiable using a gradient tape.\n", " - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n", " (it is not differentiable, and manipulates numpy arrays). It drops the\n", " stateful argument making all functions stateful.\n", " \n", "\n" ] } ], "source": [ "def frame_generator():\n", " video_paths = tf.io.gfile.glob(VIDEOS_PATH)\n", " np.random.shuffle(video_paths)\n", " for video_path in video_paths:\n", " frames = []\n", " cap = cv2.VideoCapture(video_path)\n", " num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n", " sample_every_frame = max(1, num_frames // SEQUENCE_LENGTH)\n", " current_frame = 0\n", "\n", " label = os.path.basename(os.path.dirname(video_path))\n", "\n", " max_images = SEQUENCE_LENGTH\n", " while True:\n", " success, frame = cap.read()\n", " if not success:\n", " break\n", "\n", " if current_frame % sample_every_frame == 0:\n", " # OPENCV reads in BGR, tensorflow expects RGB so we invert the order\n", " frame = frame[:, :, ::-1]\n", " img = tf.image.resize(frame, (299, 299))\n", " img = tf.keras.applications.inception_v3.preprocess_input(\n", " img)\n", " max_images -= 1\n", " yield img, video_path\n", "\n", " if max_images == 0:\n", " break\n", " current_frame += 1\n", "\n", "# `from_generator` might throw a warning, expected to disappear in upcoming versions:\n", "# https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/data/Dataset#for_example_2\n", "dataset = tf.data.Dataset.from_generator(frame_generator,\n", " output_types=(tf.float32, tf.string),\n", " output_shapes=((299, 299, 3), ()))\n", "\n", "dataset = dataset.batch(16).prefetch(tf.data.experimental.AUTOTUNE)\n", "print(dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 5: \n", "For Feature Extraction we are going to use a existing CNN model called Inception V3 which is built into TensorFlow" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-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", "Instructions for updating:\n", "Call initializer instance with the dtype argument instead of passing it to the constructor\n" ] } ], "source": [ "inception_v3 = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet')\n", "\n", "x = inception_v3.output\n", "\n", "# We add Average Pooling to transform the feature map from\n", "# 8 * 8 * 2048 to 1 x 2048, as we don't need spatial information\n", "pooling_output = tf.keras.layers.GlobalAveragePooling2D()(x)\n", "\n", "feature_extraction_model = tf.keras.Model(inception_v3.input, pooling_output)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "going to save feature extractor model toC:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\SavedModelDir_FeatureExtraction_MobileNetV2_TF_1_14\n" ] }, { "ename": "AssertionError", "evalue": "Export directory already exists, and isn't empty. Please choose a different export directory, or delete all the contents of the specified directory: C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\SavedModelDir_FeatureExtraction_MobileNetV2_TF_1_14", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mSAVED_FeatureExtractor_MODEL_DIR\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"C:\\\\Grewe\\\\Classes\\\\CS663\\\\Mat\\\\LSTM\\\\data\\\\SavedModelDir_FeatureExtraction_MobileNetV2_TF_1_14\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"going to save feature extractor model to\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSAVED_FeatureExtractor_MODEL_DIR\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexport_saved_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeature_extraction_model\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSAVED_FeatureExtractor_MODEL_DIR\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\saved_model.py\u001b[0m in \u001b[0;36mexport_saved_model\u001b[1;34m(model, saved_model_path, custom_objects, as_text, input_signature, serving_only)\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 118\u001b[0m _save_v1_format(model, saved_model_path, custom_objects, as_text,\n\u001b[1;32m--> 119\u001b[1;33m input_signature)\n\u001b[0m\u001b[0;32m 120\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\saved_model.py\u001b[0m in \u001b[0;36m_save_v1_format\u001b[1;34m(model, path, custom_objects, as_text, input_signature)\u001b[0m\n\u001b[0;32m 164\u001b[0m 'argument serving_only=True.')\n\u001b[0;32m 165\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 166\u001b[1;33m \u001b[0mbuilder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msaved_model_builder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_SavedModelBuilder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 168\u001b[0m \u001b[1;31m# Manually save variables to export them in an object-based checkpoint. This\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\saved_model\\builder_impl.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, export_dir)\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[1;34m\"Export directory already exists, and isn't empty. Please choose \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[1;34m\"a different export directory, or delete all the contents of the \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m \"specified directory: %s\" % export_dir)\n\u001b[0m\u001b[0;32m 103\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[0mfile_io\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecursive_create_dir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_export_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAssertionError\u001b[0m: Export directory already exists, and isn't empty. Please choose a different export directory, or delete all the contents of the specified directory: C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\SavedModelDir_FeatureExtraction_MobileNetV2_TF_1_14" ] } ], "source": [ "#OPTIONAL: Store Feature Extractor Model to a Saved Directory\n", "# save CNN model in SavedModel format\n", "\n", "\n", "SAVED_FeatureExtractor_MODEL_DIR = \"C:\\\\Grewe\\\\Classes\\\\CS663\\\\Mat\\\\LSTM\\\\data\\\\SavedModelDir_FeatureExtraction_MobileNetV2_TF_1_14\"\n", "print(\"going to save feature extractor model to\" + str(SAVED_FeatureExtractor_MODEL_DIR))\n", "tf.keras.experimental.export_saved_model(feature_extraction_model, SAVED_FeatureExtractor_MODEL_DIR)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#OPTIONAL: Convert and Store Feature Extractor model to a tflite file for use in Mobile\n", "#FOR VERSION Tensorflow 1.14\n", "# do it from the previously SavedModel directory\n", "\n", "#summary of model will convert\n", "feature_extraction_model.summary()\n", "\n", "# convert the SavedModel to a TFLite model\n", "converter = tf.lite.TFLiteConverter.from_saved_model(SAVED_FeatureExtractor_MODEL_DIR)\n", "tflite_feature_extractor_model = converter.convert()\n", "BASE_DATA_PATH = 'C:\\\\Grewe\\\\Classes\\\\CS663\\\\Mat\\\\LSTM\\\\data'\n", "tflite_feature_extractor_file = os.path.join(BASE_DATA_PATH, 'FeatureExtractorModel_MobileBetV2_TF1_14.tflite')\n", "print(\" want to save tflite_file\" + tflite_feature_extractor_file)\n", "open(tflite_feature_extractor_file, \"wb\").write(tflite_feature_extractor_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 6: \n", "Extract Features using our InceptionV3 CNN model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "current_path = None\n", "all_features = []\n", "\n", "#cycle through the dataset and visit each image, note the tdqm.tdqm is a iterable object with an updateable progress bar\n", "#that updates each time a new iteration is called \n", "\n", "#each time through outter for loop retrieve an image and\n", "# the video filename it belongs to (including path) and each image 299x299x3(rgb)\n", "#call feature_extraction_model above (Inception v3) for the image to extract the features\n", "#do for each image in the video\n", "for img, batch_paths in tqdm.tqdm(dataset):\n", " batch_features = feature_extraction_model(img)\n", " #reshape the tensor to shape ( #features x 1)--see https://www.tensorflow.org/api_docs/python/tf/reshape\n", " batch_features = tf.reshape(batch_features, \n", " (batch_features.shape[0], -1))\n", " \n", " for features, path in zip(batch_features.numpy(), batch_paths.numpy()):\n", " if path != current_path and current_path is not None:\n", " output_path = current_path.decode().replace('.avi', '.npy')\n", " np.save(output_path, all_features)\n", " all_features = []\n", " \n", " current_path = path\n", " all_features.append(features)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 7: \n", "Setup array of Class Labels" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Doors' 'stairs']\n", "[[0 1]\n", " [1 0]]\n", "[[0 1]\n", " [1 0]\n", " [1 0]]\n", "['Doors' 'stairs' 'stairs']\n", "length of labels 2\n" ] } ], "source": [ "#NOTE: FYI ONLY --- the default for LabelBinaizer when have 2 classes only is a 1 length vector not 2. Does [0=class 1] and [1=class 2].\n", "# we want is [X=class 1 Y= class2]. So create a class that does what we want.\n", "#Create a My2ClassLabelBinarizer \n", "\n", "\n", "class My2ClassLabelBinarizer(LabelBinarizer):\n", " def transform(self, y):\n", " Y = super().transform(y)\n", " if self.y_type_ == 'binary':\n", " return np.hstack((Y, 1-Y))\n", " else:\n", " return Y\n", " def inverse_transform(self, Y, threshold=None):\n", " if self.y_type_ == 'binary':\n", " return super().inverse_transform(Y[:, 0], threshold)\n", " else:\n", " return super().inverse_transform(Y, threshold)\n", "\n", "\n", "# In[12]:\n", "\n", "LABELS_2ClassExample = ['Doors','stairs']\n", "encoder = My2ClassLabelBinarizer()\n", "encoder.fit(LABELS_2ClassExample)\n", "print(encoder.classes_)\n", "print(encoder.transform(['Doors', 'stairs']))\n", "\n", "t= encoder.transform(['Doors', 'stairs', 'stairs'])\n", "print(t)\n", "print(encoder.inverse_transform(t))\n", "print(\"length of labels \" + str(len(LABELS_2ClassExample)))\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ApplyEyeMakeup' 'ApplyLipstick' 'Archery' 'BabyCrawling' 'BalanceBeam'\n", " 'BandMarching' 'BaseballPitch' 'Basketball' 'BasketballDunk' 'BenchPress'\n", " 'Biking' 'Billiards' 'BlowDryHair' 'BlowingCandles' 'BodyWeightSquats'\n", " 'Bowling' 'BoxingPunchingBag' 'BoxingSpeedBag' 'BreastStroke'\n", " 'BrushingTeeth' 'CleanAndJerk' 'CliffDiving' 'CricketBowling'\n", " 'CricketShot' 'CuttingInKitchen' 'Diving' 'Drumming' 'Fencing'\n", " 'FieldHockeyPenalty' 'FloorGymnastics' 'FrisbeeCatch' 'FrontCrawl'\n", " 'GolfSwing' 'Haircut' 'HammerThrow' 'Hammering' 'HandstandPushups'\n", " 'HandstandWalking' 'HeadMassage' 'HighJump' 'HorseRace' 'HorseRiding'\n", " 'HulaHoop' 'IceDancing' 'JavelinThrow' 'JugglingBalls' 'JumpRope'\n", " 'JumpingJack' 'Kayaking' 'Knitting' 'LongJump' 'Lunges' 'MilitaryParade'\n", " 'Mixing' 'MoppingFloor' 'Nunchucks' 'ParallelBars' 'PizzaTossing'\n", " 'PlayingCello' 'PlayingDaf' 'PlayingDhol' 'PlayingFlute' 'PlayingGuitar'\n", " 'PlayingPiano' 'PlayingSitar' 'PlayingTabla' 'PlayingViolin' 'PoleVault'\n", " 'PommelHorse' 'PullUps' 'Punch' 'PushUps' 'Rafting' 'RockClimbingIndoor'\n", " 'RopeClimbing' 'Rowing' 'SalsaSpin' 'ShavingBeard' 'Shotput'\n", " 'SkateBoarding' 'Skiing' 'Skijet' 'SkyDiving' 'SoccerJuggling'\n", " 'SoccerPenalty' 'StillRings' 'SumoWrestling' 'Surfing' 'Swing'\n", " 'TableTennisShot' 'TaiChi' 'TennisSwing' 'ThrowDiscus'\n", " 'TrampolineJumping' 'Typing' 'UnevenBars' 'VolleyballSpiking'\n", " 'WalkingWithDog' 'WallPushups' 'WritingOnBoard' 'YoYo']\n", "[[0 0 0 ... 