本文整理汇总了Python中inception.slim.slim.arg_scope方法的典型用法代码示例。如果您正苦于以下问题：Python slim.arg_scope方法的具体用法？Python slim.arg_scope怎么用？Python slim.arg_scope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inception.slim.slim的用法示例。
在下文中一共展示了slim.arg_scope方法的7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: testTotalLossWithoutRegularization
# 需要导入模块: from inception.slim import slim [as 别名]
# 或者: from inception.slim.slim import arg_scope [as 别名]
def testTotalLossWithoutRegularization(self):
batch_size = 5
height, width = 299, 299
num_classes = 1001
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
dense_labels = tf.random_uniform((batch_size, num_classes))
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0):
logits, end_points = slim.inception.inception_v3(
inputs,
num_classes=num_classes)
# Cross entropy loss for the main softmax prediction.
slim.losses.cross_entropy_loss(logits,
dense_labels,
label_smoothing=0.1,
weight=1.0)
# Cross entropy loss for the auxiliary softmax head.
slim.losses.cross_entropy_loss(end_points['aux_logits'],
dense_labels,
label_smoothing=0.1,
weight=0.4,
scope='aux_loss')
losses = tf.get_collection(slim.losses.LOSSES_COLLECTION)
self.assertEqual(len(losses), 2) 示例2: testTotalLossWithRegularization
# 需要导入模块: from inception.slim import slim [as 别名]
# 或者: from inception.slim.slim import arg_scope [as 别名]
def testTotalLossWithRegularization(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
dense_labels = tf.random_uniform((batch_size, num_classes))
with slim.arg_scope([slim.ops.conv2d, slim.ops.fc], weight_decay=0.00004):
logits, end_points = slim.inception.inception_v3(inputs, num_classes)
# Cross entropy loss for the main softmax prediction.
slim.losses.cross_entropy_loss(logits,
dense_labels,
label_smoothing=0.1,
weight=1.0)
# Cross entropy loss for the auxiliary softmax head.
slim.losses.cross_entropy_loss(end_points['aux_logits'],
dense_labels,
label_smoothing=0.1,
weight=0.4,
scope='aux_loss')
losses = tf.get_collection(slim.losses.LOSSES_COLLECTION)
self.assertEqual(len(losses), 2)
reg_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
self.assertEqual(len(reg_losses), 98) 示例3: testVariables
# 需要导入模块: from inception.slim import slim [as 别名]
# 或者: from inception.slim.slim import arg_scope [as 别名]
def testVariables(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d],
batch_norm_params={'decay': 0.9997}):
slim.inception.inception_v3(inputs)
self.assertEqual(len(get_variables()), 388)
self.assertEqual(len(get_variables_by_name('weights')), 98)
self.assertEqual(len(get_variables_by_name('biases')), 2)
self.assertEqual(len(get_variables_by_name('beta')), 96)
self.assertEqual(len(get_variables_by_name('gamma')), 0)
self.assertEqual(len(get_variables_by_name('moving_mean')), 96)
self.assertEqual(len(get_variables_by_name('moving_variance')), 96) 示例4: testVariablesWithoutBatchNorm
# 需要导入模块: from inception.slim import slim [as 别名]
# 或者: from inception.slim.slim import arg_scope [as 别名]
def testVariablesWithoutBatchNorm(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d],
batch_norm_params=None):
slim.inception.inception_v3(inputs)
self.assertEqual(len(get_variables()), 196)
self.assertEqual(len(get_variables_by_name('weights')), 98)
self.assertEqual(len(get_variables_by_name('biases')), 98)
self.assertEqual(len(get_variables_by_name('beta')), 0)
self.assertEqual(len(get_variables_by_name('gamma')), 0)
self.assertEqual(len(get_variables_by_name('moving_mean')), 0)
self.assertEqual(len(get_variables_by_name('moving_variance')), 0) 示例5: testVariablesByLayer
# 需要导入模块: from inception.slim import slim [as 别名]
# 或者: from inception.slim.slim import arg_scope [as 别名]
def testVariablesByLayer(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d],
batch_norm_params={'decay': 0.9997}):
slim.inception.inception_v3(inputs)
self.assertEqual(len(get_variables()), 388)
self.assertEqual(len(get_variables('conv0')), 4)
self.assertEqual(len(get_variables('conv1')), 4)
self.assertEqual(len(get_variables('conv2')), 4)
self.assertEqual(len(get_variables('conv3')), 4)
self.assertEqual(len(get_variables('conv4')), 4)
self.assertEqual(len(get_variables('mixed_35x35x256a')), 28)
self.assertEqual(len(get_variables('mixed_35x35x288a')), 28)
self.assertEqual(len(get_variables('mixed_35x35x288b')), 28)
self.assertEqual(len(get_variables('mixed_17x17x768a')), 16)
self.assertEqual(len(get_variables('mixed_17x17x768b')), 40)
self.assertEqual(len(get_variables('mixed_17x17x768c')), 40)
self.assertEqual(len(get_variables('mixed_17x17x768d')), 40)
self.assertEqual(len(get_variables('mixed_17x17x768e')), 40)
self.assertEqual(len(get_variables('mixed_8x8x2048a')), 36)
self.assertEqual(len(get_variables('mixed_8x8x2048b')), 36)
self.assertEqual(len(get_variables('logits')), 2)
self.assertEqual(len(get_variables('aux_logits')), 10) 示例6: testVariablesToRestore
# 需要导入模块: from inception.slim import slim [as 别名]
# 或者: from inception.slim.slim import arg_scope [as 别名]
def testVariablesToRestore(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d],
batch_norm_params={'decay': 0.9997}):
slim.inception.inception_v3(inputs)
variables_to_restore = tf.get_collection(
slim.variables.VARIABLES_TO_RESTORE)
self.assertEqual(len(variables_to_restore), 388)
self.assertListEqual(variables_to_restore, get_variables()) 示例7: testVariablesToRestoreWithoutLogits
# 需要导入模块: from inception.slim import slim [as 别名]
# 或者: from inception.slim.slim import arg_scope [as 别名]
def testVariablesToRestoreWithoutLogits(self):
batch_size = 5
height, width = 299, 299
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope([slim.ops.conv2d],
batch_norm_params={'decay': 0.9997}):
slim.inception.inception_v3(inputs, restore_logits=False)
variables_to_restore = tf.get_collection(
slim.variables.VARIABLES_TO_RESTORE)
self.assertEqual(len(variables_to_restore), 384)