本文整理汇总了Python中lasagne.objectives.categorical_accuracy方法的典型用法代码示例。如果您正苦于以下问题：Python objectives.categorical_accuracy方法的具体用法？Python objectives.categorical_accuracy怎么用？Python objectives.categorical_accuracy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lasagne.objectives的用法示例。
在下文中一共展示了objectives.categorical_accuracy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: build_model
# 需要导入模块: from lasagne import objectives [as 别名]
# 或者: from lasagne.objectives import categorical_accuracy [as 别名]
def build_model(self):
import theano.tensor as T
self.x = T.ftensor4('x')
self.y = T.lvector('y')
self.lr = T.scalar('lr')
net = build_model_vgg16(input_shape=(None, 3, 224, 224), verbose=self.verbose)
self.output_layer = net['prob']
from lasagne.layers import get_output
self.output = lasagne.layers.get_output(self.output_layer, self.x, deterministic=False)
self.cost = lasagne.objectives.categorical_crossentropy(self.output, self.y).mean()
from lasagne.objectives import categorical_accuracy
self.error = 1-categorical_accuracy(self.output, self.y, top_k=1).mean()
self.error_top_5 = 1-categorical_accuracy(self.output, self.y, top_k=5).mean() 示例2: compile_val
# 需要导入模块: from lasagne import objectives [as 别名]
# 或者: from lasagne.objectives import categorical_accuracy [as 别名]
def compile_val(self):
if self.verbose: print('compiling validation function...')
import theano
from lasagne.layers import get_output
output_val = lasagne.layers.get_output(self.output_layer, self.x, deterministic=True)
from lasagne.objectives import categorical_accuracy, categorical_crossentropy
cost = categorical_crossentropy(output_val, self.y).mean()
error = 1-categorical_accuracy(output_val, self.y, top_k=1).mean()
error_top_5 = 1-categorical_accuracy(output_val, self.y, top_k=5).mean()
self.val_fn= theano.function([self.subb_ind], [cost,error,error_top_5], updates=[],
givens=[(self.x, self.shared_x_slice),
(self.y, self.shared_y_slice)]
) 示例3: build_model
# 需要导入模块: from lasagne import objectives [as 别名]
# 或者: from lasagne.objectives import categorical_accuracy [as 别名]
def build_model(self):
import theano.tensor as T
self.x = T.ftensor4('x')
self.y = T.lvector('y')
self.lr = T.scalar('lr')
net = build_model_resnet50(input_shape=(None, 3, 224, 224))
if self.verbose: print('Total number of layers:', len(lasagne.layers.get_all_layers(net['prob'])))
self.output_layer = net['prob']
from lasagne.layers import get_output
self.output = lasagne.layers.get_output(self.output_layer, self.x, deterministic=False)
self.cost = lasagne.objectives.categorical_crossentropy(self.output, self.y).mean()
from lasagne.objectives import categorical_accuracy
self.error = 1-categorical_accuracy(self.output, self.y, top_k=1).mean()
self.error_top_5 = 1-categorical_accuracy(self.output, self.y, top_k=5).mean() 示例4: build_model
# 需要导入模块: from lasagne import objectives [as 别名]
# 或者: from lasagne.objectives import categorical_accuracy [as 别名]
def build_model(self):
import theano.tensor as T
self.x = T.ftensor4('x')
self.y = T.lvector('y')
self.lr = T.scalar('lr')
net = build_model_resnet152(input_shape=(None, 3, 224, 224))
self.output_layer = net['prob']
from lasagne.layers import get_output
self.output = lasagne.layers.get_output(self.output_layer, self.x, deterministic=False)
self.cost = lasagne.objectives.categorical_crossentropy(self.output, self.y).mean()
from lasagne.objectives import categorical_accuracy
self.error = 1-categorical_accuracy(self.output, self.y, top_k=1).mean()
self.error_top_5 = 1-categorical_accuracy(self.output, self.y, top_k=5).mean() 示例5: calc_accuracy
# 需要导入模块: from lasagne import objectives [as 别名]
# 或者: from lasagne.objectives import categorical_accuracy [as 别名]
def calc_accuracy(prediction, targets):
#we can use the lasagne objective categorical_accuracy to determine the top1 single label accuracy
a = T.mean(objectives.categorical_accuracy(prediction, targets, top_k=1))
return a 示例6: calc_accuracy
# 需要导入模块: from lasagne import objectives [as 别名]
# 或者: from lasagne.objectives import categorical_accuracy [as 别名]
def calc_accuracy(prediction, targets):
# We can use the lasagne objective categorical_accuracy to determine the top1 single label accuracy
a = T.mean(objectives.categorical_accuracy(prediction, targets, top_k=1))
return a 示例7: __init__
# 需要导入模块: from lasagne import objectives [as 别名]
# 或者: from lasagne.objectives import categorical_accuracy [as 别名]
def __init__(self):
# Define inputs
input_var = T.ftensor4('input_var') # input images (batchx3x64x64)
labels_classifier_var = T.ivector('labels_classifier_var') # labels for images
labels_domain_var = T.ivector('labels_domain_var') # labels for domain (1 for source, 0 for target)
learning_rate = T.fscalar('learning_rate')
# Define classifier networks
network_classifier = self.network_classifier(input_var)
network_discriminator = self.network_discriminator(network_classifier['classifier/pool1'])
# Define outputs
prediction_classifier = get_output(network_classifier['classifier/output']) # prob image classification
prediction_discriminator = get_output(network_discriminator['discriminator/output']) # prob image domain (should be 1 for source)
# Define losses (objectives)
loss_classifier_only = T.mean(categorical_crossentropy(prediction_classifier, labels_classifier_var) * labels_domain_var)
loss_discriminator = T.mean(categorical_crossentropy(prediction_discriminator, labels_domain_var))
loss_classifier = loss_classifier_only - loss_discriminator
# Define performance
perf_classifier_only = categorical_accuracy(prediction_classifier, labels_classifier_var).mean()
perf_discriminator = categorical_accuracy(prediction_discriminator, labels_domain_var).mean()
# Define params
params_classifier = lasagne.layers.get_all_params(network_classifier['classifier/output'], trainable=True)
params_discriminator = lasagne.layers.get_all_params(network_discriminator['discriminator/output'], trainable=True)
params_discriminator = [param for param in params_discriminator if 'discriminator' in param.name]
# Define updates
updates_classifier = lasagne.updates.adam(loss_classifier, params_classifier, learning_rate=learning_rate)
updates_classifier_only = lasagne.updates.adam(loss_classifier_only, params_classifier, learning_rate=learning_rate)
updates_discriminator = lasagne.updates.adam(loss_discriminator, params_discriminator, learning_rate=learning_rate)
# Define training functions
self.train_fn_classifier = theano.function(
[input_var, labels_classifier_var, labels_domain_var, learning_rate],
[loss_classifier, loss_classifier_only, prediction_classifier],
updates=updates_classifier)
self.train_fn_classifier_only = theano.function(
[input_var, labels_classifier_var, labels_domain_var, learning_rate],
[loss_classifier, loss_classifier_only, prediction_classifier],
updates=updates_classifier_only)
self.train_fn_discriminator = theano.function(
[input_var, labels_domain_var, learning_rate],
[loss_discriminator, prediction_discriminator],
updates=updates_discriminator)
# Define validation functions
self.valid_fn_classifier = theano.function(
[input_var, labels_classifier_var],
[perf_classifier_only, prediction_classifier])
self.valid_fn_discriminator = theano.function(
[input_var, labels_domain_var],
[perf_discriminator, prediction_discriminator])