0 0 0]\n", " [0 1 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": [ "LABELS = ['UnevenBars','ApplyLipstick','TableTennisShot','Fencing','Mixing','SumoWrestling','HulaHoop','PommelHorse','HorseRiding','SkyDiving','BenchPress','GolfSwing','HeadMassage','FrontCrawl','Haircut','HandstandWalking','Skiing','PlayingDaf','PlayingSitar','FrisbeeCatch','CliffDiving','BoxingSpeedBag','Kayaking','Rafting','WritingOnBoard','VolleyballSpiking','Archery','MoppingFloor','JumpRope','Lunges','BasketballDunk','Surfing','SkateBoarding','FloorGymnastics','Billiards','CuttingInKitchen','BlowingCandles','PlayingCello','JugglingBalls','Drumming','ThrowDiscus','BaseballPitch','SoccerPenalty','Hammering','BodyWeightSquats','SoccerJuggling','CricketShot','BandMarching','PlayingPiano','BreastStroke','ApplyEyeMakeup','HighJump','IceDancing','HandstandPushups','RockClimbingIndoor','HammerThrow','WallPushups','RopeClimbing','Basketball','Shotput','Nunchucks','WalkingWithDog','PlayingFlute','PlayingDhol','PullUps','CricketBowling','BabyCrawling','Diving','TaiChi','YoYo','BlowDryHair','PushUps','ShavingBeard','Knitting','HorseRace','TrampolineJumping','Typing','Bowling','CleanAndJerk','MilitaryParade','FieldHockeyPenalty','PlayingViolin','Skijet','PizzaTossing','LongJump','PlayingTabla','PlayingGuitar','BrushingTeeth','PoleVault','Punch','ParallelBars','Biking','BalanceBeam','Swing','JavelinThrow','Rowing','StillRings','SalsaSpin','TennisSwing','JumpingJack','BoxingPunchingBag'] \n", "encoder = LabelBinarizer()\n", "encoder.fit(LABELS)\n", "print(encoder.classes_)\n", "print(encoder.transform(LABELS))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 8: \n", " Create the LSTM model: 1) Masking layer - this tells the model that will have input that is padded with zeros (to get a fixed length of input here 40x FeatureVector(1x2048) --so the inputs are the same length, The masking tells the model to ignore this zero padded data (mask_value=0.) see https://www.tensorflow.org/guide/keras/masking_and_padding 2) LSTM layer with 512 cells, dropout 0.5, recurrent_dropout of 0.5 3) a fully connected relu activation layer with 256 outputs, 4) a droupout layer 0.5 5) a final decision fully connected layer of putput length of labels (which is the number of classes) with softmax activation.\n", " \n", "#### dropout and recurrent_dropout in LSTM: \n", "method where input and recurrent connections to LSTM units are probabilistically excluded from activation and weight updates while training a network. This has the effect of reducing overfitting and improving model performance. see https://machinelearningmastery.com/use-dropout-lstm-networks-time-series-forecasting/ for a better understanding.\n", "\n", "#### LSTM layer - output = [1x512] for entire video (image sequence) \n", "#### LSTM - and the default value of return_sequence (=False) as not specified\n", "see for FALSE= return the last output from processing all the input sequence. if was set to TRUE would mean returm the full sequence output values. If it had been set to TRUE the output would be [SEQUENCE_LENGTHx512] = [40 x 512]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "#setup a keras Sequential model with 1) Masking layer 2) LSTM layer with 512 cells, dropout 0.5, recurrent_dropout of 0.5 \n", "# 3) a fully connected relu activation layer with 256 outputs, 4) a droupout layer 5) a final decision fully connected layer of length of labels\n", "# (which is the number of classes) with softmax activation.\n", "\n", "model = tf.keras.Sequential([\n", " tf.keras.layers.Masking(mask_value=0.),\n", " tf.keras.layers.LSTM(512, dropout=0.5, recurrent_dropout=0.5),\n", " tf.keras.layers.Dense(256, activation='relu'),\n", " tf.keras.layers.Dropout(0.5),\n", " tf.keras.layers.Dense(len(LABELS), activation='softmax')\n", "])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 8: \n", "Setup for the model the Loss function, the Optimizer function, and any metrics want to compute in training" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "model.compile(loss='categorical_crossentropy',\n", " optimizer='rmsprop',\n", " metrics=['accuracy', 'top_k_categorical_accuracy'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 9: \n", "Setup the training and test list which are lists of the training filenames. Note you will need to change the location of these files to point to your location. Define a function make_generator that returns a generator which will randomly shuffle a file list (either training or testing that will be passed later) and then changes the file extension of the avi files listed in the list to .npy which is our features for that avi video which were calcluated in step 6" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "test_file = 'C:/Grewe/Classes/CS663/Mat/LSTM/data/testlist01_reverse_order.txt'\n", "train_file = 'C:/Grewe/Classes/CS663/Mat/LSTM/data/trainlist01_reverse_order.txt'\n", "#test_file = 'C:/Grewe/Classes/CS663/Mat/LSTM/data/testlist01.txt'\n", "#train_file = 'C:/Grewe/Classes/CS663/Mat/LSTM/data/trainlist01.txt'\n", "\n", "with open(test_file) as f:\n", " test_list = [row.strip() for row in list(f)]\n", "\n", "with open(train_file) as f:\n", " train_list = [row.strip() for row in list(f)]\n", " train_list = [row.split(' ')[0] for row in train_list]\n", "\n", "\n", "def make_generator(file_list):\n", " def generator():\n", " np.random.shuffle(file_list)\n", " for path in file_list:\n", " full_path = os.path.join(BASE_PATH, path).replace('.avi', '.npy')\n", "\n", " label = os.path.basename(os.path.dirname(path))\n", " features = np.load(full_path)\n", "\n", " padded_sequence = np.zeros((SEQUENCE_LENGTH, 2048))\n", " padded_sequence[0:len(features)] = np.array(features)\n", "\n", " transformed_label = encoder.transform([label])\n", " yield padded_sequence, transformed_label[0]\n", " return generator" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g01_c01.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g01_c02.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g01_c03.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g01_c04.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g01_c05.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g01_c06.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g02_c01.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g02_c02.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g02_c03.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g02_c04.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g03_c01.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g03_c02.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g03_c03.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g03_c04.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g03_c05.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g03_c06.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g04_c01.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g04_c02.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g04_c03.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g04_c04.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g04_c05.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g04_c06.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g04_c07.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g05_c01.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g05_c02.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g05_c03.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g05_c04.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g05_c05.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g05_c06.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g05_c07.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g06_c01.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g06_c02.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g06_c03.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g06_c04.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g06_c05.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g06_c06.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g06_c07.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g07_c01.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g07_c02.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g07_c03.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g07_c04.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g07_c05.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g07_c06.avi', 'ApplyEyeMakeup\\\\v_ApplyEyeMakeup_g07_c07.avi', 'ApplyLipstick\\\\v_ApplyLipstick_g01_c01.avi', 'ApplyLipstick\\\\v_ApplyLipstick_g01_c02.avi', 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'BabyCrawling\\\\v_BabyCrawling_g05_c04.avi', 'BabyCrawling\\\\v_BabyCrawling_g05_c05.avi', 'BabyCrawling\\\\v_BabyCrawling_g06_c01.avi', 'BabyCrawling\\\\v_BabyCrawling_g06_c02.avi', 'BabyCrawling\\\\v_BabyCrawling_g06_c03.avi', 'BabyCrawling\\\\v_BabyCrawling_g06_c04.avi', 'BabyCrawling\\\\v_BabyCrawling_g06_c05.avi', 'BabyCrawling\\\\v_BabyCrawling_g06_c06.avi', 'BabyCrawling\\\\v_BabyCrawling_g07_c01.avi', 'BabyCrawling\\\\v_BabyCrawling_g07_c02.avi', 'BabyCrawling\\\\v_BabyCrawling_g07_c03.avi', 'BabyCrawling\\\\v_BabyCrawling_g07_c04.avi', 'BabyCrawling\\\\v_BabyCrawling_g07_c05.avi', 'BabyCrawling\\\\v_BabyCrawling_g07_c06.avi', 'BalanceBeam\\\\v_BalanceBeam_g01_c01.avi', 'BalanceBeam\\\\v_BalanceBeam_g01_c02.avi', 'BalanceBeam\\\\v_BalanceBeam_g01_c03.avi', 'BalanceBeam\\\\v_BalanceBeam_g01_c04.avi', 'BalanceBeam\\\\v_BalanceBeam_g02_c01.avi', 'BalanceBeam\\\\v_BalanceBeam_g02_c02.avi', 'BalanceBeam\\\\v_BalanceBeam_g02_c03.avi', 'BalanceBeam\\\\v_BalanceBeam_g02_c04.avi', 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'BalanceBeam\\\\v_BalanceBeam_g07_c04.avi', 'BandMarching\\\\v_BandMarching_g01_c01.avi', 'BandMarching\\\\v_BandMarching_g01_c02.avi', 'BandMarching\\\\v_BandMarching_g01_c03.avi', 'BandMarching\\\\v_BandMarching_g01_c04.avi', 'BandMarching\\\\v_BandMarching_g01_c05.avi', 'BandMarching\\\\v_BandMarching_g01_c06.avi', 'BandMarching\\\\v_BandMarching_g01_c07.avi', 'BandMarching\\\\v_BandMarching_g02_c01.avi', 'BandMarching\\\\v_BandMarching_g02_c02.avi', 'BandMarching\\\\v_BandMarching_g02_c03.avi', 'BandMarching\\\\v_BandMarching_g02_c04.avi', 'BandMarching\\\\v_BandMarching_g02_c05.avi', 'BandMarching\\\\v_BandMarching_g02_c06.avi', 'BandMarching\\\\v_BandMarching_g02_c07.avi', 'BandMarching\\\\v_BandMarching_g03_c01.avi', 'BandMarching\\\\v_BandMarching_g03_c02.avi', 'BandMarching\\\\v_BandMarching_g03_c03.avi', 'BandMarching\\\\v_BandMarching_g03_c04.avi', 'BandMarching\\\\v_BandMarching_g03_c05.avi', 'BandMarching\\\\v_BandMarching_g03_c06.avi', 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'VolleyballSpiking\\\\v_VolleyballSpiking_g04_c02.