layers import Dense, Dropout, Activation, LeakyReLU from keras import backend. a model architecture JSON consistent with the format of the return value of keras. How to create an** activation function with trainable parameters**, which can be trained using gradient descent, How to create an activation function with a custom backward step. If we keep it linear, we do not need to use deep learning since it's just a simple linear functions. Let us go through these activation functions, how they work and figure out which activation functions fits well into what kind of problem statement. You can vote up the examples you like or vote down the ones you don't like. You can now book me and my 1-day workshop on deep learning with Keras and TensorFlow using R. datasets import mnist from keras. You will also use another API – Keras, which is built on top of TensorFlow, to make deep learning more user-friendly and easier. You will also use another API - Keras, which is built on top of TensorFlow, to make deep learning more user-friendly and easier. Predict using the built in binary_crossentropy function from Keras (no funnel in cost function) Predict using a custom loss function to replicate binary_crossentropy (no funnel in cost function). learnable activations, which maintain a state) are available as Advanced Activation layers, and can be found in the module keras. However, understanding activation functions, dropout management, and loss functions will also deeply affect the performance of your machine-learning program. seed_input: The model input for which activation map needs to be visualized. In addition, custom loss functions/metrics can be defined as BrainScript expressions. exp((-2*x)))-1 tanhsigma=np. Keras has a variety of loss functions and out-of-the-box optimizers to choose from. incoming : A Tensor or list of Tensor. Setting up an image backprop problem is easy. default is (100,) which means one hidden layer of 100 neurons. Written by Matt Dancho on November 28, 2017. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key Features • Explore neural network architecture and understand how it functions • Learn algorithms to solve common problems using back propagation and perceptrons. is widely extendable and currently implemented using custom activation functions within the Keras wrapper to the popular TensorFlow machine learning framework. You can write a book review and share your experiences. let's assume the game of chess, every movement is based on 0 or 1. It's helpful to have the Keras documentation open beside you, in case you want to learn more about a function or module. Today, various tools exist for generating these visualizations - allowing engineers and researchers to generate them either by hand, or even (partially) automated. How to create an activation function with trainable parameters, which can be trained using gradient descent, How to create an activation function with a custom backward step. TensorFlow provides a single function tf. get_custom_activation (activation_str) [source] ¶ If activation_str describes a custom activation function, import this function from snntoolbox. It might be useful to note that the preferred way to access the custom object pool in keras is through custom_object_scope() – Mr Tsjolder Jul 22 '17 at 19:37. The previous article was focused primarily towards word embeddings, where we saw how the word embeddings can be used to convert. Amazon Machine Learning - Amazon ML is a cloud-based service for developers. Keras is a high-level interface for neural networks that runs on top of multiple backends. More importantly though, we forgot the activation functions per layer! We only had an activation function on the fully connected layer. Image Classification on Small Datasets with Keras. Hi, I have been trying to make a custom loss function in Keras for dice_error_coefficient. You basically have 2 options in Keras: 1. Dropout Layers. Keras quickly gained traction after its introduction and in 2017, the Keras API was integrated into core Tensorflow as tf. To fit the model, all we have to do is declare the batch size and number of epochs to train for, then pass in our training data. As for the activation function that you will use, it's best to use one of the most common ones here for the purpose of getting familiar with Keras and neural networks, which is the relu activation function. First, need to define a model building function that returns a compiled keras model. As I dug deeper and deeper into the material, I'd leave behind mountain of scratch paper where I'd jotted along. The mapping of Keras to DL4J activation functions is defined in KerasActivationUtils API Reference Detailed API docs for all libraries including DL4J, ND4J, DataVec, and Arbiter. It has its implementations in T ensorBoard and I tried using the same function in Keras with TensorFlow but it keeps returning a NoneType when used model. Keras is a high-level library in Python that is a wrapper over TensorFlow, CNTK and Theano. compile(loss=keras. Retrieves the elements of indices indices in the tensor reference. Keras has a set of callbacks