本文整理汇总了Python中mlp.MLP.accuracy方法的典型用法代码示例。如果您正苦于以下问题：Python MLP.accuracy方法的具体用法？Python MLP.accuracy怎么用？Python MLP.accuracy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mlp.MLP的用法示例。
在下文中一共展示了MLP.accuracy方法的1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 mlp import MLP [as 别名]
# 或者: from mlp.MLP import accuracy [as 别名]
#.........这里部分代码省略.........
)
# compiling a Theano function that computes the mistakes that are made
# by the model on a minibatch
test_loss_model = theano.function(
inputs=[index],
outputs=classifier.loss(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
}
)
validation_loss_model = theano.function(
inputs=[index],
outputs=classifier.loss(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
}
)
training_loss_model = theano.function(
inputs=[index],
outputs=classifier.loss(y),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
}
)
# compiling a Theano function that computes the mistakes that are made
# by the model on a minibatch
test_accuracy_model = theano.function(
inputs=[index],
outputs=classifier.accuracy(y),
givens={
x: test_set_x[index * batch_size:(index + 1) * batch_size],
y: test_set_y[index * batch_size:(index + 1) * batch_size]
}
)
validation_accuracy_model = theano.function(
inputs=[index],
outputs=classifier.accuracy(y),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
y: valid_set_y[index * batch_size:(index + 1) * batch_size]
}
)
training_accuracy_model = theano.function(
inputs=[index],
outputs=classifier.accuracy(y),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
y: train_set_y[index * batch_size:(index + 1) * batch_size]
}
)
# compiling a Theano function that computes the predictions on the
# training data
training_predictions_model = theano.function(
inputs=[index],
outputs=classifier.predictions(),
givens={