def print_library_version(): print(os.getcwd()) version_pandas = pkg_resources.get_distribution("pandas").version print("Version pandas: {}".format(version_pandas)) print("Version OpenCV: {}".format(cv2.__version__)) version_cntk = pkg_resources.get_distribution("cntk").version print("Version CNTK: {}".format(version_cntk)) cntk.logging.set_trace_level(2) print("Devices used by CNTK: {}".format(cntk.all_devices()))
import cntk import keras from keras.models import Sequential from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D from keras.optimizers import SGD from keras import backend as K from scipy.io import wavfile import numpy as np from random import shuffle # ## FRAMEWORK # In[2]: cntk.try_set_default_device(cntk.all_devices()[0]) # ## DATA PREPARATION # In[3]: sec = 3 img_rows = 28 img_cols = 28 input_shape = (img_rows, img_cols, 1) num_classes = 2 root, _dirs, files = next( os.walk(os.path.join(os.getcwd(), os.path.join("dataset", "train")))) train_paths = [os.path.join(root, file) for file in files]
# for AlexNet base model use: from utils.configs.AlexNet_config import cfg as network_cfg from utils.configs.AlexNet_config import cfg as network_cfg # for Pascal VOC 2007 data set use: from utils.configs.Pascal_config import cfg as dataset_cfg # for the Grocery data set use: from utils.configs.Grocery_config import cfg as dataset_cfg # from utils.configs.Grocery_config import cfg as dataset_cfg from utils.configs.BU_config import cfg as dataset_cfg return merge_configs([detector_cfg, network_cfg, dataset_cfg]) # trains and evaluates a Fast R-CNN model. if __name__ == '__main__': cfg = get_configuration() prepare(cfg, False) cntk.logging.set_trace_level(2) cntk.all_devices() cntk.device.try_set_default_device(cntk.device.gpu(cfg.GPU_ID)) # train and test trained_model = train_faster_rcnn(cfg) eval_results = compute_test_set_aps(trained_model, cfg) # write AP results to output for class_name in eval_results: print('AP for {:>15} = {:.4f}'.format(class_name, eval_results[class_name])) print('Mean AP = {:.4f}'.format(np.nanmean(list(eval_results.values())))) # Plot results on test set images if cfg.VISUALIZE_RESULTS: num_eval = min(cfg["DATA"].NUM_TEST_IMAGES, 100)
# import the necessary packages # from keras.applications import ResNet50 from keras.models import load_model from keras.preprocessing.image import img_to_array from keras.applications import imagenet_utils from PIL import Image import numpy as np import flask import io from keras import backend as K import os from importlib import reload import cntk print(cntk.__version__) print(cntk.all_devices()) # initialize our Flask application and the Keras model app = flask.Flask(__name__) model = None model = load_model('my_model.h5') def set_keras_backend(backend): if K.backend() != backend: os.environ['KERAS_BACKEND'] = backend reload(K) assert K.backend() == backend