本文整理汇总了Python中tensorflow.contrib.slim.python.slim.nets.inception_v3.inception_v3方法的典型用法代码示例。如果您正苦于以下问题:Python inception_v3.inception_v3方法的具体用法?Python inception_v3.inception_v3怎么用?Python inception_v3.inception_v3使用的例子?那么, 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tensorflow.contrib.slim.python.slim.nets.inception_v3
的用法示例。
在下文中一共展示了inception_v3.inception_v3方法的15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: testBuildBaseNetwork
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testBuildBaseNetwork(self):
batch_size = 5
height, width = 299, 299
inputs = random_ops.random_uniform((batch_size, height, width, 3))
final_endpoint, end_points = inception_v3.inception_v3_base(inputs)
self.assertTrue(final_endpoint.op.name.startswith('InceptionV3/Mixed_7c'))
self.assertListEqual(final_endpoint.get_shape().as_list(),
[batch_size, 8, 8, 2048])
expected_endpoints = [
'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'MaxPool_3a_3x3',
'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', 'Mixed_5b',
'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'
]
self.assertItemsEqual(end_points.keys(), expected_endpoints)
示例2: testBuildOnlyUptoFinalEndpoint
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testBuildOnlyUptoFinalEndpoint(self):
batch_size = 5
height, width = 299, 299
endpoints = [
'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', 'MaxPool_3a_3x3',
'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', 'Mixed_5b',
'Mixed_5c', 'Mixed_5d', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d',
'Mixed_6e', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c'
]
for index, endpoint in enumerate(endpoints):
with ops.Graph().as_default():
inputs = random_ops.random_uniform((batch_size, height, width, 3))
out_tensor, end_points = inception_v3.inception_v3_base(
inputs, final_endpoint=endpoint)
self.assertTrue(
out_tensor.op.name.startswith('InceptionV3/' + endpoint))
self.assertItemsEqual(endpoints[:index + 1], end_points)
示例3: testBuildEndPointsWithDepthMultiplierLessThanOne
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = random_ops.random_uniform((batch_size, height, width, 3))
_, end_points = inception_v3.inception_v3(inputs, num_classes)
endpoint_keys = [
key for key in end_points.keys()
if key.startswith('Mixed') or key.startswith('Conv')
]
_, end_points_with_multiplier = inception_v3.inception_v3(
inputs, num_classes, scope='depth_multiplied_net', depth_multiplier=0.5)
for key in endpoint_keys:
original_depth = end_points[key].get_shape().as_list()[3]
new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
self.assertEqual(0.5 * original_depth, new_depth)
示例4: testBuildEndPointsWithDepthMultiplierGreaterThanOne
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = random_ops.random_uniform((batch_size, height, width, 3))
_, end_points = inception_v3.inception_v3(inputs, num_classes)
endpoint_keys = [
key for key in end_points.keys()
if key.startswith('Mixed') or key.startswith('Conv')
]
_, end_points_with_multiplier = inception_v3.inception_v3(
inputs, num_classes, scope='depth_multiplied_net', depth_multiplier=2.0)
for key in endpoint_keys:
original_depth = end_points[key].get_shape().as_list()[3]
new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
self.assertEqual(2.0 * original_depth, new_depth)
示例5: testUnknownImageShape
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testUnknownImageShape(self):
ops.reset_default_graph()
batch_size = 2
height, width = 299, 299
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = array_ops.placeholder(
dtypes.float32, shape=(batch_size, None, None, 3))
logits, end_points = inception_v3.inception_v3(inputs, num_classes)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Mixed_7c']
feed_dict = {inputs: input_np}
variables.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
示例6: testTrainEvalWithReuse
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
train_inputs = random_ops.random_uniform(
(train_batch_size, height, width, 3))
inception_v3.inception_v3(train_inputs, num_classes)
eval_inputs = random_ops.random_uniform((eval_batch_size, height, width, 3))
logits, _ = inception_v3.inception_v3(
eval_inputs, num_classes, is_training=False, reuse=True)
predictions = math_ops.argmax(logits, 1)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
示例7: build_graph
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def build_graph(self):
"""Forms the core by building a wrapper around the inception graph.
Here we add the necessary input & output tensors, to decode jpegs,
serialize embeddings, restore from checkpoint etc.
To use other Inception models modify this file. Note that to use other
models beside Inception, you should make sure input_shape matches
their input. Resizing or other modifications may be necessary as well.
See tensorflow/contrib/slim/python/slim/nets/inception_v3.py for
details about InceptionV3.
Returns:
input_jpeg: A tensor containing raw image bytes as the input layer.
embedding: The embeddings tensor, that will be materialized later.
