Structure of the weight matrix in hidden layer
where a, b, c are the parameters that shall be learned. This is to train a equivariant neural network. I need to implement a custom model using the Keras Subclassing API in tensorflow.Moreover, equivariant layers have no bias vectors.
So as the training progress, all the elements 'a' elements of the weight matrix should be same. likewise with b and c.
Related
Tensoflow Embedding Layer (https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding) is easy to use,
and there are massive articles talking about
"how to use" Embedding (https://machinelearningmastery.com/what-are-word-embeddings/, https://www.sciencedirect.com/topics/computer-science/embedding-method)
.
However, I want to know the Implemention of the very "Embedding Layer" in Tensorflow or Pytorch.
Is it a word2vec?
Is it a Cbow?
Is it a special Dense Layer?
Structure wise, both Dense layer and Embedding layer are hidden layers with neurons in it. The difference is in the way they operate on the given inputs and weight matrix.
A Dense layer performs operations on the weight matrix given to it by multiplying inputs to it ,adding biases to it and applying activation function to it. Whereas Embedding layer uses the weight matrix as a look-up dictionary.
The Embedding layer is best understood as a dictionary that maps integer indices (which stand for specific words) to dense vectors. It takes integers as input, it looks up these integers in an internal dictionary, and it returns the associated vectors. It’s effectively a dictionary lookup.
from keras.layers import Embedding
embedding_layer = Embedding(1000, 64)
Here 1000 means the number of words in the dictionary and 64 means the dimensions of those words. Intuitively, embedding layer just like any other layer will try to find vector (real numbers) of 64 dimensions [ n1, n2, ..., n64] for any word. This vector will represent the semantic meaning of that particular word. It will learn this vector while training using backpropagation just like any other layer.
When you instantiate an Embedding layer, its weights (its internal dictionary of token vectors) are initially random, just as with any other layer. During training, these word vectors are gradually adjusted via backpropagation, structuring the space into something the downstream model can exploit. Once fully trained, the embedding space will show a lot of structure—a kind of structure specialized for the specific problem for which you’re training your model.
-- Deep Learning with Python by F. Chollet
Edit - How "Backpropagation" is used to train the look-up matrix of the Embedding Layer ?
Embedding layer is similar to the linear layer without any activation function. Theoretically, Embedding layer also performs matrix multiplication but doesn't add any non-linearity to it by using any kind of activation function. So backpropagation in the Embedding layer is similar to as of any linear layer. But practically, we don't do any matrix multiplication in the embedding layer because the inputs are generally one hot encoded and the matrix multiplication of weights by a one-hot encoded vector is as easy as a look-up.
So I am new to computer vision, and I do not really know what the layers do in keras. What is the use of adding layers (dense, Conv2D, etc) in keras? What do they add to it?
Convolution neural network has 4 main steps: Convolution, Pooling, Flatten, and Full connection.
Conv2D(), Conv3D(), etc. is for Feature extraction (It's a Convolution Layer).
Pooling layers (MaxPool2D(), AvgPool2D(), etc) is for Feature extraction as well (It has different operation though).
Flattening layers (Flatten() ) are to convert the extracted feature map into Vector before being fed into the Fully connection layers (The Dense layers).
Dense layers are for Fully connected step in Computer vision that acts as Classifier (The Neural network classify each extracted features from the Convolution layers.)
There are also optimization layers such as Dropout(), BatchNormalization(), etc.
For more information, just open the keras documentation.
If you want to start learning Convolution neural network, this article may help.
A layer in an Artificial Neural Network is a bunch of nodes bound together at a specific depth in a Neural Network. Keras is a high level API used over NN modules like TensorFlow or CNTK in order to simplify tasks. A Keras layer comprises 3 main parts:
Input Layer - Which contains the raw data
Hidden layer - Where the nodes of a layer learn some aspects about
the raw data which is input. It's similar to levels of abstraction
to form a Neural network.
Output Layer - Consists of a single output which is mostly a single
node and can be subjected to classification.
