本文整理汇总了Python中mlp.MLP.scores方法的典型用法代码示例。如果您正苦于以下问题：Python MLP.scores方法的具体用法？Python MLP.scores怎么用？Python MLP.scores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.scores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 scores [as 别名]
#.........这里部分代码省略.........
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={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
}
)
validation_predictions_model = theano.function(
inputs=[index],
outputs=classifier.predictions(),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
}
)
# compiling a Theano function that computes the predictions on the
# training data
training_scores_model = theano.function(
inputs=[index],
outputs=classifier.scores(),
givens={
x: train_set_x[index * batch_size:(index + 1) * batch_size],
}
)
validation_scores_model = theano.function(
inputs=[index],
outputs=classifier.scores(),
givens={
x: valid_set_x[index * batch_size:(index + 1) * batch_size],
}
)
# start-snippet-5
# compute the gradient of cost with respect to theta (sotred in params)
# the resulting gradients will be stored in a list gparams
gparams = [T.grad(cost, param) for param in classifier.params]
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs
# given two lists of the same length, A = [a1, a2, a3, a4] and
# B = [b1, b2, b3, b4], zip generates a list C of same size, where each
# element is a pair formed from the two lists :
# C = [(a1, b1), (a2, b2), (a3, b3), (a4, b4)]
updates = [
(param, param - learning_rate * gparam)
for param, gparam in zip(classifier.params, gparams)
]