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helper.py
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helper.py
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import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelBinarizer
def _load_label_names():
"""
Load the label names from file
"""
return ['t-shirt', 'trouser', 'pullover', 'dress', 'coat', 'sandal', 'shirt', 'sneaker', 'bag', 'ankle_boot']
def load_dataset(dataset_folder_path):
"""
Load the training and test datasets
"""
with open(dataset_folder_path, mode='rb') as file:
pickle_data = pickle.load(file)
train_features = pickle_data[0].reshape((len(pickle_data[0]), 1, 28, 28)).transpose(0, 2, 3, 1)
train_labels = pickle_data[1]
test_features = pickle_data[2].reshape((len(pickle_data[2]), 1, 28, 28)).transpose(0, 2, 3, 1)
test_labels = pickle_data[3]
return train_features, train_labels, test_features, test_labels
def display_stats(dataset_folder_path, sample_id):
"""
Display Stats of the dataset
"""
train_features, train_labels, test_features, test_labels = load_dataset(dataset_folder_path)
if not (0 <= sample_id < len(train_features)):
print('{} samples in training set. {} is out of range.'.format(len(train_features), sample_id))
return None
print('Samples: {}'.format(len(train_features)))
print('Label Counts: {}'.format(dict(zip(*np.unique(train_labels, return_counts=True)))))
print('First 20 Labels: {}'.format(train_labels[:20]))
sample_image = train_features[sample_id]
sample_label = train_labels[sample_id]
label_names = _load_label_names()
print('\nExample of Image {}:'.format(sample_id))
print('Image - Min Value: {} Max Value: {}'.format(sample_image.min(), sample_image.max()))
print('Image - Shape: {}'.format(sample_image.shape))
print('Label - Label Id: {} Name: {}'.format(sample_label, label_names[sample_label]))
plt.axis('off')
plt.imshow(sample_image.squeeze(), cmap = "gray")
def _preprocess_and_save(normalize, one_hot_encode, features, labels, filename):
"""
Preprocess data and save it to file
"""
features = normalize(features)
labels = one_hot_encode(labels)
pickle.dump((features, labels), open(filename, 'wb'))
def preprocess_and_save_data(dataset_folder_path, normalize, one_hot_encode):
"""
Preprocess Training and Validation Data
"""
valid_features = []
valid_labels = []
train_features, train_labels, test_features, test_labels = load_dataset(dataset_folder_path)
validation_count = int(len(train_features) * 0.1)
# Preprocess and save new training data
_preprocess_and_save(
normalize,
one_hot_encode,
train_features[:-validation_count],
train_labels[:-validation_count],
'preprocess_train' + '.p')
# Use a portion of training batch for validation
valid_features.extend(train_features[-validation_count:])
valid_labels.extend(train_labels[-validation_count:])
# Preprocess and Save all validation data
_preprocess_and_save(
normalize,
one_hot_encode,
np.array(valid_features),
np.array(valid_labels),
'preprocess_validation.p')
# Preprocess and Save all test data
_preprocess_and_save(
normalize,
one_hot_encode,
np.array(test_features),
np.array(test_labels),
'preprocess_test.p')
def batch_features_labels(features, labels, batch_size):
"""
Split features and labels into batches
"""
for start in range(0, len(features), batch_size):
end = min(start + batch_size, len(features))
yield features[start:end], labels[start:end]
def load_preprocess_training_batch(batch_size):
"""
Load the Preprocessed Training data and return them in batches of <batch_size> or less
"""
filename = 'preprocess_train' + '.p'
features, labels = pickle.load(open(filename, mode='rb'))
# Return the training data in batches of size <batch_size> or less
return batch_features_labels(features, labels, batch_size)
def display_image_predictions(features, labels, predictions):
n_classes = 10
label_names = _load_label_names()
label_binarizer = LabelBinarizer()
label_binarizer.fit(range(n_classes))
label_ids = label_binarizer.inverse_transform(np.array(labels))
fig, axies = plt.subplots(nrows=4, ncols=2)
fig.tight_layout()
fig.suptitle('Softmax Predictions', fontsize=20, y=1.1)
n_predictions = 3
margin = 0.05
ind = np.arange(n_predictions)
width = (1. - 2. * margin) / n_predictions
for image_i, (feature, label_id, pred_indicies, pred_values) in enumerate(zip(features, label_ids, predictions.indices, predictions.values)):
pred_names = [label_names[pred_i] for pred_i in pred_indicies]
correct_name = label_names[label_id]
axies[image_i][0].imshow(feature.squeeze(), cmap='gray')
axies[image_i][0].set_title(correct_name)
axies[image_i][0].set_axis_off()
axies[image_i][1].barh(ind + margin, pred_values[::-1], width)
axies[image_i][1].set_yticks(ind + margin)
axies[image_i][1].set_yticklabels(pred_names[::-1])
axies[image_i][1].set_xticks([0, 0.5, 1.0])