In order to repeat data to expand a tensor, we can use tf.tile() function. In this tutorial, we will use some examples to show you how to this function correctly.
To shield some elements in tensor, we can use a mask tesnor. In this tutorial, we will introduce tf.sequence_mask(), which can create a mask tensor.
If you need to shield some elements in a tensor, tf.boolean_mask() may be a good choice. In this tutorial, we will use some examples to show you how to use it correctly.
TensorFlow tf.svd() function can not be run on gpu, we should run it on cpu. In this tutorial, we will use a simple code to make tf.svd() run on cpu.
We often use gpu in tensorflow, which can save our lots of time. Your computer may have have installed gpu, however, do you know the gpu is suitable for tensorflow in your computer?
We often use GPU to speed up tensorflow. Do you know what gpu is installed in your windows 10 system or what version gpu is installed in your computer? In this tutorial, we will introduce you how do do.
We often use tf.train.import_meta_graph() to load a saved tensorflow model to use, however, you may find a KeyError: ‘BatchMatMulV2’. In this tutorial, we will introduce how to fix this error.
TensorFlow tf.sqrt() function may occur NaN error. In this tutorial, we will use a simple tip to fix it.
Euclidean Distance is common used to be a loss function in deep learning. In this tutorial, we will introduce how to calculate euclidean distance of two tensors.
TensorFlow tf.map_fn() method can allow us to call a function for each element in a tensor on axis = 0. In this tutorial, we will use some simple examples to help you understand and use this function.