本文整理汇总了Python中LogisticRegression.LogisticRegression.getPrediction方法的典型用法代码示例。如果您正苦于以下问题：Python LogisticRegression.getPrediction方法的具体用法？Python LogisticRegression.getPrediction怎么用？Python LogisticRegression.getPrediction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LogisticRegression.LogisticRegression的用法示例。
在下文中一共展示了LogisticRegression.getPrediction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: evaluate_lenet5
# 需要导入模块: from LogisticRegression import LogisticRegression [as 别名]
# 或者: from LogisticRegression.LogisticRegression import getPrediction [as 别名]
#.........这里部分代码省略.........
# the cost we minimize during training is the NLL of the model
cost = layer3.loss_nll(y)
# create a function to compute the mistakes that are made by the model
train_errors = theano.function(
inputs=[index],
outputs=layer3.prediction_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],
},
)
test_model = theano.function(
[index],
layer3.prediction_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],
},
)
validate_model = theano.function(
[index],
layer3.prediction_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],
},
)
#######################Confusion matrix code######################################
confusion_model_train = theano.function(
[index], layer3.getPrediction(), givens={x: train_set_x[index * batch_size : (index + 1) * batch_size]}
)
confusion_model_validate = theano.function(
[index], layer3.getPrediction(), givens={x: valid_set_x[index * batch_size : (index + 1) * batch_size]}
)
confusion_model_test = theano.function(
[index], layer3.getPrediction(), givens={x: test_set_x[index * batch_size : (index + 1) * batch_size]}
)
confusion_model_train_y = theano.function(
[index], y, givens={y: train_set_y[index * batch_size : (index + 1) * batch_size]}
)
confusion_model_validate_y = theano.function(
[index], y, givens={y: valid_set_y[index * batch_size : (index + 1) * batch_size]}
)
confusion_model_test_y = theano.function(
[index], y, givens={y: test_set_y[index * batch_size : (index + 1) * batch_size]}
)
###################################################################################
# create a list of all model parameters to be fit by gradient descent
params = layer3.params + layer2.params + layer1.params + layer0.params
# create a list of gradients for all model parameters
grads = T.grad(cost, params)
# train_model is a function that updates the model parameters by
# SGD Since this model has many parameters, it would be tedious to
# manually create an update rule for each model parameter. We thus
# create the updates list by automatically looping over all
# (params[i],grads[i]) pairs.