to extract statistics and internal states during training and allows the user to build his own callbacks. By default, Keras uses a TensorFlow. A custom callback is a powerful tool to customize the behavior of a Keras model during training, evaluation, or inference, including reading/changing the Keras model. Convolutional Layer. This rapid hands-on course quickly shows you how to get to grips with TensorFlow in the context of real-world application development. Implementing Sequential neural newtork model using Keras : As mentioned earlier it has nicer and more interpret-able way of calling the functions to actually create your custom neural network. Activation functions. Keras is High-Level Deep learning Python library extensively used by Data-scientists when it comes to architect the neural networks for complex problems. Customer Analytics: Using Deep Learning With Keras To Predict Customer Churn Written by Matt Dancho on November 28, 2017 Customer churn is a problem that all companies need to monitor, especially those that depend on subscription-based revenue streams. Within the hidden-layers we use the relu function because this is always a good start and yields a satisfactory result most of the time. The last point I’ll make is that Keras is relatively new. This allows the layer to learn a convex, piecewise linear activation function over the inputs. Activation Functions in TensorFlow Posted by Alexis Alulema Perceptron is a simple algorithm which, given an input vector x of m values (x1, x2, …, xm), outputs either 1 (ON) or 0 (OFF), and we define its function as follows:. That means that you’re looking to build a fairly simple stack of fully-connected layers to solve this problem. When you want to do some tasks every time a training/epoch/batch, that's when you need to define your own callback. "linear" activation: a(x) = x). In the last article, we started our discussion about deep learning for natural language processing. 11/08/2016; 4 minutes to read +1; In this article. This is the 17th article in my series of articles on Python for NLP. batch_input_shape: Shapes, including the batch size. Define weighted loss function. Callbacks to track and. activation_relu: Activation functions in keras: R Interface to 'Keras'. For example, imagine we’re building a model for stock portfolio optimization. Enabling Wide and Easy-to-Implement Adoption Neuromorphic Hardware in Practice and Use Description of the workshop Abstract –This workshop is designed to explore the current advances, challenges and best practices for working with and implementing algorithms on. There are a number of popular pre-trained models (e. In this tutorial, I will show you how to build a model with the on-browser framework TensorFlow. Keras is a high-level interface for neural networks that runs on top of multiple backends. Built on Apache Spark, HBase and Spray. from __future__ import absolute_import from __future__ import print_function import numpy as np np. Predict using the built in binary_crossentropy function from Keras (no funnel in cost function) Predict using a custom loss function to replicate binary_crossentropy (no funnel in cost function). ) are available as Advanced Activation layers, and can be found in the module keras. Here is a good resource about it. Minimalworkingexample Speaking of importing functions from python modules,let’s load these four functions from keras: >>> from keras. Get to grips with the basics of Keras to implement fast and efficient deep-learning models Key Features • Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games • See how various deep-learning models and practical use-cases can be implemented using Keras. You can create a function that returns the output shape, probably after taking input_shape as an input. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices. It is a very simple concept. framework import ops from tensorflow. 1 With function. Activation functions differ, mostly in speed, but all the ones available in Keras and TensorFlow are viable; feel free to play around with them. Keras, the deep learning framework for Python that I prefer due to its flexibility and ease of use, supports the creation of custom activation functions. All code from this tutorial is available on GitHub. Let's look at a concrete example for our traffic flow predictions. In part 2, we will continue with multiple metric functions. relu is the most popular activation function in deep learning, but there are many other candidates, which all come with similarly strange names: prelu, elu, and so on. Convolutional Layer. If we keep it linear, we do not need to use deep learning since it's just a simple linear functions. layers import Dense, Dropout, Activation, LeakyReLU from keras import backend. default is (100,) which means one hidden layer of 100 neurons. layers import Dense, Dropout, Activation, Flatten, BatchNormalization from keras. Many common activation functions (e. This course will introduce you to the field of deep learning and teach you the fundamentals. Here we used (2, 2) which will. If this support. Although Keras is already