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Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technique which can perform complex amplitude imaging with both large field of view and high resolution by using a simple microscope setup. Here, we propose a transformer based neural network named as FP-transformer, which takes the low-resolution amplitude (LRA) images as the sequential input and uses self-attention mechanism to compute the relationship among them. The high-resolution FPM complex amplitude reconstruction is the end-to-end output of the FP-transformer. We apply the image library of div2k to generate the FPM LRA images with the physical model, and then perform the training and validation with this dataset containing ground truth. We also perform the validation with the experiment images and it is found that the high-quality FPM complex amplitude image pairs can be obtained. Therefore, the FP-transformer creates a new platform for the FPM deep learning reconstruction, which has the better dependability and adaptability.The code of this work will be available at <uri>https://github.com/zhaolin6/FPTransfomer</uri> for the sake of reproducibility.