| export DATASET_DIR=/media/drive2/tensorflow_datasets/cub/with_200_val_split | |
| export EXPERIMENT_DIR=/media/drive2/tensorflow_experiments/ebird/cub_image_experiment | |
| export IMAGENET_PRETRAINED_MODEL=/media/drive3/tensorflow_models/inception_v3.ckpt | |
| # Visualize the inputs to the network | |
| CUDA_VISIBLE_DEVICES=1 python visualize_train_inputs.py \ | |
| --tfrecords $DATASET_DIR/train* \ | |
| --config $EXPERIMENT_DIR/config_train.yaml \ | |
| --text_labels |
Installing TensorFlow r1.1 on an AWS C4 instance.
Following this with a few modifications
Built using Ubuntu 16.04 with c4.2xlarge instance and 25GB of standard ssd storage.
# Update and upgrade installed packages
sudo apt-get update
sudo apt-get upgrade
Installing TensorFlow r1.1 on an AWS P2 instance.
Following this with a few modifications
Built using Ubuntu 16.04 with p2.xlarge instance and 25GB of standard ssd storage.
# Update and upgrade installed packages
sudo apt-get update
sudo apt-get upgrade
| import json | |
| import os | |
| import numpy as np | |
| from sklearn.metrics import accuracy_score | |
| from sklearn.svm import LinearSVC | |
| # File Paths | |
| DATA_FOLDER = '' # Fill in | |
| TRAIN_DATASET_FP = os.path.join(DATA_FOLDER, 'train2018.json'); assert os.path.exists(TRAIN_DATASET_FP), "Train json file not found" |