used in production, but you should think twice before deploying keras models for productions. They are from open source Python projects. There are a number of popular pre-trained models (e. You can also try using AvgPooling to observe the results. Continue reading on Medium ». This is particularly useful if …. In fact, we can create custom activation functions, regularization layers, or metrics following a very similar pattern. U-Net variant for density estimation applied to phase contrast images input image ground-truth density predicted density 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 15 121 127 133 139 145 151 157 163 169 175 181 187 193 199. relu is the most popular activation function in deep learning, but there are many other candidates, which all come with similarly strange names: prelu, elu, and so on. The weights which should be loaded to the model using the Dlib’s landmarks detector, and finally the custom dataset of images that should be loaded to the model. You can also define custom metric functions. A custom loss function in Keras can improve a machine learning model’s performance in the ways we want and can be very useful for solving specific problems more efficiently. The function should therefore cap at somewhere near 26 above and below zero. By reading the Conv2D arguments, we learn how to define the size of the kernels, the stride, the padding and the activation function. An important argument to note is the data_format. The first step that we take in the constructor is to call the init() method, which goes on to allocate the layers and their components in memory, and to initialize their values. How to make a custom activation function in keras with a learnable parameter? What is the update rule for hidden layer if softmax activation function is used? 0. In the last article, we started our discussion about deep learning for natural language processing. He then looks at convolutional neural networks, explaining why they're particularly good at image recognition tasks. I read the KERAS documentation but could. OK, I Understand. generic_utils import get_custom_objects # definition of tanhsigma def tanhsigma(x): return 2/(1+np. Arguments used: Pool size: It is an integer or tuple used to downscale. Sigmoid function’s values are within the following range [0,1], and due to its nature, small and large values passed through the sigmoid function will become values close to zero and one respectively. So, it is less flexible when it comes to building custom operations. 0 - a Python package on PyPI - Libraries. Note that this is a linear layer; if you wish to apply activation function (you shouldn't need to --they are universal function approximators), an Activation layer must be added after. Mostly used non-linear activation functions for the hidden layers (intermediate layers) are ReLu and Tanh. excerpt: Before jumping into this lower level you might consider extending Keras before moving past it. This is the 17th article in my series of articles on Python for NLP. callbacks will be explained. These include smooth nonlinearities (sigmoid, tanh, elu, softplus, and softsign), continuous but not everywhere differentiable functions (relu, relu6, crelu and relu_x), and random regularization (dropout). activation, activation function, backpropagation, derivative, keras, mish, neural networks, python, softplus, tanh Using Custom Activation Functions in Keras Almost every day a new innovation is announced in ML field. These include PReLU and LeakyReLU. It is a very simple concept. Though you can use them like any other activation function, they work well for image-oriented learning. Here, the function returns the shape of the WHOLE BATCH. utils and return it. optimization. In this article, we'll walk through building a convolutional neural network (CNN) to classify images without relying on pre-trained models. datasets import mnist from keras. 6, we can use the Sequence object instead of a generator which allows for safe multiprocessing which means significant speedups and less risk of bottlenecking your GPU if you have one. I'm using keras and I wanted to add my own activation function myf to tensorflow backend. U-Net variant for density estimation applied to phase contrast images input image ground-truth density predicted density 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 15 121 127 133 139 145 151 157 163 169 175 181 187 193 199. In particular, we will learn how to implement a Custom Layer in Keras, and custom Activation functions, and custom optimisers. You can create a function that returns the output shape, probably after taking input_shape as an input. Keras is a Python library for deep learning that wraps the efficient numerical libraries TensorFlow and Theano. 