Python create_trainer_environment Beispiele

Beispiel #1

from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
import os
import numpy as np

from trainer.environment import create_trainer_environment

NUM_CLASSES = 10
EPOCHS = 10
NUM_PREDICTIONS = 20
MODEL_NAME = 'keras_cifar10_trained_model.h5'

# the trainer environment contains useful information about
env = create_trainer_environment()
print('creating SageMaker trainer environment:\n%s' % str(env))

# getting the hyperparameters
batch_size = env.hyperparameters.get('batch_size', object_type=int)
data_augmentation = env.hyperparameters.get('data_augmentation',
                                            default=True,
                                            object_type=bool)
learning_rate = env.hyperparameters.get('learning_rate',
                                        default=.0001,
                                        object_type=float)
width_shift_range = env.hyperparameters.get('width_shift_range',
                                            object_type=float)
height_shift_range = env.hyperparameters.get('height_shift_range',
                                             object_type=float)
EPOCHS = env.hyperparameters.get('epochs', default=10, object_type=int)

Beispiel #2

Datei: start.py Projekt: FNDaily/amazon-sagemaker-examples

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import os
import numpy as np

from trainer.environment import create_trainer_environment

NUM_CLASSES = 10
EPOCHS = 10
NUM_PREDICTIONS = 20
MODEL_NAME = 'keras_cifar10_trained_model.h5'

# the trainer environment contains useful information about
env = create_trainer_environment()
print('creating SageMaker trainer environment:\n%s' % str(env))

# getting the hyperparameters
batch_size = env.hyperparameters.get('batch_size', object_type=int)
data_augmentation = env.hyperparameters.get('data_augmentation', default=True, object_type=bool)
learning_rate = env.hyperparameters.get('learning_rate', default=.0001, object_type=float)
width_shift_range = env.hyperparameters.get('width_shift_range', object_type=float)
height_shift_range = env.hyperparameters.get('height_shift_range', object_type=float)
EPOCHS = env.hyperparameters.get('epochs', default=10, object_type=int)

# reading data from train and test channels
train_data = np.load(os.path.join(env.channel_dirs['train'], 'cifar-10-npz-compressed.npz'))
(x_train, y_train) = train_data['x'], train_data['y']

test_data = np.load(os.path.join(env.channel_dirs['test'], 'cifar-10-npz-compressed.npz'))