Update data provider

cifar100_network_trainer.py

@@ -23,7 +23,7 @@ experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_pr
 
 rng = np.random.RandomState(seed=seed)  # set seed
 
-train_data = CIFAR100DataProvider(which_set="train", batch_size=batch_size, rng=rng)
+train_data = CIFAR100DataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
 val_data = CIFAR100DataProvider(which_set="valid", batch_size=batch_size, rng=rng)
 test_data = CIFAR100DataProvider(which_set="test", batch_size=batch_size, rng=rng)
 #  setup our data providers

cifar10_network_trainer.py

@@ -23,7 +23,7 @@ experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_pr
 
 rng = np.random.RandomState(seed=seed)  # set seed
 
-train_data = CIFAR10DataProvider(which_set="train", batch_size=batch_size, rng=rng)
+train_data = CIFAR10DataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
 val_data = CIFAR10DataProvider(which_set="valid", batch_size=batch_size, rng=rng)
 test_data = CIFAR10DataProvider(which_set="test", batch_size=batch_size, rng=rng)
 #  setup our data providers

data_providers.py

@@ -138,7 +138,7 @@ class MNISTDataProvider(DataProvider):
     """Data provider for MNIST handwritten digit images."""
 
     def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
-                 random_sampling=True, rng=None):
+                 random_sampling=False, rng=None):
         """Create a new MNIST data provider object.
 
         Args:

emnist_network_trainer.py

@@ -23,7 +23,7 @@ experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_pr
 
 rng = np.random.RandomState(seed=seed)  # set seed
 
-train_data = EMNISTDataProvider(which_set="train", batch_size=batch_size, rng=rng)
+train_data = EMNISTDataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
 val_data = EMNISTDataProvider(which_set="valid", batch_size=batch_size, rng=rng)
 test_data = EMNISTDataProvider(which_set="test", batch_size=batch_size, rng=rng)
 #  setup our data providers

msd10_network_trainer.py

@@ -23,7 +23,7 @@ experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_pr
 
 rng = np.random.RandomState(seed=seed)  # set seed
 
-train_data = MSD10GenreDataProvider(which_set="train", batch_size=batch_size, rng=rng)
+train_data = MSD10GenreDataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
 val_data = MSD10GenreDataProvider(which_set="valid", batch_size=batch_size, rng=rng)
 test_data = MSD10GenreDataProvider(which_set="test", batch_size=batch_size, rng=rng)
 #  setup our data providers

msd25_network_trainer.py

@@ -23,7 +23,7 @@ experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(experiment_pr
 
 rng = np.random.RandomState(seed=seed)  # set seed
 
-train_data = MSD10GenreDataProvider(which_set="train", batch_size=batch_size, rng=rng)
+train_data = MSD10GenreDataProvider(which_set="train", batch_size=batch_size, rng=rng, random_sampling=True)
 val_data = MSD10GenreDataProvider(which_set="valid", batch_size=batch_size, rng=rng)
 test_data = MSD10GenreDataProvider(which_set="test", batch_size=batch_size, rng=rng)
 #  setup our data providers