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g04_c03.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g04_c04.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g04_c05.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g04_c06.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g04_c07.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g05_c01.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g05_c02.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g05_c03.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g05_c04.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g05_c05.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g06_c01.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g06_c02.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g06_c03.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g06_c04.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g07_c01.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g07_c02.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g07_c03.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g07_c04.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g07_c05.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g07_c06.avi', 'VolleyballSpiking\\\\v_VolleyballSpiking_g07_c07.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g01_c01.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g01_c02.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g01_c03.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g01_c04.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g02_c01.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g02_c02.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g02_c03.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g02_c04.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g02_c05.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g02_c06.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g03_c01.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g03_c02.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g03_c03.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g03_c04.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g03_c05.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g04_c01.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g04_c02.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g04_c03.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g04_c04.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g04_c05.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g05_c01.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g05_c02.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g05_c03.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g05_c04.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g05_c05.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g06_c01.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g06_c02.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g06_c03.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g06_c04.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g06_c05.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g07_c01.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g07_c02.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g07_c03.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g07_c04.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g07_c05.avi', 'WalkingWithDog\\\\v_WalkingWithDog_g07_c06.avi', 'WallPushups\\\\v_WallPushups_g01_c01.avi', 'WallPushups\\\\v_WallPushups_g01_c02.avi', 'WallPushups\\\\v_WallPushups_g01_c03.avi', 'WallPushups\\\\v_WallPushups_g01_c04.avi', 'WallPushups\\\\v_WallPushups_g02_c01.avi', 'WallPushups\\\\v_WallPushups_g02_c02.avi', 'WallPushups\\\\v_WallPushups_g02_c03.avi', 'WallPushups\\\\v_WallPushups_g02_c04.avi', 'WallPushups\\\\v_WallPushups_g03_c01.avi', 'WallPushups\\\\v_WallPushups_g03_c02.avi', 'WallPushups\\\\v_WallPushups_g03_c03.avi', 'WallPushups\\\\v_WallPushups_g03_c04.avi', 'WallPushups\\\\v_WallPushups_g03_c05.avi', 'WallPushups\\\\v_WallPushups_g04_c01.avi', 'WallPushups\\\\v_WallPushups_g04_c02.avi', 'WallPushups\\\\v_WallPushups_g04_c03.avi', 'WallPushups\\\\v_WallPushups_g04_c04.avi', 'WallPushups\\\\v_WallPushups_g05_c01.avi', 'WallPushups\\\\v_WallPushups_g05_c02.avi', 'WallPushups\\\\v_WallPushups_g05_c03.avi', 'WallPushups\\\\v_WallPushups_g05_c04.avi', 'WallPushups\\\\v_WallPushups_g05_c05.avi', 'WallPushups\\\\v_WallPushups_g06_c01.avi', 'WallPushups\\\\v_WallPushups_g06_c02.avi', 'WallPushups\\\\v_WallPushups_g06_c03.avi', 'WallPushups\\\\v_WallPushups_g06_c04.avi', 'WallPushups\\\\v_WallPushups_g06_c05.avi', 'WallPushups\\\\v_WallPushups_g06_c06.avi', 'WallPushups\\\\v_WallPushups_g06_c07.avi', 'WallPushups\\\\v_WallPushups_g07_c01.avi', 'WallPushups\\\\v_WallPushups_g07_c02.avi', 'WallPushups\\\\v_WallPushups_g07_c03.avi', 'WallPushups\\\\v_WallPushups_g07_c04.avi', 'WallPushups\\\\v_WallPushups_g07_c05.avi', 'WallPushups\\\\v_WallPushups_g07_c06.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g01_c01.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g01_c02.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g01_c03.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g01_c04.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g01_c05.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g01_c06.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g01_c07.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g02_c01.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g02_c02.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g02_c03.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g02_c04.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g02_c05.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g02_c06.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g02_c07.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g03_c01.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g03_c02.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g03_c03.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g03_c04.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g03_c05.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g03_c06.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g03_c07.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g04_c01.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g04_c02.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g04_c03.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g04_c04.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g05_c01.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g05_c02.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g05_c03.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g05_c04.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g05_c05.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g05_c06.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g06_c01.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g06_c02.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g06_c03.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g06_c04.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g06_c05.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g06_c06.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g06_c07.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g07_c01.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g07_c02.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g07_c03.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g07_c04.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g07_c05.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g07_c06.avi', 'WritingOnBoard\\\\v_WritingOnBoard_g07_c07.avi', 'YoYo\\\\v_YoYo_g01_c01.avi', 'YoYo\\\\v_YoYo_g01_c02.avi', 'YoYo\\\\v_YoYo_g01_c03.avi', 'YoYo\\\\v_YoYo_g01_c04.avi', 'YoYo\\\\v_YoYo_g01_c05.avi', 'YoYo\\\\v_YoYo_g01_c06.avi', 'YoYo\\\\v_YoYo_g01_c07.avi', 'YoYo\\\\v_YoYo_g02_c01.avi', 'YoYo\\\\v_YoYo_g02_c02.avi', 'YoYo\\\\v_YoYo_g02_c03.avi', 'YoYo\\\\v_YoYo_g02_c04.avi', 'YoYo\\\\v_YoYo_g02_c05.avi', 'YoYo\\\\v_YoYo_g03_c01.avi', 'YoYo\\\\v_YoYo_g03_c02.avi', 'YoYo\\\\v_YoYo_g03_c03.avi', 'YoYo\\\\v_YoYo_g03_c04.avi', 'YoYo\\\\v_YoYo_g03_c05.avi', 'YoYo\\\\v_YoYo_g03_c06.avi', 'YoYo\\\\v_YoYo_g04_c01.avi', 'YoYo\\\\v_YoYo_g04_c02.avi', 'YoYo\\\\v_YoYo_g04_c03.avi', 'YoYo\\\\v_YoYo_g04_c04.avi', 'YoYo\\\\v_YoYo_g04_c05.avi', 'YoYo\\\\v_YoYo_g05_c01.avi', 'YoYo\\\\v_YoYo_g05_c02.avi', 'YoYo\\\\v_YoYo_g05_c03.avi', 'YoYo\\\\v_YoYo_g05_c04.avi', 'YoYo\\\\v_YoYo_g05_c05.avi', 'YoYo\\\\v_YoYo_g06_c01.avi', 'YoYo\\\\v_YoYo_g06_c02.avi', 'YoYo\\\\v_YoYo_g06_c03.avi', 'YoYo\\\\v_YoYo_g06_c04.avi', 'YoYo\\\\v_YoYo_g07_c01.avi', 'YoYo\\\\v_YoYo_g07_c02.avi', 'YoYo\\\\v_YoYo_g07_c03.avi', 'YoYo\\\\v_YoYo_g07_c04.avi']" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(test_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 10: \n", "Setup the train_dataset and valid_dataset (validation/testing). Here we setting up training batch sets of 16. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "#for tensorflow 2.*\n", "train_dataset = tf.data.Dataset.from_generator(make_generator(train_list),\n", " output_types=(tf.float32, tf.int16),\n", " output_shapes=((SEQUENCE_LENGTH, 2048), (len(LABELS))))\n", "train_dataset = train_dataset.batch(16, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)\n", "\n", "\n", "valid_dataset = tf.data.Dataset.from_generator(make_generator(test_list),\n", " output_types=(tf.float32, tf.int16),\n", " output_shapes=((SEQUENCE_LENGTH, 2048), (len(LABELS))))\n", "valid_dataset = valid_dataset.batch(16, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "#for tensorflow 1.14\n", "train_dataset = tf.data.Dataset.from_generator(make_generator(train_list),\n", " output_types=(tf.float32, tf.int16),\n", " output_shapes=(tf.TensorShape([SEQUENCE_LENGTH, 2048]), tf.TensorShape([len(LABELS)])))\n", "#train_dataset = train_dataset.batch(16, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)\n", "#reduce batch size because of memory on machine\n", "train_dataset = train_dataset.batch(16, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)\n", "\n", "\n", "\n", "valid_dataset = tf.data.Dataset.from_generator(make_generator(test_list),\n", " output_types=(tf.float32, tf.int16),\n", " output_shapes=(tf.TensorShape([SEQUENCE_LENGTH, 2048]), tf.TensorShape([len(LABELS)])))\n", "#valid_dataset = valid_dataset.batch(16, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)\n", "#reduce batch set because of memory on machine\n", "valid_dataset = valid_dataset.batch(16, drop_remainder=True).prefetch(tf.data.experimental.AUTOTUNE)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(train_dataset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 11: \n", "Setup tensorboard_callback so that will store in a log directory (you change the path) tensorboard information for visualization of training process every 1000 samples passed through traiing. Call model.fit to perform the training on the train_dataset and run for 17 Epochs with the tensorboard_callback defined and using our \"testing data set\" valid_datset for validation" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mylog directory = C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\train_log\n", "C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\train_log\\train\n", "WARNING:tensorflow:From C:\\Users\\Lynne\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py:3794: add_dispatch_support..wrapper (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" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;31m#following call works for tensorflow 1.1.4\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_dataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m17\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalid_dataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[0;32m 778\u001b[0m \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 779\u001b[0m \u001b[0mvalidation_freq\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidation_freq\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 780\u001b[1;33m steps_name='steps_per_epoch')\n\u001b[0m\u001b[0;32m 781\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 782\u001b[0m def evaluate(self,\n", "\u001b[1;32m~\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mmodel_iteration\u001b[1;34m(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)\u001b[0m\n\u001b[0;32m 272\u001b[0m \u001b[1;31m# `ins` can be callable in tf.distribute.Strategy + eager case.