Keras, as a whole consists of many different types of layers. A Convolutional layer creates a kernel which is convoluted with the input over a single temporal space to derive a group of outputs. Pooling layers provide sampling of the feature maps by simplifying features in a map into patches. Max Pooling and Average Pooling are commonly used methods in a Pool layer.
Other commonly used layers in Keras are Embedding layers, Noise layers and Core layers. A single NN layer can represent only a Linearly seperable method. Most prediction problems are complicated and more than just one layer is required. This is where Multi Layer concept is required.
I think i clear your doubts and for any other queries you can see on https://www.tensorflow.org/api_docs/python/tf/keras
Neural networks are a great tool nowadays to automate classification problems. However when it comes to computer vision the amount of input data is too great to be handled efficiently by simple neural networks.
To reduce the network workload, your data needs to be preprocessed and certain features need to be identified. To find features in images we can use certain filters (like sobel edge detection), which will highlight the essential features needed for classification.
Again the amount of filters required to classify one image is too great, and thus the selection of those filters needs to be automated.
That's where the convolutional layer comes in.
We use a convolutional layer to generate multiple random (at first) filters that will highlight certain features in an image. While the network is training those filters are optimized to do a better job at highlighting features.
In Tensorflow we use Conv2D() to add one of those layers. An example of parameters is : Conv2D(64, 3, activation='relu'). 64 denotes the number of filters used, 3 denotes the size of the filters (in this case 3x3) and activation='relu' denotes the activation function
After the convolutional layer we use a pooling layer to further highlight the features produced by the previous convolutional layer. In Tensorflow this is usually done with MaxPooling2D() which takes the filtered image and applies a 2x2 (by default) layer every 2 pixels. The filter applied by MaxPooling is basically looking for the maximum value in that 2x2 area and adds it in a new image.
We can use this set of convolutional layer and pooling layers multiple times to make the image easier for the network to work with.
After we are done with those layers, we need to pass the output to a conventional (Dense) neural network.
To do that, we first need to flatten the image data from a 2D Tensor(Matrix) to a 1D Tensor(Vector). This is done by calling the Flatten() method.
Finally we need to add our Dense layers which are used to train on the flattened data. We do this by calling Dense(). An example of parameters is Dense(64, activation='relu')
where 64 is the number of nodes we are using.
Here is an example CNN structure I used recently:
# Build model
model = tf.keras.models.Sequential()
# Convolution and pooling layers
model.add(tf.keras.layers.Conv2D(64, 3, activation='relu', input_shape=(IMG_SIZE, IMG_SIZE, 1))) # Input layer
model.add(tf.keras.layers.MaxPooling2D())
model.add(tf.keras.layers.Conv2D(64, 3, activation='relu'))
model.add(tf.keras.layers.MaxPooling2D())
# Flattened layers
model.add(tf.keras.layers.Flatten())
# Dense layers
model.add(tf.keras.layers.Dense(64, activation='relu'))
model.add(tf.keras.layers.Dense(2, activation='softmax')) # Output layer
Of course this worked for a certain classification problem and the number of layers and method parameters differ depending on the problem.
The Youtube channel The Coding Train has a very helpful video explaining the Convolutional and Pooling layer.
As we know, a DNN is comprised of many layers which consist of many neurons applying the same function to different parts of the input. Meanwhile, if we use Tensorflow to execute a DNN task, we will get a dataflow graph generated by Tensorflow automatically and we can use Tensorboard to visualize the dataflow graph as blow. But there is no neuron in the layer. So I wonder what is the relationship between Tensorflow dataflow graph and a DNN? When a neuron of DNN's layer map into dataflow graph, how is it represented?What is the relationship of neuron in DNN and node in tensorflow(representing an operation)? I just started to learn DNN and Tensorflow, please help me arrange thoughts in order. Thanks:) enter image description here
You have to differentiate between the metaphoric representation of a DNN and it's mathematic description. The math behind a classic neuron is the sum of the weighted inputs + a bias (usually calling a activation function on this result)
So in this case you have an input vector mutplied by a weight vector (containing trainable variables) and then summed up with a bias scalar (also trainable)
If you now consider a layer of neurons instead of one, the weights will become a matrix and the bias a vector. So calculating a feed forward layer is nothing more then a matrix multiplication follow by a sum of vectors.
