Implement GELU Activation Function in TensorFlow – TensorFlow Tutorial
In this tutorial, we will introduce how to implement gelu activation function in tensorflow.
TensorFlow Tutorials and Examples for Beginners
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.
Implement Scale-invariant Source-to-noise Ratio (SI-SNR) in TensorFlow
In this tutorial, we will introduce how to implement Scale-invariant Source-to-noise Ratio (SI-SNR) using tensorflow.
Implement TensorFlow log10 Function: A Step Guide – TensorFlow Tutorial
There is not log10(x) in tensorflow. In this tutorial, we will create a function named log10(x) to compuate \(log10(x)\).
tf.roll(): Rolls the Elements of a Tensor Along an Axis – TensorFlow Tutorial
In this example, we will use some examples to show you how to use tf.roll() function correctly.
Fix tf.map_fn() ValueError: The two structures don’t have the same nested structure – TensorFlow Tutorial
When we are using tf.map_fn() function, we may get this error: ValueError: The two structures don’t have the same nested structure. In this tutorial, we will introduce you how to fix it.
Get Forward and Backward LSTM Output of Bi-LSTM in TensorFlow – TensorFlow Tutorial
![We usually use Bi-LSTM or Bi-GRU to model sequence. How to get its output? In this tutorial, we will discuss this topic. Bi-LSTM or Bi-GRU A Bi-LSTM or Bi-GRU looks like below: As to Bi-LSTM, it contains a forward LSTM and backword LSTM. How to create Bi-LSTM in TensorFlow? In tensroflow, we can use tf.nn.bidirectional_dynamic_rnn() to create a Bi-LSTM network. Here is an example: import tensorflow as tf import numpy as np #5*4*10 inputs = tf.Variable(tf.truncated_normal([3, 5, 100], stddev=0.1), name="inputs") hidden_size = 10 outputs, state = tf.nn.bidirectional_dynamic_rnn( cell_fw=tf.nn.rnn_cell.LSTMCell(hidden_size, forget_bias=1.0), cell_bw=tf.nn.rnn_cell.LSTMCell(hidden_size, forget_bias=1.0), inputs=inputs, sequence_length= None, dtype=tf.float32, scope='bilstm_doc_word' ) You should notice the inputs tensor of Bi-LSTM is [batch_size, max_seq_length, dim]. In this example, it is [3, 5, 100]. How to get the output of Bi-LSTM? There are three types of outputs in Bi-LSTM. Type 1: concatenating the output of forward and backward LSTM. outputs_merge = tf.concat(outputs, 2) Then you will get a output with shape [batch_size, max_seq_length, 2*hidden_dim], here hidden_dim is the hidden state size in lstm. As to example above, you will find outputs_merge is [3, 5, 2*10] Type 2: Get the mean output of the forward and backward LSTM. You can find how to do in this tutorial: Average the Output of RNN/GRU/LSTM/BiLSTM for Variable Length Sequences – Deep Learning Tutorial Type 3: Get the last hidden output of the forward and backward LSTM. Here is an example: last_forward = outputs[0][:,-1,:] # 3*5*10 last_backward = outputs[1][:,0,:] init = tf.global_variables_initializer() init_local = tf.local_variables_initializer() with tf.Session() as sess: sess.run([init, init_local]) np.set_printoptions(precision=4, suppress=True) f =sess.run(last_forward) b = sess.run(last_backward) m = sess.run(outputs_merge) print("last forward shape=", f.shape) print(f) print("last backward shape=", b.shape) print(b) print("concatenate forward and backward") print(m) Run this code, you will see:](https://www.tutorialexample.com/wp-content/uploads/2021/11/Get-Forward-and-Backward-LSTM-Output-of-Bi-LSTM-in-TensorFlow-TensorFlow-Tutorial-200x140.png)
We usually use Bi-LSTM or Bi-GRU to model sequence. How to get its output? In this tutorial, we will discuss this topic.
A Completed Guide to Train Your Own Model Based on an Existing TensorFlow Model – TensorFlow Tutorial
In this tutorial, we will introduce how to train you own tensorflow model which is built on an existing model. You can learn how to do by our steps.
A Beginner Guide to Get the Output and Weights from an Existing TensorFlow Model – TensorFlow Tutorial
In this tutorial, we will introduce how to get the output and weights value from an existing tensorflow model.
Only Initialize New Variables When Using an Existing Model for Fine-tuning – TensorFlow Tutorial
In this tutorial, we will introduce how to initialize variables when we plan to build a new model based on an existing tensorflow model.
A Beginner Guide to Get Stable Result in TensorFlow – TensorFlow Tutorial
In this tutorial, we will introduce you how to make the tensorflow model result is stable and get repeated result.