def __init__(self, rand_int=0, num_of_samples=None, args=None):
if args == None:
args = netp.get_default_parser(num_of_samples)
self.cov_net = args.cov_net
self.calc_information = args.calc_information
self.run_in_parallel = args.run_in_parallel
self.num_ephocs = args.num_ephocs
self.learning_rate = args.learning_rate
self.batch_size = args.batch_size
self.activation_function = args.activation_function
self.interval_accuracy_display = args.interval_accuracy_display
self.save_grads = args.save_grads
self.num_of_repeats = args.num_of_repeats
self.calc_information_last = args.calc_information_last
self.num_of_bins = args.num_of_bins
self.interval_information_display = args.interval_information_display
self.save_ws = args.save_ws
self.name = args.data_dir + args.data_name
# The arch of the networks
self.layers_sizes = netp.select_network_arch(args.net_type)
# The percents of the train data samples
self.train_samples = np.linspace(1, 100, 199)[[[x * 2 - 2 for x in index] for index in args.inds]]
# The indexs that we want to calculate the information for them in logspace interval
self.epochs_indexes = np.unique(
np.logspace(np.log2(args.start_samples), np.log2(args.num_ephocs), args.num_of_samples, dtype=int,
base=2)) - 1
max_size = np.max([len(layers_size) for layers_size in self.layers_sizes])
# load data
self.data_sets = load_data(self.name, args.random_labels)
# create arrays for saving the data
self.ws, self.grads, self.information, self.models, self.names, self.networks, self.weights = [
[[[[None] for k in range(len(self.train_samples))] for j in range(len(self.layers_sizes))]
for i in range(self.num_of_repeats)] for _ in range(7)]
self.loss_train, self.loss_test, self.test_error, self.train_error, self.l1_norms, self.l2_norms = \
[np.zeros((self.num_of_repeats, len(self.layers_sizes), len(self.train_samples), len(self.epochs_indexes)))
for _ in range(6)]
params = {'sampleLen': len(self.train_samples),
'nDistSmpls': args.nDistSmpls,
'layerSizes': ",".join(str(i) for i in self.layers_sizes[0]), 'nEpoch': args.num_ephocs,
'batch': args.batch_size,
'nRepeats': args.num_of_repeats, 'nEpochInds': len(self.epochs_indexes),
'LastEpochsInds': self.epochs_indexes[-1], 'DataName': args.data_name,
'lr': args.learning_rate}
self.name_to_save = args.name + "_" + "_".join([str(i) + '=' + str(params[i]) for i in params])
params['train_samples'], params['CPUs'], params[
'directory'], params['epochsInds'] = self.train_samples, NUM_CORES, self.name_to_save, self.epochs_indexes
self.params = params
self.rand_int = rand_int
# If we trained already the network
self.traind_network = False
def __init__(self, rand_int=0, num_of_samples=None, args=None):
if args == None:
args = netp.get_default_parser(num_of_samples)
self.conv_net = args.conv_net # boolian indicating if this should be a convolutional architecture. defunct
self.calc_information = args.calc_information # boolean indicating whether to find the info or not
self.calcMethod = args.calcMethod # name for the method of calculating the information
self.run_in_parallel = args.run_in_parallel # boolean for its name
self.num_epochs = args.num_epochs # how many epochs to run the model
self.learning_rate = args.learning_rate
self.batch_size = args.batch_size
self.activation_function = args.activation_function # 0 is tanh, 1 is ReLU
self.interval_accuracy_display = args.interval_accuracy_display
self.save_grads = args.save_grads # bool, used for plotting the gradients
self.num_of_repeats = args.num_of_repeats
self.calc_information_last = args.calc_information_last # whether to calculate the information after running the network
self.num_of_bins = args.num_of_bins
self.interval_information_display = args.interval_information_display
self.save_ws = args.save_ws
self.name = args.data_dir + args.data_name
# The arch of the networks
self.layers_sizes = netp.select_network_arch(args.net_type)
# The percents of the train data samples
self.train_samples = np.linspace(1, 100, 199)[[[x * 2 - 2 for x in index] for index in args.inds]]
# The indexs that we want to calculate the information for them in logspace interval
self.epochs_indexes = np.unique(
np.logspace(np.log2(args.start_samples), np.log2(args.num_epochs), args.num_of_samples, dtype=int,
base=2)) - 1
max_size = np.max([len(layers_size) for layers_size in self.layers_sizes])
# load data
self.data_sets = load_data(self.name, args.random_labels)
# create arrays for saving the data
# ws is the activation in the (repeat, layer, training example)
self.ws, self.grads, self.information, self.models, self.names, self.networks, self.weights = [
[[[[None] for k in range(len(self.train_samples))] for j in range(len(self.layers_sizes))]
for i in range(self.num_of_repeats)] for _ in range(7)]
# ws has "shape" train_samples by layer_sizes by num_of_repeats
self.loss_train, self.loss_test, self.test_error, self.train_error, self.l1_norms, self.l2_norms = [np.zeros((self.num_of_repeats, len(self.layers_sizes), len(self.train_samples), len(self.epochs_indexes))) for _ in range(6)]
params = {'sampleLen': len(self.train_samples),
'nDistSmpls': args.nDistSmpls,
'layerSizes': ",".join(str(i) for i in self.layers_sizes[0]), 'nEpoch': args.num_epochs, 'batch': args.batch_size,
'nRepeats': args.num_of_repeats, 'nEpochInds': len(self.epochs_indexes),
'LastEpochsInds': self.epochs_indexes[-1], 'DataName': args.data_name,
'lr': args.learning_rate}
self.name_to_save = args.name + "_" + "_".join([str(i) + '=' + str(params[i]) for i in params])
params['train_samples'], params['CPUs'], params[
'directory'], params['epochsInds'] = self.train_samples, NUM_CORES, self.name_to_save, self.epochs_indexes
self.params = params
self.rand_int = rand_int
# If we trained already the network
self.traind_network = False
