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Python MLP.cost方法代码示例

本文整理汇总了Python中mlp.MLP.cost方法的典型用法代码示例。如果您正苦于以下问题:Python MLP.cost方法的具体用法?Python MLP.cost怎么用?Python MLP.cost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.cost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: train

# 需要导入模块: from mlp import MLP [as 别名]
# 或者: from mlp.MLP import cost [as 别名]
    def train(self, X, Y, learning_rate=0.1, n_epochs=100, report_frequency=10, lambda_l2=0.0):

        self.report_frequency = report_frequency 

        # allocate symbolic variables for the data
        x = T.matrix('x')  
        y = T.matrix('y')  

        # put the data in shared memory
        self.shared_x = theano.shared(numpy.asarray(X, dtype=theano.config.floatX))
        self.shared_y = theano.shared(numpy.asarray(Y, dtype=theano.config.floatX))
        rng = numpy.random.RandomState(1234)

        # initialize the mlp
        mlp = MLP(rng=rng, input=x, n_in=self.n_in, n_out=self.n_out,
                  n_hidden=self.n_hidden, activation=self.activation)

        # define the cost function, possibly with regularizing term
        if lambda_l2>0.0:
            cost = mlp.cost(y) + lambda_l2*mlp.l2
        else:
            cost = mlp.cost(y) 

        # compute the gradient of cost with respect to theta (stored in params)
        # the resulting gradients will be stored in a list gparams
        gparams = [T.grad(cost, param) for param in mlp.params]

        updates = [(param, param - learning_rate * gparam)
            for param, gparam in zip(mlp.params, gparams) ]

        # compiling a Theano function `train_model` that returns the cost, but
        # at the same time updates the parameter of the model based on the rules
        # defined in `updates`
        train_model = theano.function(
            inputs=[],
            outputs=cost,
            updates=updates,
            givens={
                x: self.shared_x,
                y: self.shared_y
            }
        )

        #define function that returns model prediction
        self.predict_model = theano.function(
            inputs=[mlp.input], outputs=mlp.y_pred)

        ###############
        # TRAIN MODEL #
        ###############

        epoch = 0

        while (epoch < n_epochs):
            epoch = epoch + 1
            epoch_cost = train_model()
            if epoch % self.report_frequency == 0:
                print("epoch: %d  cost: %f" % (epoch, epoch_cost))
开发者ID:TianqiJiang,项目名称:Machine-Learning-Class,代码行数:60,代码来源:function_approximator.py


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