"""
input_jpeg = tf.placeholder(tf.string, shape=None)
image = tf.image.decode_jpeg(input_jpeg, channels=self.CHANNELS)
# Note resize expects a batch_size, but we are feeding a single image.
# So we have to expand then squeeze. Resize returns float32 in the
# range [0, uint8_max]
image = tf.expand_dims(image, 0)
# convert_image_dtype also scales [0, uint8_max] -> [0 ,1).
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.image.resize_bilinear(
image, [self.HEIGHT, self.WIDTH], align_corners=False)
# Then rescale range to [-1, 1) for Inception.
image = tf.subtract(image, 0.5)
inception_input = tf.multiply(image, 2.0)
# Build Inception layers, which expect a tensor of type float from [-1, 1)
# and shape [batch_size, height, width, channels].
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(inception_input, is_training=False)
embedding = end_points['PreLogits']
return input_jpeg, embedding
示例8: testBuildClassificationNetwork
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testBuildClassificationNetwork(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, end_points = inception_v3.inception_v3(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Predictions' in end_points)
self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
[batch_size, num_classes])
示例9: testBuildAndCheckAllEndPointsUptoMixed7c
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testBuildAndCheckAllEndPointsUptoMixed7c(self):
batch_size = 5
height, width = 299, 299
inputs = random_ops.random_uniform((batch_size, height, width, 3))
_, end_points = inception_v3.inception_v3_base(
inputs, final_endpoint='Mixed_7c')
endpoints_shapes = {
'Conv2d_1a_3x3': [batch_size, 149, 149, 32],
'Conv2d_2a_3x3': [batch_size, 147, 147, 32],
'Conv2d_2b_3x3': [batch_size, 147, 147, 64],
'MaxPool_3a_3x3': [batch_size, 73, 73, 64],
'Conv2d_3b_1x1': [batch_size, 73, 73, 80],
'Conv2d_4a_3x3': [batch_size, 71, 71, 192],
'MaxPool_5a_3x3': [batch_size, 35, 35, 192],
'Mixed_5b': [batch_size, 35, 35, 256],
'Mixed_5c': [batch_size, 35, 35, 288],
'Mixed_5d': [batch_size, 35, 35, 288],
'Mixed_6a': [batch_size, 17, 17, 768],
'Mixed_6b': [batch_size, 17, 17, 768],
'Mixed_6c': [batch_size, 17, 17, 768],
'Mixed_6d': [batch_size, 17, 17, 768],
'Mixed_6e': [batch_size, 17, 17, 768],
'Mixed_7a': [batch_size, 8, 8, 1280],
'Mixed_7b': [batch_size, 8, 8, 2048],
'Mixed_7c': [batch_size, 8, 8, 2048]
}
self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys())
for endpoint_name in endpoints_shapes:
expected_shape = endpoints_shapes[endpoint_name]
self.assertTrue(endpoint_name in end_points)
self.assertListEqual(end_points[endpoint_name].get_shape().as_list(),
expected_shape)
示例10: testModelHasExpectedNumberOfParameters
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testModelHasExpectedNumberOfParameters(self):
batch_size = 5
height, width = 299, 299
inputs = random_ops.random_uniform((batch_size, height, width, 3))
with arg_scope(inception_v3.inception_v3_arg_scope()):
inception_v3.inception_v3_base(inputs)
total_params, _ = model_analyzer.analyze_vars(
variables_lib.get_model_variables())
self.assertAlmostEqual(21802784, total_params)
示例11: testRaiseValueErrorWithInvalidDepthMultiplier
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testRaiseValueErrorWithInvalidDepthMultiplier(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = random_ops.random_uniform((batch_size, height, width, 3))
with self.assertRaises(ValueError):
_ = inception_v3.inception_v3(inputs, num_classes, depth_multiplier=-0.1)
with self.assertRaises(ValueError):
_ = inception_v3.inception_v3(inputs, num_classes, depth_multiplier=0.0)
示例12: testHalfSizeImages
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testHalfSizeImages(self):
batch_size = 5
height, width = 150, 150
num_classes = 1000
inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, end_points = inception_v3.inception_v3(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('InceptionV3/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Mixed_7c']
self.assertListEqual(pre_pool.get_shape().as_list(),
[batch_size, 3, 3, 2048])
示例13: testEvaluation
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testEvaluation(self):
batch_size = 2
height, width = 299, 299
num_classes = 1000
eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
logits, _ = inception_v3.inception_v3(
eval_inputs, num_classes, is_training=False)
predictions = math_ops.argmax(logits, 1)
with self.test_session() as sess:
sess.run(variables.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (batch_size,))
示例14: testLogitsNotSqueezed
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def testLogitsNotSqueezed(self):
num_classes = 25
images = random_ops.random_uniform([1, 299, 299, 3])
logits, _ = inception_v3.inception_v3(
images, num_classes=num_classes, spatial_squeeze=False)
with self.test_session() as sess:
variables.global_variables_initializer().run()
logits_out = sess.run(logits)
self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
示例15: network_inception_v3
# 需要导入模块: from tensorflow.contrib.slim.python.slim.nets import inception_v3 [as 别名]
# 或者: from tensorflow.contrib.slim.python.slim.nets.inception_v3 import inception_v3 [as 别名]
def network_inception_v3():
input_shape = [1, 224, 224, 3]
input_ = tf.placeholder(dtype=tf.float32, name='input', shape=input_shape)
net, _end_points = inception_v3(input_, num_classes=1000, is_training=False)
return net