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tf.train.adamoptimizer.minimiz

时间: 2023-04-26 22:06:10 浏览: 257

visualize the neural network.pdf

Neural network training relies on our ability to find “good” minimizers of highly non-convex loss functions. It is well known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and well- chosen training parameters (batch size, learning rate, optimizer) produce minimiz- ers that generalize better. However, the reasons for these differences, and their effect on the underlying loss landscape, is not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple “filter normalization” method that helps us visualize loss function curvature, and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network archi- tecture affects the loss landscape, and how training parameters affect the shape of minimizers.

e() tf.train.AdamOptimizer.minimize()是TensorFlow中Adam优化器的一个方法，用于最小化损失函数。Adam优化器是一种基于梯度下降的优化算法，它可以自适应地调整学习率，从而更快地收敛到最优解。在训练神经网络时，通常会使用Adam优化器来更新模型的参数，以使损失函数最小化。

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