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Load train, validation, and test set for mnist. Input is reshaped into Nx28x28
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| import numpy as np | |
| IMAGE_SIZE = 28 | |
| def load_train_data(data_path, validation_size=500): | |
| """ | |
| Load mnist data. Each row in csv is formatted (label, input) | |
| :return: 3D Tensor input of train and validation set with 2D Tensor of one hot encoded image labels | |
| """ | |
| # Data format: 1 byte label, 28 * 28 input | |
| train_data = np.genfromtxt(data_path, delimiter=',', dtype=np.float32) | |
| x_train = train_data[:, 1:] | |
| # Get label and one-hot encode | |
| y_train = train_data[:, 0] | |
| y_train = (np.arange(10) == y_train[:, None]).astype(np.float32) | |
| # get a validation set and remove it from the train set | |
| x_train, x_val, y_train, y_val = x_train[0:(len(x_train) - validation_size), :], x_train[( | |
| len(x_train) - validation_size):len(x_train), :], \ | |
| y_train[0:(len(y_train) - validation_size), :], y_train[( | |
| len(y_train) - validation_size):len(y_train), :] | |
| # reformat the data so it's not flat | |
| x_train = x_train.reshape(len(x_train), IMAGE_SIZE, IMAGE_SIZE, 1) | |
| x_val = x_val.reshape(len(x_val), IMAGE_SIZE, IMAGE_SIZE, 1) | |
| return x_train, x_val, y_train, y_val | |
| def load_test_data(data_path): | |
| """ | |
| Load mnist test data | |
| :return: 3D Tensor input of train and validation set with 2D Tensor of one hot encoded image labels | |
| """ | |
| test_data = np.genfromtxt(data_path, delimiter=',', dtype=np.float32) | |
| x_test = test_data[:, 1:] | |
| y_test = np.array(test_data[:, 0]) | |
| y_test = (np.arange(10) == y_test[:, None]).astype(np.float32) | |
| x_test = x_test.reshape(len(x_test), IMAGE_SIZE, IMAGE_SIZE, 1) | |
| return x_test, y_test |
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