DEVICE_ID = DEVICE_ID_LIST[0] # grab first element from list
# Set CUDA_VISIBLE_DEVICES to mask out all other GPUs than the first available device id
os.environ["CUDA_VISIBLE_DEVICES"] = str(DEVICE_ID)
except EnvironmentError:
print("GPU not found")
select_gpu()
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
backend.set_session(sess)
x_train, y_train, x_test, y_test = get_data()
X = np.concatenate((x_train, x_test))
Y = np.concatenate((y_train, y_test))
k_fold = StratifiedKFold(n_splits=10, shuffle=False, random_state=7)
ret = []
y_stub = np.random.randint(0, 10, X.shape[0])
cnn_model = get_model(X)
weights = cnn_model.get_weights()
for train, test in k_fold.split(X, y_stub):
cnn_model.set_weights(weights)
print("Training started")
ret.append(train_model(cnn_model, X[train], Y[train], X[test], Y[test]))
print(np.array(ret))
from examples.cnn.cifar10.cifar10 import get_data
from examples.cnn.cnn import cnn, select_gpu
select_gpu()
for width in [64, 128, 256]:
for dropout in [0.25, 0.5]:
for regularizer in [0.01, 0.001]:
params = [width * 1.0] * 6 + [dropout] * 6 + [regularizer] * 4
print(params)
cnn(params, get_data())