training_epochs = 1000
batch_size = 10
# early-stopping parameters
patience = 20000 # look as this many examples regardless
patience_increase = 2 # wait this much longer if new best found
improvement_threshold = 0.995 # consider this improvement significant
pretrain_vis_freq = False
finetrain_vis_freq = False
if __name__ == '__main__':
logger = utils.logs.get_logger(
__name__, update_stream_level=utils.logs.logging.DEBUG)
logger.info('Loading data ...')
source = data.Load_Data(location=data.data_loc)
datasets = source.all()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
# np random generator
np_rng = np.random.RandomState(123)
logger.info('Building the model ...')
training_epochs = 1000
batch_size = 10
# early-stopping parameters
patience = 20000 # look as this many examples regardless
patience_increase = 2 # wait this much longer if new best found
improvement_threshold = 0.995 # consider this improvement significant
pretrain_vis_freq = 200
finetrain_vis_freq = 1
if __name__ == '__main__':
logger = utils.logs.get_logger(__name__,
update_stream_level=utils.logs.logging.DEBUG)
logger.info('Loading data ...')
source = data.Load_Data()
datasets = source.mnist()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0]// batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]// batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
# np random generator
np_rng = np.random.RandomState(123)
logger.info('Building the model ...')
## early-stopping parameters
patience = 50000 # look as this many examples regardless
patience_increase = 2 # wait this much longer if new best is found
improvement_threshold = 0.995 # consider this improvement significant
# sample for plotting
freq = 1
if __name__ == "__main__":
logger = utils.logs.get_logger(
__name__, update_stream_level=utils.logs.logging.DEBUG)
logger.info('Loading data ...')
source = data.Load_Data(location=data.data_loc,
# search_pat='day1'
)
datasets = source.all()
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] // batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] // batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] // batch_size
logger.info('Building the model ...')
# allocate symbolic variables for the data