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 273\u001b[0m \u001b[0mactual_inputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32melse\u001b[0m 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\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_fetch_callbacks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3294\u001b[0m output_structure = nest.pack_sequence_as(\n", "\u001b[1;32m~\\Anaconda3\\envs\\TensorFlow\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1456\u001b[0m ret = tf_session.TF_SessionRunCallable(self._session._session,\n\u001b[0;32m 1457\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1458\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 1459\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1460\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "\n", "BASE_DATA_PATH = 'C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data'\n", "mylog_dir = os.path.join( BASE_DATA_PATH, \"train_log\")\n", "print(\"Mylog directory = \" + mylog_dir)\n", "tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=mylog_dir, update_freq=1000)\n", "print(os.path.join(mylog_dir, 'train'))\n", "#tensorboard_callback = tf.keras.callbacks.TensorBoard(os.path.join('tmp'), update_freq=1000)\n", "#model.fit(train_dataset, epochs=17, callbacks=[tensorboard_callback], validation_data=valid_dataset)\n", "\n", "\n", "#following call works for tensorflow 1.1.4\n", "model.fit(train_dataset, epochs=17, validation_data=valid_dataset, validation_steps=4, verbose=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 12: save the tensorflow model to an h5 file" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "BASE_DATA_PATH = 'C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data'\n", "model_file = os.path.join(BASE_DATA_PATH, 'my_model.h5')\n", "# Save the entire model to a HDF5 file.\n", "# The '.h5' extension indicates that the model shuold be saved to HDF5.\n", "model.save(model_file) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 13: try to conver the model to tflite --- Support to come 2019 (when?)--Curently LSTM conversion to TFLite NOT supported" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " want to save tflite_fileC:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\my_tflite_model.tflite\n" ] }, { "ename": "AttributeError", "evalue": "type object 'TFLiteConverter' has no attribute 'from_keras_model'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" want to save tflite_file\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mtflite_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m# Convert the model.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mconverter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlite\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTFLiteConverter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_keras_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[0mtflite_model\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconverter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: type object 'TFLiteConverter' has no attribute 'from_keras_model'" ] } ], "source": [ "# FOR VERSION Tensorflow 2.0+\n", "#from tensorflow import lite \n", "tflite_file = os.path.join(BASE_DATA_PATH, 'my_tflite_model.tflite')\n", "print(\" want to save tflite_file\" + tflite_file)\n", "# Convert the model.\n", "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n", "tflite_model = converter.convert()\n", "\n", "\n", "#now save the tflite model to the file\n", "#tflite_model.save(tflite_file) #Note this does not seem to work although in google documentation\n", "open(tflite_file, \"wb\").write(tflite_model)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 14: run evaluation on the test data feature extraction" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Evaluate on test data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 101/Unknown - 1s 850ms/step - loss: 1.3290 - acc: 0.6250 - top_k_categorical_accuracy: 0.875 - 1s 628ms/step - loss: 0.8937 - acc: 0.7188 - top_k_categorical_accuracy: 0.906 - 2s 560ms/step - loss: 0.9143 - acc: 0.6875 - top_k_categorical_accuracy: 0.916 - 2s 530ms/step - loss: 1.0660 - acc: 0.6875 - top_k_categorical_accuracy: 0.921 - 3s 505ms/step - loss: 1.0973 - acc: 0.6875 - top_k_categorical_accuracy: 0.925 - 3s 487ms/step - loss: 1.1684 - acc: 0.6667 - top_k_categorical_accuracy: 0.927 - 3s 478ms/step - loss: 1.1793 - acc: 0.6696 - top_k_categorical_accuracy: 0.928 - 4s 466ms/step - loss: 1.1676 - acc: 0.6797 - top_k_categorical_accuracy: 0.929 - 4s 462ms/step - loss: 1.2038 - acc: 0.6875 - top_k_categorical_accuracy: 0.923 - 5s 458ms/step - loss: 1.2246 - acc: 0.6938 - top_k_categorical_accuracy: 0.925 - 5s 452ms/step - loss: 1.1760 - acc: 0.7045 - top_k_categorical_accuracy: 0.926 - 5s 449ms/step - loss: 1.2205 - acc: 0.6927 - top_k_categorical_accuracy: 0.916 - 6s 450ms/step - loss: 1.2123 - acc: 0.6971 - top_k_categorical_accuracy: 0.913 - 6s 452ms/step - loss: 1.1956 - acc: 0.7054 - top_k_categorical_accuracy: 0.910 - 7s 449ms/step - loss: 1.2007 - acc: 0.7125 - top_k_categorical_accuracy: 0.908 - 7s 452ms/step - loss: 1.2296 - acc: 0.7031 - top_k_categorical_accuracy: 0.898 - 8s 451ms/step - loss: 1.2308 - acc: 0.6949 - top_k_categorical_accuracy: 0.900 - 8s 451ms/step - loss: 1.2001 - acc: 0.7014 - top_k_categorical_accuracy: 0.906 - 9s 450ms/step - loss: 1.2872 - acc: 0.6908 - top_k_categorical_accuracy: 0.901 - 9s 449ms/step - loss: 1.2660 - acc: 0.6938 - top_k_categorical_accuracy: 0.903 - 9s 448ms/step - loss: 1.3139 - acc: 0.6905 - top_k_categorical_accuracy: 0.901 - 10s 446ms/step - loss: 1.3165 - acc: 0.6847 - top_k_categorical_accuracy: 0.90 - 10s 446ms/step - loss: 1.3166 - acc: 0.6848 - top_k_categorical_accuracy: 0.89 - 11s 450ms/step - loss: 1.3136 - acc: 0.6823 - top_k_categorical_accuracy: 0.89 - 11s 451ms/step - loss: 1.3145 - acc: 0.6775 - top_k_categorical_accuracy: 0.89 - 12s 454ms/step - loss: 1.3151 - acc: 0.6779 - top_k_categorical_accuracy: 0.89 - 12s 454ms/step - loss: 1.3174 - acc: 0.6806 - top_k_categorical_accuracy: 0.89 - 13s 456ms/step - loss: 1.3062 - acc: 0.6763 - top_k_categorical_accuracy: 0.89 - 13s 456ms/step - loss: 1.3306 - acc: 0.6746 - top_k_categorical_accuracy: 0.89 - 14s 457ms/step - loss: 1.3056 - acc: 0.6771 - top_k_categorical_accuracy: 0.89 - 14s 460ms/step - loss: 1.3179 - acc: 0.6754 - top_k_categorical_accuracy: 0.89 - 15s 459ms/step - loss: 1.3408 - acc: 0.6758 - top_k_categorical_accuracy: 0.88 - 15s 461ms/step - loss: 1.3485 - acc: 0.6742 - top_k_categorical_accuracy: 0.88 - 16s 460ms/step - loss: 1.3363 - acc: 0.6765 - top_k_categorical_accuracy: 0.88 - 16s 460ms/step - loss: 1.3815 - acc: 0.6661 - top_k_categorical_accuracy: 0.87 - 16s 458ms/step - loss: 1.3664 - acc: 0.6667 - top_k_categorical_accuracy: 0.88 - 17s 458ms/step - loss: 1.3401 - acc: 0.6740 - top_k_categorical_accuracy: 0.88 - 17s 459ms/step - loss: 1.3573 - acc: 0.6711 - top_k_categorical_accuracy: 0.88 - 18s 460ms/step - loss: 1.3433 - acc: 0.6731 - top_k_categorical_accuracy: 0.88 - 18s 462ms/step - loss: 1.3260 - acc: 0.6766 - top_k_categorical_accuracy: 0.88 - 19s 465ms/step - loss: 1.3236 - acc: 0.6753 - top_k_categorical_accuracy: 0.88 - 20s 466ms/step - loss: 1.3542 - acc: 0.6711 - top_k_categorical_accuracy: 0.88 - 20s 466ms/step - loss: 1.3607 - acc: 0.6686 - top_k_categorical_accuracy: 0.88 - 21s 467ms/step - loss: 1.3815 - acc: 0.6619 - top_k_categorical_accuracy: 0.87 - 21s 467ms/step - loss: 1.3789 - acc: 0.6639 - top_k_categorical_accuracy: 0.88 - 21s 467ms/step - loss: 1.3918 - acc: 0.6603 - top_k_categorical_accuracy: 0.87 - 22s 467ms/step - loss: 1.3893 - acc: 0.6609 - top_k_categorical_accuracy: 0.87 - 22s 467ms/step - loss: 1.3754 - acc: 0.6641 - top_k_categorical_accuracy: 0.88 - 23s 467ms/step - loss: 1.3558 - acc: 0.6684 - top_k_categorical_accuracy: 0.88 - 23s 466ms/step - loss: 1.3659 - acc: 0.6650 - top_k_categorical_accuracy: 0.88 - 24s 466ms/step - loss: 1.3558 - acc: 0.6679 - top_k_categorical_accuracy: 0.88 - 24s 465ms/step - loss: 1.3436 - acc: 0.6683 - top_k_categorical_accuracy: 0.88 - 25s 465ms/step - loss: 1.3371 - acc: 0.6686 - top_k_categorical_accuracy: 0.88 - 25s 464ms/step - loss: 1.3412 - acc: 0.6667 - top_k_categorical_accuracy: 0.88 - 26s 465ms/step - loss: 1.3554 - acc: 0.6648 - top_k_categorical_accuracy: 0.88 - 26s 467ms/step - loss: 1.3564 - acc: 0.6641 - top_k_categorical_accuracy: 0.88 - 27s 468ms/step - loss: 1.3653 - acc: 0.6590 - top_k_categorical_accuracy: 0.88 - 27s 470ms/step - loss: 1.3846 - acc: 0.6562 - top_k_categorical_accuracy: 0.87 - 28s 470ms/step - loss: 1.3815 - acc: 0.6557 - top_k_categorical_accuracy: 0.87 - 28s 471ms/step - loss: 1.3955 - acc: 0.6531 - top_k_categorical_accuracy: 0.87 - 29s 471ms/step - loss: 1.3988 - acc: 0.6527 - top_k_categorical_accuracy: 0.87 - 29s 470ms/step - loss: 1.3906 - acc: 0.6532 - top_k_categorical_accuracy: 0.87 - 30s 470ms/step - loss: 1.3778 - acc: 0.6538 - top_k_categorical_accuracy: 0.87 - 30s 471ms/step - loss: 1.3792 - acc: 0.6533 - top_k_categorical_accuracy: 0.87 - 31s 471ms/step - loss: 1.3762 - acc: 0.6519 - top_k_categorical_accuracy: 0.87 - 31s 472ms/step - loss: 1.3786 - acc: 0.6506 - top_k_categorical_accuracy: 0.87 - 32s 474ms/step - loss: 1.3733 - acc: 0.6530 - top_k_categorical_accuracy: 0.88 - 32s 474ms/step - loss: 1.3842 - acc: 0.6489 - top_k_categorical_accuracy: 0.88 - 33s 474ms/step - loss: 1.3929 - acc: 0.6476 - top_k_categorical_accuracy: 0.88 - 33s 473ms/step - loss: 1.3882 - acc: 0.6482 - top_k_categorical_accuracy: 0.88 - 34s 473ms/step - loss: 1.3807 - acc: 0.6479 - top_k_categorical_accuracy: 0.88 - 34s 472ms/step - loss: 1.3830 - acc: 0.6484 - top_k_categorical_accuracy: 0.88 - 34s 472ms/step - loss: 1.3873 - acc: 0.6490 - top_k_categorical_accuracy: 0.88 - 35s 471ms/step - loss: 1.3881 - acc: 0.6478 - top_k_categorical_accuracy: 0.88 - 35s 471ms/step - loss: 1.3920 - acc: 0.6458 - top_k_categorical_accuracy: 0.88 - 36s 471ms/step - loss: 1.3861 - acc: 0.6464 - top_k_categorical_accuracy: 0.88 - 36s 470ms/step - loss: 1.3892 - acc: 0.6469 - top_k_categorical_accuracy: 0.88 - 37s 470ms/step - loss: 1.3902 - acc: 0.6466 - top_k_categorical_accuracy: 0.88 - 37s 469ms/step - loss: 1.3868 - acc: 0.6472 - top_k_categorical_accuracy: 0.88 - 38s 469ms/step - loss: 1.3823 - acc: 0.6484 - top_k_categorical_accuracy: 0.88 - 38s 469ms/step - loss: 1.3880 - acc: 0.6474 - top_k_categorical_accuracy: 0.87 - 38s 469ms/step - loss: 1.3837 - acc: 0.6463 - top_k_categorical_accuracy: 0.87 - 39s 469ms/step - loss: 1.3755 - acc: 0.6483 - top_k_categorical_accuracy: 0.88 - 39s 468ms/step - loss: 1.3775 - acc: 0.6481 - top_k_categorical_accuracy: 0.88 - 40s 469ms/step - loss: 1.3814 - acc: 0.6471 - top_k_categorical_accuracy: 0.88 - 40s 468ms/step - loss: 1.3905 - acc: 0.6439 - top_k_categorical_accuracy: 0.88 - 41s 469ms/step - loss: 1.3864 - acc: 0.6444 - top_k_categorical_accuracy: 0.88 - 41s 468ms/step - loss: 1.3910 - acc: 0.6428 - top_k_categorical_accuracy: 0.88 - 42s 468ms/step - loss: 1.3854 - acc: 0.6426 - top_k_categorical_accuracy: 0.88 - 42s 468ms/step - loss: 1.3863 - acc: 0.6424 - top_k_categorical_accuracy: 0.88 - 43s 468ms/step - loss: 1.3809 - acc: 0.6422 - top_k_categorical_accuracy: 0.88 - 43s 468ms/step - loss: 1.3831 - acc: 0.6427 - top_k_categorical_accuracy: 0.88 - 44s 469ms/step - loss: 1.4034 - acc: 0.6411 - top_k_categorical_accuracy: 0.88 - 44s 469ms/step - loss: 1.4019 - acc: 0.6403 - top_k_categorical_accuracy: 0.88 - 45s 469ms/step - loss: 1.4023 - acc: 0.6401 - top_k_categorical_accuracy: 0.88 - 45s 468ms/step - loss: 1.4015 - acc: 0.6400 - top_k_categorical_accuracy: 0.88 - 45s 468ms/step - loss: 1.4053 - acc: 0.6385 - top_k_categorical_accuracy: 0.88 - 46s 469ms/step - loss: 1.4079 - acc: 0.6390 - top_k_categorical_accuracy: 0.88 - 46s 469ms/step - loss: 1.4059 - acc: 0.6395 - top_k_categorical_accuracy: 0.88 - 47s 469ms/step - loss: 1.3988 - acc: 0.6406 - top_k_categorical_accuracy: 0.88 - 47s 469ms/step - loss: 1.3940 - acc: 0.6411 - top_k_categorical_accuracy: 0.88 202/Unknown - 48s 469ms/step - loss: 1.3970 - acc: 0.6415 - top_k_categorical_accuracy: 0.88 - 48s 468ms/step - loss: 1.4043 - acc: 0.6396 - top_k_categorical_accuracy: 0.88 - 49s 468ms/step - loss: 1.4045 - acc: 0.6406 - top_k_categorical_accuracy: 0.88 - 49s 468ms/step - loss: 1.4126 - acc: 0.6405 - top_k_categorical_accuracy: 0.88 - 50s 467ms/step - loss: 1.4227 - acc: 0.6397 - top_k_categorical_accuracy: 0.87 - 50s 467ms/step - loss: 1.4211 - acc: 0.6396 - top_k_categorical_accuracy: 0.87 - 50s 467ms/step - loss: 1.4191 - acc: 0.6395 - top_k_categorical_accuracy: 0.87 - 51s 466ms/step - loss: 1.4184 - acc: 0.6388 - top_k_categorical_accuracy: 0.87 - 51s 466ms/step - loss: 1.4157 - acc: 0.6398 - top_k_categorical_accuracy: 0.87 - 52s 466ms/step - loss: 1.4190 - acc: 0.6391 - top_k_categorical_accuracy: 0.87 - 52s 465ms/step - loss: 1.4272 - acc: 0.6390 - top_k_categorical_accuracy: 0.87 - 53s 465ms/step - loss: 1.4268 - acc: 0.6377 - top_k_categorical_accuracy: 0.88 - 53s 465ms/step - loss: 1.4264 - acc: 0.6382 - top_k_categorical_accuracy: 0.87 - 53s 465ms/step - loss: 1.4276 - acc: 0.6375 - top_k_categorical_accuracy: 0.87 - 54s 465ms/step - loss: 1.4261 - acc: 0.6379 - top_k_categorical_accuracy: 0.87 - 54s 465ms/step - loss: 1.4242 - acc: 0.6384 - top_k_categorical_accuracy: 0.87 - 55s 465ms/step - loss: 1.4283 - acc: 0.6377 - top_k_categorical_accuracy: 0.87 - 55s 465ms/step - loss: 1.4273 - acc: 0.6376 - top_k_categorical_accuracy: 0.87 - 56s 465ms/step - loss: 1.4272 - acc: 0.6385 - top_k_categorical_accuracy: 0.87 - 56s 465ms/step - loss: 1.4260 - acc: 0.6389 - top_k_categorical_accuracy: 0.87 - 57s 464ms/step - loss: 1.4216 - acc: 0.6388 - top_k_categorical_accuracy: 0.87 - 57s 464ms/step - loss: 1.4241 - acc: 0.6377 - top_k_categorical_accuracy: 0.87 - 58s 464ms/step - loss: 1.4228 - acc: 0.6391 - top_k_categorical_accuracy: 0.87 - 58s 464ms/step - loss: 1.4160 - acc: 0.6410 - top_k_categorical_accuracy: 0.87 - 58s 464ms/step - loss: 1.4114 - acc: 0.6414 - top_k_categorical_accuracy: 0.87 - 59s 464ms/step - loss: 1.4103 - acc: 0.6412 - top_k_categorical_accuracy: 0.87 - 59s 465ms/step - loss: 1.4083 - acc: 0.6416 - top_k_categorical_accuracy: 0.87 - 60s 464ms/step - loss: 1.4118 - acc: 0.6420 - top_k_categorical_accuracy: 0.87 - 60s 464ms/step - loss: 1.4092 - acc: 0.6423 - top_k_categorical_accuracy: 0.87 - 61s 464ms/step - loss: 1.4198 - acc: 0.6407 - top_k_categorical_accuracy: 0.87 - 61s 465ms/step - loss: 1.4143 - acc: 0.6430 - top_k_categorical_accuracy: 0.88 - 62s 465ms/step - loss: 1.4281 - acc: 0.6419 - top_k_categorical_accuracy: 0.87 - 62s 466ms/step - loss: 1.4316 - acc: 0.6423 - top_k_categorical_accuracy: 0.87 - 63s 467ms/step - loss: 1.4258 - acc: 0.6440 - top_k_categorical_accuracy: 0.87 - 63s 467ms/step - loss: 1.4222 - acc: 0.6443 - top_k_categorical_accuracy: 0.88 - 64s 466ms/step - loss: 1.4280 - acc: 0.6432 - top_k_categorical_accuracy: 0.87 - 64s 466ms/step - loss: 1.4258 - acc: 0.6440 - top_k_categorical_accuracy: 0.88 - 65s 466ms/step - loss: 1.4260 - acc: 0.6439 - top_k_categorical_accuracy: 0.87 - 65s 465ms/step - loss: 1.4251 - acc: 0.6438 - top_k_categorical_accuracy: 0.87 - 66s 465ms/step - loss: 1.4220 - acc: 0.6441 - top_k_categorical_accuracy: 0.88 - 66s 465ms/step - loss: 1.4206 - acc: 0.6439 - top_k_categorical_accuracy: 0.88 - 66s 465ms/step - loss: 1.4256 - acc: 0.6429 - top_k_categorical_accuracy: 0.87 - 67s 464ms/step - loss: 1.4372 - acc: 0.6411 - top_k_categorical_accuracy: 0.87 - 67s 464ms/step - loss: 1.4365 - acc: 0.6414 - top_k_categorical_accuracy: 0.87 - 68s 464ms/step - loss: 1.4383 - acc: 0.6404 - top_k_categorical_accuracy: 0.87 - 68s 463ms/step - loss: 1.4357 - acc: 0.6403 - top_k_categorical_accuracy: 0.87 - 69s 463ms/step - loss: 1.4426 - acc: 0.6406 - top_k_categorical_accuracy: 0.87 - 69s 463ms/step - loss: 1.4402 - acc: 0.6414 - top_k_categorical_accuracy: 0.87 - 69s 463ms/step - loss: 1.4377 - acc: 0.6421 - top_k_categorical_accuracy: 0.87 - 70s 462ms/step - loss: 1.4374 - acc: 0.6420 - top_k_categorical_accuracy: 0.87 - 70s 462ms/step - loss: 1.4342 - acc: 0.6427 - top_k_categorical_accuracy: 0.87 - 71s 462ms/step - loss: 1.4376 - acc: 0.6417 - top_k_categorical_accuracy: 0.87 - 71s 462ms/step - loss: 1.4461 - acc: 0.6408 - top_k_categorical_accuracy: 0.87 - 72s 461ms/step - loss: 1.4452 - acc: 0.6411 - top_k_categorical_accuracy: 0.87 - 72s 461ms/step - loss: 1.4407 - acc: 0.6414 - top_k_categorical_accuracy: 0.87 - 72s 461ms/step - loss: 1.4425 - acc: 0.6409 - top_k_categorical_accuracy: 0.87 - 73s 461ms/step - loss: 1.4404 - acc: 0.6420 - top_k_categorical_accuracy: 0.87 - 73s 460ms/step - loss: 1.4429 - acc: 0.6419 - top_k_categorical_accuracy: 0.87 - 74s 460ms/step - loss: 1.4464 - acc: 0.6410 - top_k_categorical_accuracy: 0.87 - 74s 460ms/step - loss: 1.4414 - acc: 0.6417 - top_k_categorical_accuracy: 0.87 - 75s 460ms/step - loss: 1.4380 - acc: 0.6424 - top_k_categorical_accuracy: 0.87 - 75s 460ms/step - loss: 1.4375 - acc: 0.6430 - top_k_categorical_accuracy: 0.87 - 75s 460ms/step - loss: 1.4407 - acc: 0.6425 - top_k_categorical_accuracy: 0.87 - 76s 460ms/step - loss: 1.4388 - acc: 0.6428 - top_k_categorical_accuracy: 0.87 - 76s 460ms/step - loss: 1.4431 - acc: 0.6423 - top_k_categorical_accuracy: 0.87 - 77s 460ms/step - loss: 1.4478 - acc: 0.6411 - top_k_categorical_accuracy: 0.87 - 77s 460ms/step - loss: 1.4492 - acc: 0.6406 - top_k_categorical_accuracy: 0.87 - 78s 460ms/step - loss: 1.4550 - acc: 0.6402 - top_k_categorical_accuracy: 0.87 - 78s 460ms/step - loss: 1.4522 - acc: 0.6404 - top_k_categorical_accuracy: 0.87 - 79s 460ms/step - loss: 1.4495 - acc: 0.6411 - top_k_categorical_accuracy: 0.87 - 79s 460ms/step - loss: 1.4497 - acc: 0.6414 - top_k_categorical_accuracy: 0.87 - 79s 460ms/step - loss: 1.4480 - acc: 0.6405 - top_k_categorical_accuracy: 0.87 - 80s 459ms/step - loss: 1.4463 - acc: 0.6404 - top_k_categorical_accuracy: 0.87 - 80s 459ms/step - loss: 1.4426 - acc: 0.6418 - top_k_categorical_accuracy: 0.87 - 81s 459ms/step - loss: 1.4428 - acc: 0.6413 - top_k_categorical_accuracy: 0.87 - 81s 459ms/step - loss: 1.4424 - acc: 0.6416 - top_k_categorical_accuracy: 0.87 - 82s 459ms/step - loss: 1.4385 - acc: 0.6419 - top_k_categorical_accuracy: 0.87 - 82s 458ms/step - loss: 1.4380 - acc: 0.6418 - top_k_categorical_accuracy: 0.87 - 82s 458ms/step - loss: 1.4352 - acc: 0.6424 - top_k_categorical_accuracy: 0.87 - 83s 458ms/step - loss: 1.4336 - acc: 0.6423 - top_k_categorical_accuracy: 0.87 - 83s 458ms/step - loss: 1.4342 - acc: 0.6415 - top_k_categorical_accuracy: 0.87 - 84s 458ms/step - loss: 1.4324 - acc: 0.6421 - top_k_categorical_accuracy: 0.87 - 84s 458ms/step - loss: 1.4329 - acc: 0.6420 - top_k_categorical_accuracy: 0.87 - 85s 457ms/step - loss: 1.4382 - acc: 0.6426 - top_k_categorical_accuracy: 0.87 - 85s 457ms/step - loss: 1.4385 - acc: 0.6428 - top_k_categorical_accuracy: 0.87 - 85s 457ms/step - loss: 1.4391 - acc: 0.6424 - top_k_categorical_accuracy: 0.87 - 86s 457ms/step - loss: 1.4385 - acc: 0.6423 - top_k_categorical_accuracy: 0.87 - 86s 457ms/step - loss: 1.4379 - acc: 0.6419 - top_k_categorical_accuracy: 0.87 - 87s 457ms/step - loss: 1.4346 - acc: 0.6424 - top_k_categorical_accuracy: 0.87 - 87s 456ms/step - loss: 1.4364 - acc: 0.6420 - top_k_categorical_accuracy: 0.87 - 88s 456ms/step - loss: 1.4358 - acc: 0.6413 - top_k_categorical_accuracy: 0.87 - 88s 456ms/step - loss: 1.4370 - acc: 0.6412 - top_k_categorical_accuracy: 0.87 - 88s 456ms/step - loss: 1.4384 - acc: 0.6401 - top_k_categorical_accuracy: 0.88 - 89s 456ms/step - loss: 1.4391 - acc: 0.6401 - top_k_categorical_accuracy: 0.87 - 89s 456ms/step - loss: 1.4373 - acc: 0.6403 - top_k_categorical_accuracy: 0.88 - 90s 456ms/step - loss: 1.4420 - acc: 0.6396 - top_k_categorical_accuracy: 0.88 - 90s 456ms/step - loss: 1.4426 - acc: 0.6389 - top_k_categorical_accuracy: 0.88 - 91s 456ms/step - loss: 1.4409 - acc: 0.6398 - top_k_categorical_accuracy: 0.88 - 91s 456ms/step - loss: 1.4424 - acc: 0.6397 - top_k_categorical_accuracy: 0.88 - 92s 456ms/step - loss: 1.4481 - acc: 0.6393 - top_k_categorical_accuracy: 0.87 - 92s 456ms/step - loss: 1.4489 - acc: 0.6392 - top_k_categorical_accuracy: 0.8790 236/Unknown - 92s 456ms/step - loss: 1.4515 - acc: 0.6385 - top_k_categorical_accuracy: 0.87 - 93s 456ms/step - loss: 1.4533 - acc: 0.6385 - top_k_categorical_accuracy: 0.87 - 93s 456ms/step - loss: 1.4501 - acc: 0.6390 - top_k_categorical_accuracy: 0.87 - 94s 456ms/step - loss: 1.4490 - acc: 0.6387 - top_k_categorical_accuracy: 0.87 - 94s 456ms/step - loss: 1.4474 - acc: 0.6389 - top_k_categorical_accuracy: 0.87 - 95s 456ms/step - loss: 1.4467 - acc: 0.6391 - top_k_categorical_accuracy: 0.87 - 95s 456ms/step - loss: 1.4450 - acc: 0.6394 - top_k_categorical_accuracy: 0.87 - 96s 455ms/step - loss: 1.4439 - acc: 0.6396 - top_k_categorical_accuracy: 0.87 - 96s 455ms/step - loss: 1.4495 - acc: 0.6395 - top_k_categorical_accuracy: 0.87 - 96s 455ms/step - loss: 1.4474 - acc: 0.6400 - top_k_categorical_accuracy: 0.87 - 97s 455ms/step - loss: 1.4512 - acc: 0.6391 - top_k_categorical_accuracy: 0.87 - 97s 455ms/step - loss: 1.4487 - acc: 0.6393 - top_k_categorical_accuracy: 0.87 - 98s 455ms/step - loss: 1.4478 - acc: 0.6392 - top_k_categorical_accuracy: 0.87 - 98s 454ms/step - loss: 1.4519 - acc: 0.6389 - top_k_categorical_accuracy: 0.87 - 99s 454ms/step - loss: 1.4497 - acc: 0.6388 - top_k_categorical_accuracy: 0.87 - 99s 454ms/step - loss: 1.4496 - acc: 0.6393 - top_k_categorical_accuracy: 0.87 - 99s 454ms/step - loss: 1.4468 - acc: 0.6393 - top_k_categorical_accuracy: 0.87 - 100s 454ms/step - loss: 1.4482 - acc: 0.6389 - top_k_categorical_accuracy: 0.877 - 100s 454ms/step - loss: 1.4458 - acc: 0.6391 - top_k_categorical_accuracy: 0.878 - 101s 454ms/step - loss: 1.4420 - acc: 0.6402 - top_k_categorical_accuracy: 0.878 - 101s 454ms/step - loss: 1.4503 - acc: 0.6396 - top_k_categorical_accuracy: 0.877 - 102s 453ms/step - loss: 1.4499 - acc: 0.6395 - top_k_categorical_accuracy: 0.877 - 102s 453ms/step - loss: 1.4502 - acc: 0.6386 - top_k_categorical_accuracy: 0.877 - 102s 453ms/step - loss: 1.4526 - acc: 0.6383 - top_k_categorical_accuracy: 0.876 - 103s 453ms/step - loss: 1.4548 - acc: 0.6382 - top_k_categorical_accuracy: 0.876 - 103s 453ms/step - loss: 1.4608 - acc: 0.6373 - top_k_categorical_accuracy: 0.875 - 104s 453ms/step - loss: 1.4578 - acc: 0.6378 - top_k_categorical_accuracy: 0.875 - 104s 453ms/step - loss: 1.4589 - acc: 0.6370 - top_k_categorical_accuracy: 0.874 - 104s 452ms/step - loss: 1.4554 - acc: 0.6380 - top_k_categorical_accuracy: 0.875 - 105s 452ms/step - loss: 1.4565 - acc: 0.6379 - top_k_categorical_accuracy: 0.875 - 105s 452ms/step - loss: 1.4585 - acc: 0.6379 - top_k_categorical_accuracy: 0.874 - 106s 452ms/step - loss: 1.4575 - acc: 0.6384 - top_k_categorical_accuracy: 0.875 - 106s 452ms/step - loss: 1.4553 - acc: 0.6388 - top_k_categorical_accuracy: 0.875 - 107s 452ms/step - loss: 1.4540 - acc: 0.6390 - top_k_categorical_accuracy: 0.875 - 107s 452ms/step - loss: 1.4540 - acc: 0.6390 - top_k_categorical_accuracy: 0.8755test loss, test acc: [1.4539773562702083, 0.639036, 0.87552965]\n" ] } ], "source": [ "# evaluate the test data using model\n", "\n", "\n", "# Evaluate the model on the test data using `evaluate`\n", "print('\\n# Evaluate on test data')\n", "\n", "# NOTE: should have separate test data but, only have validation data\n", "#results = model.evaluate_generator(val_data_gen, verbose=1)\n", "results = model.evaluate(valid_dataset, verbose=1)\n", "print('test loss, test acc:', results)\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STEP 15: Run predictions on the test data feature extracted" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "# Generate predictions \n", " 236/Unknown - 1s 599ms/ste - 1s 512ms/ste - 1s 484ms/ste - 2s 467ms/ste - 2s 457ms/ste - 3s 450ms/ste - 3s 445ms/ste - 4s 442ms/ste - 4s 441ms/ste - 4s 440ms/ste - 5s 438ms/ste - 5s 436ms/ste - 6s 433ms/ste - 6s 432ms/ste - 6s 432ms/ste - 7s 431ms/ste - 7s 430ms/ste - 8s 430ms/ste - 8s 429ms/ste - 9s 429ms/ste - 9s 428ms/ste - 9s 427ms/ste - 10s 426ms/st - 10s 427ms/st - 11s 427ms/st - 11s 426ms/st - 11s 426ms/st - 12s 426ms/st - 12s 425ms/st - 13s 424ms/st - 13s 424ms/st - 14s 424ms/st - 14s 428ms/st - 15s 429ms/st - 15s 430ms/st - 15s 430ms/st - 16s 431ms/st - 16s 432ms/st - 17s 432ms/st - 17s 432ms/st - 18s 432ms/st - 18s 433ms/st - 19s 434ms/st - 19s 434ms/st - 20s 434ms/st - 20s 433ms/st - 20s 433ms/st - 21s 433ms/st - 21s 433ms/st - 22s 432ms/st - 22s 432ms/st - 22s 432ms/st - 23s 431ms/st - 23s 431ms/st - 24s 431ms/st - 24s 431ms/st - 25s 431ms/st - 25s 431ms/st - 25s 431ms/st - 26s 430ms/st - 26s 430ms/st - 27s 430ms/st - 27s 430ms/st - 27s 429ms/st - 28s 429ms/st - 28s 429ms/st - 29s 429ms/st - 29s 429ms/st - 30s 429ms/st - 30s 430ms/st - 31s 430ms/st - 31s 430ms/st - 31s 430ms/st - 32s 431ms/st - 32s 431ms/st - 33s 431ms/st - 33s 431ms/st - 34s 432ms/st - 34s 432ms/st - 35s 432ms/st - 35s 432ms/st - 35s 432ms/st - 36s 432ms/st - 36s 432ms/st - 37s 432ms/st - 37s 432ms/st - 38s 431ms/st - 38s 431ms/st - 38s 431ms/st - 39s 431ms/st - 39s 431ms/st - 40s 431ms/st - 40s 431ms/st - 40s 431ms/st - 41s 431ms/st - 41s 431ms/st - 42s 430ms/st - 42s 430ms/st - 43s 430ms/st - 43s 430ms/st - 43s 430ms/st - 44s 430ms/st - 44s 430ms/st - 45s 430ms/st - 45s 430ms/st - 46s 429ms/st - 46s 430ms/st - 46s 430ms/st - 47s 430ms/st - 47s 431ms/st - 48s 431ms/st - 48s 431ms/st - 49s 431ms/st - 49s 431ms/st - 50s 431ms/st - 50s 432ms/st - 51s 432ms/st - 51s 432ms/st - 51s 432ms/st - 52s 432ms/st - 52s 432ms/st - 53s 431ms/st - 53s 431ms/st - 53s 431ms/st - 54s 431ms/st - 54s 431ms/st - 55s 431ms/st - 55s 431ms/st - 56s 431ms/st - 56s 431ms/st - 56s 431ms/st - 57s 431ms/st - 57s 430ms/st - 58s 431ms/st - 58s 431ms/st - 59s 431ms/st - 59s 431ms/st - 59s 431ms/st - 60s 431ms/st - 60s 431ms/st - 61s 431ms/st - 61s 431ms/st - 62s 431ms/st - 62s 431ms/st - 63s 432ms/st - 63s 432ms/st - 63s 432ms/st - 64s 432ms/st - 64s 432ms/st - 65s 432ms/st - 65s 433ms/st - 66s 433ms/st - 66s 433ms/st - 67s 433ms/st - 67s 433ms/st - 68s 433ms/st - 68s 433ms/st - 68s 433ms/st - 69s 433ms/st - 69s 433ms/st - 70s 432ms/st - 70s 432ms/st - 70s 432ms/st - 71s 432ms/st - 71s 432ms/st - 72s 432ms/st - 72s 432ms/st - 73s 432ms/st - 73s 432ms/st - 73s 432ms/st - 74s 432ms/st - 74s 431ms/st - 75s 431ms/st - 75s 431ms/st - 75s 431ms/st - 76s 431ms/st - 76s 431ms/st - 77s 431ms/st - 77s 431ms/st - 78s 431ms/st - 78s 431ms/st - 78s 431ms/st - 79s 431ms/st - 79s 431ms/st - 80s 431ms/st - 80s 431ms/st - 81s 431ms/st - 81s 431ms/st - 82s 431ms/st - 82s 432ms/st - 82s 432ms/st - 83s 432ms/st - 83s 432ms/st - 84s 432ms/st - 84s 432ms/st - 85s 432ms/st - 85s 432ms/st - 85s 431ms/st - 86s 432ms/st - 86s 431ms/st - 87s 431ms/st - 87s 431ms/st - 88s 431ms/st - 88s 431ms/st - 88s 431ms/st - 89s 431ms/st - 89s 431ms/st - 90s 431ms/st - 90s 431ms/st - 90s 431ms/st - 91s 431ms/st - 91s 431ms/st - 92s 431ms/st - 92s 431ms/st - 93s 431ms/st - 93s 431ms/st - 93s 430ms/st - 94s 431ms/st - 94s 431ms/st - 95s 431ms/st - 95s 431ms/st - 96s 431ms/st - 96s 431ms/st - 97s 431ms/st - 97s 431ms/st - 97s 431ms/st - 98s 431ms/st - 98s 431ms/st - 99s 432ms/st - 99s 431ms/st - 100s 431ms/ste - 100s 431ms/ste - 100s 431ms/ste - 101s 431ms/ste - 101s 431ms/ste - 102s 431ms/ste - 102s 431ms/step" ] } ], "source": [ "# make predictions\n", "\n", "# Generate predictions (probabilities -- the output of the last layer)\n", "# on new data using `predict`\n", "print('\\n# Generate predictions ')\n", "predictions = model.predict(valid_dataset, verbose=1 )\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "predictions shape: (3776, 101)\n", "[[1.0961332e-16 6.6839920e-15 1.1504911e-25 ... 4.0068742e-31\n", " 1.5352553e-28 2.1056621e-28]\n", " [1.4465630e-05 1.7060614e-05 4.8076676e-04 ... 6.6043017e-03\n", " 2.6113707e-05 4.9992286e-05]\n", " [3.1131844e-08 6.6483857e-08 4.9871995e-10 ... 1.8386590e-12\n", " 2.3222468e-10 6.1724608e-09]\n", " ...\n", " [3.0067618e-04 1.5095937e-06 2.1350641e-15 ... 7.1118018e-14\n", " 2.5027019e-08 2.5671815e-13]\n", " [1.5635610e-08 1.7025616e-06 1.8315610e-09 ... 2.1071238e-10\n", " 1.6627087e-07 7.9437429e-10]\n", " [5.2210744e-03 1.5661175e-07 3.6163006e-09 ... 4.0324247e-11\n", " 5.2803464e-09 8.7386461e-12]]\n", "3776\n" ] } ], "source": [ "#print out prediction info for validation data set (as do not have separate test data set)\n", "print('predictions shape:', predictions.shape)\n", "print(predictions)\n", "print(len(predictions))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OPTIONAL : reload from h5 file & run some predictions" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:Sequential models without an `input_shape` passed to the first layer cannot reload their optimizer state. As a result, your model isstarting with a freshly initialized optimizer.\n", "Model: \"sequential_3\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "masking_3 (Masking) multiple 0 \n", "_________________________________________________________________\n", "lstm_3 (LSTM) multiple 5244928 \n", "_________________________________________________________________\n", "dense_6 (Dense) multiple 131328 \n", "_________________________________________________________________\n", "dropout_3 (Dropout) multiple 0 \n", "_________________________________________________________________\n", "dense_7 (Dense) multiple 25957 \n", "=================================================================\n", "Total params: 5,402,213\n", "Trainable params: 5,402,213\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "\n", "# Generate predictions \n", " 9/Unknown - 1s 1s/st - 2s 845ms/ste - 2s 741ms/ste - 3s 692ms/ste - 3s 654ms/ste - 4s 631ms/ste - 4s 616ms/ste - 5s 603ms/ste - 5s 607ms/ste - 5s 607ms/step" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;31m# see https://github.com/tensorflow/tensorflow/issues/30892\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\n# Generate predictions '\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalid_dataset\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 19\u001b[0m \u001b[1;31m#print out prediction info for validation data set (as do not have separate test data set)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'predictions shape:'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m 907\u001b[0m \u001b[0mmax_queue_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 908\u001b[0m \u001b[0mworkers\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 909\u001b[1;33m use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[0;32m 910\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 911\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mreset_metrics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, model, x, batch_size, verbose, steps, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m 460\u001b[0m return self._model_iteration(\n\u001b[0;32m 461\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mModeKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPREDICT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m steps=steps, callbacks=callbacks, **kwargs)\n\u001b[0m\u001b[0;32m 463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2.py\u001b[0m in \u001b[0;36m_model_iteration\u001b[1;34m(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, **kwargs)\u001b[0m\n\u001b[0;32m 442\u001b[0m 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num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[0;32m 