TensorFlow is an open source platform for machine learning from Google. It can make us to build some AI applications easily. It is a popular deep learning platform in word.
In this page, we write some tutorials and examples on how to use tensorflow, you can build some AI applications by following our tutorials and examples.
In this tutorial, we will introduce you that using GraphKeys.REGULARIZATION_LOSSES to implement L2 and L1 regularization loss in tensorflow.
In this tutorial, we will introduce how to fix Entity
To implement 2D convolution operation, we can use tf.layers.conv2d(). In this tutorial, we will use some examples to show you how to use it correctly.
Batch normalization is widely used in neural networks. In this tutorial, we will introduce how to use it in tensorflow.
In tensorflow, if we want to count the number of each value in a tensor, we can use tf.unique_with_counts() to implement. In this tutorial, we will use some examples to show you how to do.
In this tutorial, we will introduce how to fix Tensor’ object has no attribute ‘assign’ error. You can do by following our steps.
Normal Distribution (Gaussian Distribution) is widely used in deep learning model. In this tutorial, we will introduce how to compute it in tensorflow.
Linear-Chain CRF, which is also called linear-chain conditional random field algorithm, is widely used for Named Entity Recognition (NER). In this tutorial, we will introduce some basic knowlege on it in tensorflow.
TensorFlow tf.nn.batch_normalization() function can normalize a layer in batch. In this tutorial, we will use some examples to show you how to use it.
TensorFlow tf.tensordot() is a powerful function to multiply tensors. It allows us to multiply different rank tensors. In this tutorial, we will use some examples to show you how to use this function.