numpy - Python - Datatype Retention in saving to Pickle file -


i'm saving dictionary of numpy arrays pickle file. , unpickling them new variables. code this:

pickling:

# here variables 'train_dataset', 'train_labels' etc np arrays. save = {     'train_dataset': train_dataset,     'train_labels': train_labels,     'valid_dataset': valid_dataset,     'valid_labels': valid_labels,     'test_dataset': test_dataset,     'test_labels': test_labels,     } pickle.dump(save, f, pickle.highest_protocol)

unpickling:

save = pickle.load(f) train_dataset_new = save['train_dataset'] train_labels_new = save['train_labels'] valid_dataset_new = save['valid_dataset'] valid_labels_new = save['valid_labels'] test_dataset_new = save['test_dataset'] test_labels_new = save['test_labels']

will variables loaded pickle file np arrays? please elaborate bit if can.

thanks

quoting directly docs:

read string open file object file , interpret pickle data stream, reconstructing , returning original object hierarchy.

little test code check datatype of loaded variable <type 'numpy.ndarray'>:

import numpy np import pickle   #f = open( "pickled.p", "wb" )  train_dataset = np.ones(5) train_labels = np.ones(5) valid_dataset = np.ones(5) valid_labels = np.ones(5) test_dataset = np.ones(5) test_labels = np.ones(5)  print type(train_dataset)  # <type 'numpy.ndarray'> print train_dataset.shape  # <5l,>  # here variables 'train_dataset', 'train_labels' etc np arrays. save = {     'train_dataset': train_dataset,     'train_labels': train_labels,     'valid_dataset': valid_dataset,     'valid_labels': valid_labels,     'test_dataset': test_dataset,     'test_labels': test_labels,     } pickle.dump(save, open( "save.p", "wb" ), pickle.highest_protocol)  save = pickle.load(open( "save.p", "rb" )) train_dataset_new = save['train_dataset'] train_labels_new = save['train_labels'] valid_dataset_new = save['valid_dataset'] valid_labels_new = save['valid_labels'] test_dataset_new = save['test_dataset'] test_labels_new = save['test_labels']  print type(train_dataset_new)  # <type 'numpy.ndarray'> print train_dataset_new.shape  # <5l,>

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