121\u001b[0m step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[0;32m 122\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 123\u001b[1;33m \u001b[0mbatch_outs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 124\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOutOfRangeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 125\u001b[0m \u001b[1;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\python36\\lib\\site-packages\\tensorflow_core\\python\\keras\\engine\\training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[1;34m(input_fn)\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[1;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 85\u001b[0m return nest.map_structure(_non_none_constant_value,\n\u001b[1;32m---> 86\u001b[1;33m distributed_function(input_fn))\n\u001b[0m\u001b[0;32m 87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 88\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mc:\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m 455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 456\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[1;33m=\u001b[0m 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So we can\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[1;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 494\u001b[1;33m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 495\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 496\u001b[0m raise ValueError(\"Creating variables on a non-first call to a function\"\n", "\u001b[1;32mc:\\python36\\lib\\site-packages\\tensorflow_core\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1821\u001b[0m \u001b[1;34m\"\"\"Calls a graph function specialized to the inputs.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1822\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1823\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1824\u001b[0m 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"\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "#VERSION TF 2.*\n", "# Save the entire model as a SavedModel.\n", "BASE_DATA_PATH = 'C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data' \n", "\n", "#load the previously saved h5 model\n", "# try to reload the saved h5 file\n", "# Recreate the exact same model, including its weights and the optimizer\n", "\n", "model_file = os.path.join(BASE_DATA_PATH, 'my_model.h5')\n", "model = tf.keras.models.load_model(model_file)\n", "# Show the model architecture\n", "model.summary()\n", "\n", "#for some reason the input layer not set when doing a model reload so says to either recall fit (which is ridiculuous)\n", "# or call predict(). To manually set the shapes, call model._set_inputs(inputs).\n", "# see https://github.com/tensorflow/tensorflow/issues/30892\n", "print('\\n# Generate predictions ')\n", "predictions = model.predict(valid_dataset, verbose=1 )\n", "#print out prediction info for validation data set (as do not have separate test data set)\n", "print('predictions shape:', predictions.shape)\n", "print(predictions)\n", "print(len(predictions))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential_3\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "masking_3 (Masking) multiple 0 \n", "_________________________________________________________________\n", "lstm_3 (LSTM) multiple 5244928 \n", "_________________________________________________________________\n", "dense_6 (Dense) multiple 131328 \n", "_________________________________________________________________\n", "dropout_3 (Dropout) multiple 0 \n", "_________________________________________________________________\n", "dense_7 (Dense) multiple 25957 \n", "=================================================================\n", "Total params: 5,402,213\n", "Trainable params: 5,402,213\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## OPTIONAL: Save to a SavedModel" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " path exists=True\n" ] } ], "source": [ "#create directory to save the SavedModel\n", "#setup directories\n", "saved_model_dir1 = os.path.join(BASE_DATA_PATH, 'saved_model')\n", "print(\" path exists=\" + str(os.path.exists(saved_model_dir1)))\n", "if(os.path.exists(saved_model_dir1) == False):\n", " print(\" creating\" + str(saved_model_dir1))\n", " os.mkdir(saved_model_dir1)\n", " \n", "saved_model_dir = os.path.join(BASE_DATA_PATH, 'saved_model\\LSTM_SavedModel') \n", "if(os.path.exists(saved_model_dir) == False):\n", " print(\" creating\" + str(saved_model_dir))\n", " os.mkdir(saved_model_dir)\n", " \n" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": true }, "outputs": [ { "ename": "OSError", "evalue": "Unable to create file (unable to open file: name = 'C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\saved_model\\LSTM_SavedModel', errno = 13, error message = 'Permission denied', flags = 13, o_flags = 302)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m 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userblock_size,\n\u001b[0;32m 407\u001b[0m \u001b[0mfapl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmake_fcpl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 408\u001b[1;33m swmr=swmr)\n\u001b[0m\u001b[0;32m 409\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 410\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlibver\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\h5py\\_hl\\files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[1;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[0;32m 177\u001b[0m \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m 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= 'C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\saved_model\\LSTM_SavedModel', errno = 13, error message = 'Permission denied', flags = 13, o_flags = 302)" ] } ], "source": [ "#VERSION TF 2.* - not working\n", "#save model as SavedModel - tensorflow 2.*\n", " \n", "model.save(saved_model_dir) \n", "\n", "\n", "#save SavedModel using different call\n", "#tf.saved_model.save(model, saved_model_dir)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'saved_model_dir' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m#VERSION TF 1.14 -working\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#save model as SavedModel - tensorflow 1.14\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexport_saved_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaved_model_dir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mNameError\u001b[0m: name 'saved_model_dir' is not defined" ] } ], "source": [ "#VERSION TF 1.14 -working\n", "#save model as SavedModel - tensorflow 1.14\n", "tf.keras.experimental.export_saved_model(model, saved_model_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optional: Predict on a single video clip (stored in file)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "unexpected EOF while parsing (, line 5)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m model = tf.keras.experimental.load_from_saved_model(saved_model_dir)\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unexpected EOF while parsing\n" ] } ], "source": [ "#STEP 0: if LSTM model not loaded then load the file\n", "print(\"loading from SavedModel in \" + str( #tf.keras.experimental.load_from_saved_model(directory)))\n", "\n", "#tensorflow 1.14 call\n", "model = tf.keras.experimental.load_from_saved_model(saved_model_dir)\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\testing_video\\myvideo.avi\n", "\n" ] } ], "source": [ "#STEP 1: create an DataSet using a generator function to extract correct number of frames from a video clip\n", "BASE_DATA_PATH = 'C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data'\n", "video_path = os.path.join( BASE_DATA_PATH, \"testing_video\\myvideo.avi\")\n", "print(video_path)\n", "\n", "#function to retrieve the next image in the set of image to extract from a video clip\n", "def single_video_frame_generator():\n", " BASE_DATA_PATH = 'C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data'\n", " video_path = os.path.join( BASE_DATA_PATH, \"testing_video\\myvideo.avi\")\n", " frames = []\n", " cap = cv2.VideoCapture(video_path)\n", " num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n", " sample_every_frame = max(1, num_frames // SEQUENCE_LENGTH)\n", " current_frame = 0\n", "\n", " label = os.path.basename(os.path.dirname(video_path))\n", " \n", "\n", "\n", " max_images = SEQUENCE_LENGTH\n", " while True:\n", " success, frame = cap.read()\n", " if not success:\n", " break\n", "\n", " if current_frame % sample_every_frame == 0:\n", " # OPENCV reads in BGR, tensorflow expects RGB so we invert the order\n", " frame = frame[:, :, ::-1]\n", " img = tf.image.resize(frame, (299, 299))\n", " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", " max_images -= 1\n", " yield img, video_path\n", "\n", " if max_images == 0:\n", " break\n", " current_frame += 1\n", "\n", "\n", "#create a Dataset using the prevous function frame_generator \n", "# `from_generator` might throw a warning, expected to disappear in upcoming versions:\n", "# https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/data/Dataset#for_example_2\n", "dataset = tf.data.Dataset.from_generator(single_video_frame_generator,\n", " output_types=(tf.float32, tf.string),\n", " output_shapes=((299, 299, 3), ()))\n", "\n", "#dataset = dataset.batch(16).prefetch(tf.data.experimental.AUTOTUNE)\n", "print(dataset)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "#STEP 2: Setup Feature Extractor (if not done previously to process the images in our single video clip)\n", "# uses an Inception v3 CNN that exists in tensorflow\n", "inception_v3 = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet')\n", "\n", "x = inception_v3.output\n", "\n", "# We add Average Pooling to transform the feature map from\n", "# 8 * 8 * 2048 to 1 x 2048, as we don't need spatial information\n", "pooling_output = tf.keras.layers.GlobalAveragePooling2D()(x)\n", "\n", "feature_extraction_model = tf.keras.Model(inception_v3.input, pooling_output)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Going to process video = C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\testing_video\\myvideo.avi\n", " going to process C:\\Grewe\\Classes\\CS663\\Mat\\LSTM\\data\\testing_video\\myvideo.avi\n", " on frame0\n", " img currently is:\n", "Tensor(\"sub:0\", shape=(299, 299, 3), dtype=float32)\n", " img shape is (299, 299, 3)\n", " expanded dimension tensor now is Tensor(\"ExpandDims:0\", shape=(1, 299, 299, 3), dtype=float32)\n", " --shape is(1, 299, 299, 3)\n" ] }, { "ename": "AttributeError", "evalue": "'Tensor' object has no attribute 'numpy'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 183\u001b[0m \u001b[1;31m# extract images of SEQUENCE_LENGTH and process each with feature extractor\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 184\u001b[0m \u001b[1;31m# returning the array of features of SEQUENCE_LENGTH\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 185\u001b[1;33m \u001b[0mfeatures\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgrabImagesFromVideo_Process_with_InceptionCNN_FeatureExtractor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvideo\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeature_extraction_model\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 187\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\" Returned set of features is \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m\u001b[0m in \u001b[0;36mgrabImagesFromVideo_Process_with_InceptionCNN_FeatureExtractor\u001b[1;34m(video_path, feature_extraction_model)\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[1;31m#convert tensor current_features to an numpy array !