scalenettrainer.py 文件源码

def setupTrain(self):
        # train_model is a function that updates the model parameters by SGD

        opt = Optimizer(self.grads, self.params)
        self.updates = opt.RMSProp(self.learning_rate, 0.9, 1.0/100.)

        batch_size = self.cfgParams.batch_size
        givens_train = {self.x[0]: self.train_data_x[self.index * batch_size:(self.index + 1) * batch_size]}
        for i in range(1, self.poseNet.cfgParams.numInputs):
            givens_train[self.x[i]] = getattr(self, 'train_data_x'+str(i))[self.index * batch_size:(self.index + 1) * batch_size]
        givens_train[self.y] = self.train_data_y[self.index * batch_size:(self.index + 1) * batch_size]

        print("compiling train_model() ... ")
        self.train_model = theano.function(inputs=[self.index, self.learning_rate],
                                           outputs=self.cost,
                                           updates=self.updates,
                                           givens=givens_train)

        print("done.")

        print("compiling test_model_on_train() ... ")
        batch_size = self.cfgParams.batch_size
        givens_test_on_train = {self.x[0]: self.train_data_x[self.index * batch_size:(self.index + 1) * batch_size]}
        for i in range(1, self.poseNet.cfgParams.numInputs):
            givens_test_on_train[self.x[i]] = getattr(self, 'train_data_x'+str(i))[self.index * batch_size:(self.index + 1) * batch_size]
        givens_test_on_train[self.y] = self.train_data_y[self.index * batch_size:(self.index + 1) * batch_size]

        self.test_model_on_train = theano.function(inputs=[self.index],
                                                   outputs=self.errors,
                                                   givens=givens_test_on_train)
        print("done.")