def build_dataset(filenames , batch_size):
test_dataset = tf.data.TFRecordDataset(filenames)
test_dataset = test_dataset.map(utils.
parse_single_example).map(lambda image , label:
(utils.build_hsv_grayscale_image(image), label)
)
test_dataset = test_dataset.batch(batch_size)
return test_dataset
def build_datasets(filenames, batch_size):
train_dataset = tf.data.TFRecordDataset(filenames).repeat()
train_dataset = train_dataset.map(utils.parse_single_example).map(
lambda image, label: (utils.augment_image(image), label))
train_dataset = train_dataset.shuffle(buffer_size=10000,
reshuffle_each_iteration=True)
train_dataset = train_dataset.batch(batch_size)
test_dataset = tf.data.TFRecordDataset(filenames)
test_dataset = test_dataset.map(utils.parse_single_example).map(
lambda image, label: (utils.build_hsv_grayscale_image(image), label))
test_dataset = test_dataset.batch(batch_size)
return train_dataset, test_dataset
useCkpt = False
def build_datasets(filenames , batch_size):
train_dataset = tf.data.TFRecordDataset(filenames)
.repeat()
train_dataset = train_dataset.map(utils.
parse_single_example).map(lambda image , label:
(utils.augment_image(image), label))
train_dataset = train_dataset.shuffle(buffer_size
=10000 , reshuffle_each_iteration=True)
train_dataset = train_dataset.batch(batch_size)
test_dataset = tf.data.TFRecordDataset(filenames)
test_dataset = test_dataset.map(utils.
parse_single_example).map(lambda image , label:
(utils.build_hsv_grayscale_image(image), label)
)
test_dataset = test_dataset.batch(batch_size)
return train_dataset , test_dataset
def train_model(session , train_operation ,
loss_operation , correct_prediction , iterator_map):
time1 = time.time()
train_iterator = iterator_map[" train_iterator "]
test_iterator = iterator_map[" test_iterator "]
test_init_op = iterator_map[" test_init_op "]
train_images_with_labels = train_iterator.get_next
()
test_images_with_labels = test_iterator.get_next()
for i in range(1, iterations + 1):