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[1;31m#only works in TF 2.* !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 147\u001b[1;33m \u001b[0mcurrent_features\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcurrent_features\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'numpy'" ] } ], "source": [ "#STEP 3: Now process the images in our dataSet with the Inception v3 CNN feature extraction model created prevously\n", "#\n", "#IMPORTANT: the conversion from tensor object to numpy array ONLY WORKS IN TF 2.* !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n", "\n", "\n", "# step 3.1 create function to go through video specified and return an array of images of lenght SEQUNCE_LENGTH\n", "# each image is resized and preprocessed for input into a FeatureExtractor InceptionV3 CNN\n", "# returns array of tenors (1 tensor per image)\n", "def grabImagesFromVideo_PreProcess_for_InceptionCNN_FeatureExtractor(video_path):\n", " \n", " \n", " print(\" going to process \" + str(video_path))\n", " \n", " frames = []\n", " cap = cv2.VideoCapture(video_path)\n", " num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n", " sample_every_frame = max(1, num_frames // SEQUENCE_LENGTH)\n", " \n", "\n", " \n", " max_images = SEQUENCE_LENGTH\n", " \n", " \n", " #cycle through the frames in the video\n", " for current_frame_index in range(num_frames):\n", " \n", " print(\" on frame\" + str(current_frame_index))\n", "\n", " #read in next frame from video\n", " success, frame = cap.read()\n", " if not success:\n", " break\n", "\n", " #take every kth(sample_every_frame) frame and store in frames array\n", " if current_frame_index % sample_every_frame == 0:\n", " # OPENCV reads in BGR, tensorflow expects RGB so we invert the order\n", " frame = frame[:, :, ::-1]\n", " #appropriately resize and preprocess the image for Feature Extraction with inceptionV3 CNN\n", " img = tf.image.resize(frame, (299, 299))\n", " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", " print(\" going to save image\")\n", " print(img)\n", " print(img[0])\n", " print(img[0][0])\n", "\n", " \n", " frames.append(img)\n", " max_images -= 1\n", " \n", " \n", "\n", " # if we have sampled SEQUENCE_LENGTH number of frames then stop\n", " if max_images == 0:\n", " break\n", " \n", " \n", " \n", " return frames\n", " \n", " \n", "\n", "# step 3.1 create function to go through video specified and return an array of images of lenght SEQUNCE_LENGTH\n", "# each image is resized and preprocessed for input into a FeatureExtractor InceptionV3 CNN\n", "# then run through the FeatureExtractor --output will be a 1x2048 feature vector for each image\n", "# append to the set of features and return\n", "# features is an array of SEQUENCE_LENGTH (40) Tensors (each 1x2048 in length)\n", "def grabImagesFromVideo_Process_with_InceptionCNN_FeatureExtractor(video_path, feature_extraction_model):\n", " \n", " \n", " print(\" going to process \" + str(video_path))\n", " \n", " features = []\n", " cap = cv2.VideoCapture(video_path)\n", " num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n", " sample_every_frame = max(1, num_frames // SEQUENCE_LENGTH)\n", " \n", "\n", " \n", " max_images = SEQUENCE_LENGTH\n", " \n", " \n", " #cycle through the frames in the video\n", " for current_frame_index in range(num_frames):\n", " \n", " print(\" on frame\" + str(current_frame_index))\n", "\n", " #read in next frame from video\n", " success, frame = cap.read()\n", " \n", " \n", " \n", " \n", " \n", " if not success:\n", " break\n", "\n", " #take every kth(sample_every_frame) frame and store in frames array\n", " if current_frame_index % sample_every_frame == 0:\n", " # OPENCV reads in BGR, tensorflow expects RGB so we invert the order\n", " frame = frame[:, :, ::-1]\n", " \n", " \n", " #appropriately resize and preprocess the image for Feature Extraction with inceptionV3 CNN\n", " # CONVERTS TO A TENSOR from an array with the size of 299x299\n", " img = tf.image.resize(frame, (299, 299))\n", " \n", " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", " \n", " \n", " \n", " \n", " print(\" img currently is:\")\n", " print(img)\n", " print(\" img shape is \" + str(img.shape))\n", " \n", " \n", " tensor_input = tf.expand_dims(img,axis=0)\n", " print(\" expanded dimension tensor now is \" + str(tensor_input))\n", " print(\" --shape is\" + str(tensor_input.shape))\n", " \n", " \n", " #Diagnostics: run the \"graph\" to print out tensor object\n", " if(False):\n", " with tf.Session() as sess:\n", " #sess.run(init_op) #execute init_op\n", " #print the random values that we sample\n", " print(\" content img\")\n", " print (sess.run(img))\n", " print(\" _________________\")\n", "\n", " print(\" expanded dimension tensor now is \" + str(tensor_input))\n", " print(\" --shape is\" + str(tensor_input.shape))\n", " print(\" content tensor_input\")\n", " print (sess.run(tensor_input))\n", "\n", " \n", " \n", " # now going to process with the feature extraction model (inceptionv3 CNN based)\n", " current_features = feature_extraction_model(tensor_input)\n", " \n", " #current_features = feature_extraction_model.predict(img, steps=1)\n", " #reshape the tensor to shape ( #features x 1)--see https://www.tensorflow.org/api_docs/python/tf/reshape\n", " current_features = tf.reshape(current_features, (current_features.shape[0], -1))\n", " \n", " #convert tensor current_features to an numpy array !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n", " #only works in TF 2.* !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n", " current_features = current_features.numpy()\n", " \n", " \n", " \n", " features.append(current_features)\n", " \n", " #reduce counter\n", " max_images -= 1\n", " \n", " \n", "\n", " # if we have sampled SEQUENCE_LENGTH number of frames then stop\n", " if max_images == 0:\n", " break\n", " \n", " \n", " #now make sure we have length SEQUENCE_LENGTH and pad with zeros if necessary\n", " \n", " #padded_sequence = np.zeros((SEQUENCE_LENGTH, 2048))\n", " #padded_sequence[0:len(features)] = np.array(features)\n", " \n", " return features\n", " \n", " \n", "\n", "\n", "#step 3.2 call function passing a video_path and returning the array of images of SEQUENCE_LENGTH\n", "\n", "video = \"C:\\\\Grewe\\\\Classes\\\\CS663\\\\Mat\\\\LSTM\\\\data\\\\testing_video\\\\myvideo.avi\"\n", "print(\"Going to process video = \" + video)\n", "#images = grabImagesFromVideo_PreProcess_for_InceptionCNN_FeatureExtractor(video)\n", "#print(\" Returned set of images is \" + str(images))\n", "\n", "\n", "\n", "#step 3.2 call function passing a video_path and feature extraction model\n", "# extract images of SEQUENCE_LENGTH and process each with feature extractor\n", "# returning the array of features of SEQUENCE_LENGTH\n", "features = grabImagesFromVideo_Process_with_InceptionCNN_FeatureExtractor(video, feature_extraction_model)\n", "\n", "print(\" Returned set of features is \" + str(features))\n", "\n", "\n", "\n", " \n", " \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#print out feature Vector for first image in sequence\n", "print(features[0])\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true }, "outputs": [ { "ename": "NameError", "evalue": "name 'features' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# optional- save the array to an npy file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"what are features\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0moutput_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvideo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.avi'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'.npy'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m#save the array of feature vectors to file same location but extension .npy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m#ONLY do this for diagnostics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mNameError\u001b[0m: name 'features' is not defined" ] } ], "source": [ "# optional- save the array to an npy file\n", "print(\"what are features\" + str(features))\n", "output_path = video.replace('.avi', '.npy')\n", "#save the array of feature vectors to file same location but extension .npy\n", "#ONLY do this for diagnostics\n", "#np.save(output_path, images)\n", "\n", "\n", "# optional- save the array to an npy file\n", "output_path = video.replace('.avi', '.npy')\n", "print(\" goint to save images to \" + output_path)\n", "#save the array of feature vectors to file same location but extension .npy\n", "#ONLY do this for diagnostics\n", "fid = open(output_path, \"wb\")\n", "np.save(output_path, features)\n", "#open(output_path, \"wb\").write(tflite_feature_extractor_model)\n", "\n", "\n", "# optional- save the array to an npy file\n", "output_path = video.replace('.avi', '.npy')\n", "#save the array of feature vectors to file same location but extension .npy\n", "#ONLY do this for diagnostics\n", "#np.save(output_path, images)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#STEP 4: take the array of vectors all_features and convert it to a tensor for input into our LSTM model\n", "tensor_input = tf.convert_to_tensor(features, dtype=tf.float32)\n", "print(tensor_input)\n", "\n", "#create tensor needed from processed video\n", "#current array of feature vectors representing images in video clip called all_features\n", "\n", "# NOT DONE --need to figure out what shape of input tensor should be for our model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#STEP 5: call prediciton on our model\n", "print(tensor_input)\n", "prediction= model.predict(tensor_input, verbose=1 )\n", "print(prediction)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "print(\"length of predictions\" + str(len(prediction)))\n", "\n", "i=0\n", "for p in prediction:\n", " print(\"for prediction\" + str(i))\n", " print(\"index of max predicition class \")\n", " print(np.argmax(p))\n", " i+=1\n", "\n", "print(\"index of max predicition class for feature vector 1\")\n", "print(np.argmax(prediction[1]))\n", "\n", "print(\"index of max predicition class for feature vector 29\")\n", "print(np.argmax(prediction[29]))\n", "\n", "\n", "print(\"index of max predicition class for feature vector 39\")\n", "print(np.argmax(prediction[39]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" 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