本文整理汇总了Python中datasets.download_and_convert_flowers.run方法的典型用法代码示例。如果您正苦于以下问题：Python download_and_convert_flowers.run方法的具体用法？Python download_and_convert_flowers.run怎么用？Python download_and_convert_flowers.run使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类datasets.download_and_convert_flowers的用法示例。
在下文中一共展示了download_and_convert_flowers.run方法的12个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'wikiart':
if not FLAGS.input_dataset_dir is None:
convert_wikiart.run(FLAGS.input_dataset_dir, FLAGS.dataset_dir)
else:
raise ValueError("For wikiart, you must supply a valid input directory with --input_dataset_dir")
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name) 示例2: main
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
if FLAGS.shard:
download_convert_and_shard_cifar10.run(FLAGS.dataset_dir)
else:
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_dir) 示例3: main
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'fer':
download_and_convert_fer.run(FLAGS.dataset_dir,FLAGS.pic_path)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name) 示例4: main
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'diabetic':
download_and_convert_diabetic.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name) 开发者ID:PacktPublishing，项目名称:Machine-Learning-with-TensorFlow-1.x，代码行数:19，代码来源:download_and_convert_data.py
示例5: display_data
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def display_data():
with tf.Graph().as_default():
dataset = flowers.get_split('train', flowers_data_dir)
data_provider = slim.dataset_data_provider.DatasetDataProvider(
dataset, common_queue_capacity=32, common_queue_min=1)
image, label = data_provider.get(['image', 'label'])
with tf.Session() as sess:
with slim.queues.QueueRunners(sess):
for i in range(4):
np_image, np_label = sess.run([image, label])
height, width, _ = np_image.shape
class_name = name = dataset.labels_to_names[np_label]
plt.figure()
plt.imshow(np_image)
plt.title('%s, %d x %d' % (name, height, width))
plt.axis('off')
plt.show()
return 示例6: disp_data
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def disp_data():
with tf.Graph().as_default():
dataset = flowers.get_split('train', flowers_data_dir)
data_provider = slim.dataset_data_provider.DatasetDataProvider(
dataset, common_queue_capacity=32, common_queue_min=1)
image, label,format = data_provider.get(['image', 'label', 'format'])
with tf.Session() as sess:
with slim.queues.QueueRunners(sess):
for i in range(4):
np_image, np_label,np_format = sess.run([image, label,format])
height, width, _ = np_image.shape
class_name = name = dataset.labels_to_names[np_label]
plt.figure()
plt.imshow(np_image)
plt.title('%s, %d x %d' % (name, height, width))
plt.axis('off')
plt.show()
return 示例7: main
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'customized':
convert_customized.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name) 示例8: main
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'visualwakewords':
download_and_convert_visualwakewords.run(
FLAGS.dataset_dir, FLAGS.small_object_area_threshold,
FLAGS.foreground_class_of_interest)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name) 示例9: main
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_name) 示例10: main
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def main(_):
if not FLAGS.dataset_name:
raise ValueError('You must supply the dataset name with --dataset_name')
if not FLAGS.dataset_dir:
raise ValueError('You must supply the dataset directory with --dataset_dir')
if FLAGS.dataset_name == 'cifar10':
download_and_convert_cifar10.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'flowers':
download_and_convert_flowers.run(FLAGS.dataset_dir)
elif FLAGS.dataset_name == 'mnist':
download_and_convert_mnist.run(FLAGS.dataset_dir)
else:
raise ValueError(
'dataset_name [%s] was not recognized.' % FLAGS.dataset_dir) 示例11: download_convert
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def download_convert():
dataset_dir = flowers_data_dir
download_and_convert_flowers.run(dataset_dir)
return 示例12: apply_random_image
# 需要导入模块: from datasets import download_and_convert_flowers [as 别名]
# 或者: from datasets.download_and_convert_flowers import run [as 别名]
def apply_random_image():
with tf.Graph().as_default():
# The model can handle any input size because the first layer is convolutional.
# The size of the model is determined when image_node is first passed into the my_cnn function.
# Once the variables are initialized, the size of all the weight matrices is fixed.
# Because of the fully connected layers, this means that all subsequent images must have the same
# input size as the first image.
batch_size, height, width, channels = 3, 28, 28, 3
images = tf.random_uniform([batch_size, height, width, channels], maxval=1)
# Create the model.
num_classes = 10
logits = my_cnn(images, num_classes, is_training=True)
probabilities = tf.nn.softmax(logits)
# Initialize all the variables (including parameters) randomly.
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
# Run the init_op, evaluate the model outputs and print the results:
sess.run(init_op)
probabilities = sess.run(probabilities)
print('Probabilities Shape:')
print(probabilities.shape) # batch_size x num_classes
print('\nProbabilities:')
print(probabilities)
print('\nSumming across all classes (Should equal 1):')
print(np.sum(probabilities, 1)) # Each row sums to 1
return