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Python MLP.accuracy方法代码示例

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={
开发者ID:perellonieto,项目名称:deep_calibration,代码行数:70,代码来源:train.py


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