I am currently in the process of learning Keras, a Deep Learning module.
I came across a sentence that I can't understand because I don't know what a python Wrapper is.
from keras.models import Sequential
#Create the Sequential model
model = Sequential()
"The keras.models.Sequential class is a wrapper for the neural network model that treats the network as a sequence of layers. It implements the Keras model interface with common methods like compile(), fit(), and evaluate() that are used to train and run the model."
In the above sentence, what is a Python wrapper and what does it do?
This is not unique to Python. In this case, Sequential is a helper class that provides methods to make the creation of this specific type of neural network model easier. It inherits many properties from its parent class, which is a generic Model. See the source for more info.
Related
In some tf. keras tutorials, I've seen them instantiated their model class like this:
model = tf.keras.Sequential()
While in some places, they use something like this:
model = tf.keras.Model(inputs=input, outputs=output)
But seeing here in the docs, they do seem the same, but I am not sure nor is it explicitly mentioned. What are the differences between the two?
There are two class API to define a model in tf. keras. According to the doc
Sequential class: Sequential groups a linear stack of layers into a tf. keras.Model.
Model class: Model group's layers into an object with training and inference features.
An Sequential model is the simplest type of model, a linear stack of layers. But there are some flaws in using the sequential model API, it's limited in certain points. We can't build complex networks such as multi-input or multi-output networks using this API.
But using Model class, we can instantiate a Model with the Functional API (and also with Subclassing the Model class) that allows us to create arbitrary graphs of layers. From this, we can get more flexibility and easily define models where each layer can connect not just with the previous and next layers but also share feature information with other layers in the model, for example, model-like ResNet, EfficientNet.
In fact, most of the SOTA model that you can get from tf.keras.applications is basically implemented using the Functional API. However, in subclassing API, we define our layers in __init__ and we implement the model's forward pass in the call method.
Generally speaking, all the model definitions using Sequential API, can be achieved in Functional API or Model Subclassing API. And in Functional API or Model Subclassing API, we can create complex layers that not possible to achieve in Sequential API. If you wondering which one to choose, the answer is, it totally depends on your need. However, check out the following blog post where we have discussed the various model strategies in tf. keras with more examples. Model Sub-Classing and Custom Training Loop from Scratch in TensorFlow 2
It's because they are from different versions of tensorflow. According to the documentation (TensorFlow 2.0), tf.keras.Sequential is the most recent way of calling the function. If you go to the documentation, and click on "View aliases", you can see the different aliases used in older version of Tensorflow for that function.
Tensorflow has some docs for subclassing (tf) Keras Model and Layer.
However, it is unclear which to use for "modules" or "blocks" (e.g. several layers collectively).
Since it is technically several layers, I feel that subclassing Layer would be deceiving, and while subclassing Model works, I am unsure if there are any negative penalties for doing so.
e.g.
x = inputs
a = self.dense_1(x) # <--- self.dense_1 = tf.keras.Dense(...)
b = self.dense_2(a)
c = self.add([x, b])
which is appropriate to use?
(Please note that this answer is old, later, Keras changed to allow and use subclassing regularly)
Initially, there is no need to sublass anything with Keras. Unless you have a particular reason for that (which is not building, training, predicting), you don't subclass for Keras.
Buiding a Keras model:
Either using Sequential (the model is ready already, just add layers), or using Model (create a graph with layers and finally call Model(inputs, outputs)), you don't need to subclass.
The moment you create an instance of Sequential or Model, you have a fully defined model, ready to use in all situations.
This model can even be used as parts of other models, its layers can be easily accessed to get intermetiate outputs and create new branches in your graph.
So, I don't see any reason at all to subclass Model, unless you are using some additional framework that would require this (but I don't think so). This seems to be something from PyTorch users (because this kind of model building is typical for PyTorch, create a subclass for Module and add layers and a call method). But Pytorch doesn't offer the same ease as Keras does for getting intermediate results.
The main advantage of using Keras is exactly this: you can easily access layers from blocks and models and instantly start branching from that point without needing to rebuild any call methods or adding any extra code for that in the models. So, when you subclass Model, you just defeat the purpose of Keras making it all more difficult.
The docs you mentioned say:
Model subclassing is particularly useful when eager execution is enabled since the forward pass can be written imperatively.
I don't really understand what "imperatively" means, and I don't see how it would be easier than just building a model with regular layers.
Another quote from the docs:
Key Point: Use the right API for the job. While model subclassing offers flexibility, it comes at a cost of greater complexity and more opportunities for user errors. If possible, prefer the functional API.
Well... it's always possible.
Subclassing layers
Here, there may be good reasons to do so. And these reasons are:
You want a layer that performs custom calculations that are not available with regular layers
This layer must have persistent weights.
If you don't need "both" things above, you don't need to subclass a layer. If you just want "custom calculations" without weights, a Lambda layer is enough.
Reading through the documentation of implementing custom layers with tf.keras, they specify two options to inherit from, tf.keras.Layer and tf.keras.Model.
Under the context of creating custom layers, I'm asking myself what is the difference between these two? Technically what is different?
If I were to implement the transformer encoder for example, which one would be more suitable? (assuming the transformer is a only a "layer" in my full model)
In the documentation:
The Model class has the same API as Layer, with the following
differences: - It exposes built-in training, evaluation, and
prediction loops (model.fit(), model.evaluate(), model.predict()). -
It exposes the list of its inner layers, via the model.layers
property. - It exposes saving and serialization APIs.
Effectively, the "Layer" class corresponds to what we refer to in the
literature as a "layer" (as in "convolution layer" or "recurrent
layer") or as a "block" (as in "ResNet block" or "Inception block").
Meanwhile, the "Model" class corresponds to what is referred to in the
literature as a "model" (as in "deep learning model") or as a
"network" (as in "deep neural network").
So if you want to be able to call .fit(), .evaluate(), or .predict() on those blocks or you want to be able to save and load those blocks separately or something you should use the Model class. The Layer class is leaner so you won't bloat the layers with unnecessary functionality...but I would guess that that generally wouldn't be a big problem.
A layer takes in a tensor and give out a tensor which is a result of
some tensor operations
A model is a composition of multiple layers.
If you are building a new model architecture using existing keras/tf layers then build a custom model.
If you are implementing your own custom tensor operations with in a layer, then build a custom layer. If you are using non tensor operation inside your custom layer, then you have to code how the layer will forward propagate and backward propagate.
I am trying to generate class scores by calling predict_proba() of Keras model, but it seems that this function does not exist! Is it deprecated because I see some examples in Google? I am using Keras 2.2.2.
The predict_proba() and predict_classes() methods are not well-defined for models created using functional API (i.e. keras.models.Model()). That's because the models created using functional API may have multiple output layers each with different configurations. Therefore predicting probabilities in this case is not meaningful, even if your model outputs probabilities. The method you referred to, as well as predict_classes(), is only defined for Sequential models (i.e. keras.models.Sequential()).
I am trying to create CNN using theano and tensorflow. Defined layers are MLP, convolvemaxpool in different classes now I am trying to create CNN using these classes depending on the input provided by user. CNN should call theano or tensorflow classes depending on the user input. Now, Let's say theano and tensorflow both has classes MLP, convolvemaxpool implementing MLP, Convolution simultaneously. Now my problem is how to call the classes depending on the input? I don't want to use if else since adding more libraries means more if else statement for each class which I don't think is a right solution for now. Any help will be appreciated. Thanks.