This is the operation you can see in your tensorflow graph.
You can actually build your Neural Network this way without any use of the so called High Level API which use the Layer abstraction. (Many have done this in the early days of tensorflow)
The actual "magic", which tensorflow does for you is calculating and executing the derivatives of this foreword pass in order to calculate the updates for the weights.
I'm not sure if my understanding is correct but...
While training a seq2seq model, one of the purpose I want to initiated a set of pre-trained fasttext weights in the embedding layers is to decrease the unknown words in the test environment (these unknown words are not in training set). Since pre-trained fasttext model has larger vocabulary, during test environment, the unknown word can be represented by fasttext out-of-vocabulary word vectors, which supposed to have similar direction of the semantic similar words in the training set.
However, due to the fact that the initial fasttext weights in the embedding layers will be updated through the training process (updating weights generates better results). I am wondering if the updated embedding weights would distort the relationship of semantic similarity between words and undermine the representation of fasttext out-of-vocabulary word vectors? (and, between those updated embedding weights and word vectors in the initial embedding layers but their corresponding ID didn't appear in the training data)
If the input ID can be distributed represented vectors extracted from pre-trained model and, then, map these pre-trained word vectors (fixed weights while training) via a lookup table to the embedding layers (these weights will be updated while training), would it be a better solution?
Any suggestions will be appreciated!
You are correct about the problem: when using pre-trained vector and fine-tuning them in your final model, the words that are infrequent or hasn't appear in your training set won't get any updates.
Now, usually one can test how much of the issue for your particular case this is. E.g. if you have a validation set, try fine-tuning and not fine-tuning the weights and see what's the difference in model performance on validation set.
If you see a big difference in performance on validation set when you are not fine-tuning, here is a few ways to handle this:
a) Add a linear transformation layer after not-trainable embeddings. Fine-tuning embeddings in many cases does affine transformations to the space, so one can capture this in a separate layer that can be applied at test time.
E.g. A is pre-trained embedding matrix:
embeds = tf.nn.embedding_lookup(A, tokens)
X = tf.get_variable("X", [embed_size, embed_size])
b = tf.get_vairable("b", [embed_size])
embeds = tf.mul(embeds, X) + b
b) Keep pre-trained embeddings in the not-trainable embedding matrix A. Add trainable embedding matrix B, that has a smaller vocab of popular words in your training set and embedding size. Lookup words both in A and B (and if word is out of vocab use ID=0 for example), concat results and use it input to your model. This way you will teach your model to use mostly A and sometimes rely on B for popular words in your training set.
fixed_embeds = tf.nn.embedding_lookup(A, tokens)
B = tf.get_variable("B", [smaller_vocab_size, embed_size])
oov_tokens = tf.where(tf.less(tokens, smaller_vocab_size), tokens, tf.zeros(tf.shape(tokens), dtype=tokens.dtype))
dyn_embeds = tf.nn.embedding_lookup(B, oov_tokens)
embeds = tf.concat([fixed_embeds, dyn_embeds], 1)
I have two neural network (TF Computation Graph) defined like following:
Model 1: some input&hidden layers + a 3-way softmax layer
Model 2: some input&hidden layers(same as Model 1) + a binary Sigmoid layer
The only difference between Model 1 and 2 is the output layer.
I have stored some trained parameters for Model 1 and would like to use them (the input & hidden layers) to initialize new Model 2. Ideally, for Model 2, the input & hidden layers should use the saved weights while the output layer will be randomly initialized. I am wondering if it is possible to implement this partial weights recovery in TensorFlow. What is the best practice to do this?
Many thanks!