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GitHub Repository: amanchadha/coursera-deep-learning-specialization
 Path: blob/master/C1 - Neural Networks and Deep Learning/Week 2/Logistic Regression as a Neural Network/lr_utils.py
Views: 4818
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import numpy as np

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import h5py

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def load_dataset():

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    train_dataset = h5py.File('datasets/train_catvnoncat.h5', "r")

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    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features

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    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels

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    test_dataset = h5py.File('datasets/test_catvnoncat.h5', "r")

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    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features

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    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels

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    classes = np.array(test_dataset["list_classes"][:]) # the list of classes

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    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))

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    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))

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    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

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