1 With function. SparseCategoricalCrossentropy that combines a softmax activation with a loss function. You may think of an activation function as being the nonlinearity that gets applied to the output of a layer, but you can also consider activation functions to be layers of their own. layers import Convolution2D, MaxPooling2D from keras. However, understanding activation functions, dropout management, and loss functions will also deeply affect the performance of your machine-learning program. k_get_session(). pythonmodel. We need to introduce non linearity hence an activation function is needed. Easy extensibility. how to make a custom pyTorch LSTM with custom activation functions, how the PackedSequence object works and is built, how to convert an attention layer from Keras to pyTorch,. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions, regularization schemes are all standalone modules that you can combine to create new models. The only difference is in the number of parameters of the last layer due to more. Is it possible instead to give the last non-sequential LSTM a softmax activation? The answer is yes. Each topic includes lecture content along with hands-on labs in the Databricks notebook environment. A custom logger is optional because Keras can be configured to display a built-in set of information during training. Keras is a high-level neural networks API, written in Python that runs on top of the Deep Learning framework TensorFlow. The good news is that we can develop our own Custom layers. Then, you will get hands-on experience in solving problems using Deep Learning. That means that you're looking to build a fairly simple stack of fully-connected layers to solve this problem. The simplest type of model is the Sequential Fender 《フェンダー》CUSTOM SHOP 951 Telecaster HS Telecaster HS Relic/Aged Nocaster SHOP Blonde #R99409【あす楽対応】【oskpu】：Ikebe大阪プレミアム店HSレイアウトのテリーが待望の再入荷！. activation: Name of activation function to use. Each topic includes lecture content along with hands-on labs in the Databricks notebook environment. The mse loss function, it computes the square of the difference between the predictions and the targets, a widely used loss function for regression tasks. Declaring the input shape is only required of the first layer – Keras is good enough to work out the size of the tensors flowing through the. Activations that are more complex than a simple TensorFlow/Theano/CNTK function (eg. You would typically use the "relu" activation function for all layers but the last. This is an example of a Tensorflow Keras implementation of a Participant for federated learning. Dense layer, filter_idx is interpreted as the output index. First, define the activation function; we chose the GELU activation function gelu(). You can also try using AvgPooling to observe the results. For instance, the parametric rectified linear unit or PReLu function was invented to surpass human-level performance on ImageNet classification. Mostly used non-linear activation functions for the hidden layers (intermediate layers) are ReLu and Tanh. But you can use activation functions to add non-linearities to the model and this allows you to model arbitrary. seed_input: The model input for which activation map needs to be visualized. Convolutional Layer. 0] I decided to look into Keras callbacks. In this workshop, you will learn how to get started with deep learning using one of the most popular frameworks for implementing deep learning - TensorFlow. The last layer, in a classifier, would use "softmax" activation. The code is hosted on GitHub, and community support forums include the GitHub issues page, and a Slack channel. “kwargs” specifies keyword arguments to the function, except arguments named “t” or “t_list”. There are multiple activation functions, like: “Sigmoid”, “Tanh”, ReLu and many other. They are from open source Python projects. json) file given by the file name modelfile. hidden_layers = a tuple defining the number of neurons per hidden layer. How to make a custom activation function? I know this has been covered before but i guess my main question is where is the source code for something like relu in 2. If you don't specify anything, no activation is applied (ie. input_shape: Input shape (list of integers, does not include the samples axis) which is required when using this layer as the first layer in a model. Activations functions can either be used through layer_activation(), or through the activation argument supported by all forward layers. INTRO IN KERAS. Jeff Heaton 989 views. maxpooling2d keras does not do pooling at all. models import Sequential from tensorflow. To begin, install the keras R package from CRAN as. To fit the model, all we have to do is declare the batch size and number of epochs to train for, then pass in our training data. Loss functions and metrics. We can create a custom loss function in Keras by writing a function that returns a scalar and takes two arguments: namely, the true value and predicted value. As we can see now, our current loss function MAE will not give us information about direction of change! We will try to fix it right now. Although Keras is already used in production, but you should think twice before deploying keras models for productions. The function returns the layers defined in the HDF5 (. Keras custom layer using tensorflow function Hot Network Questions Find maximum extent out of list of shapefiles (in projected coordinate system) in R. Keras uses one of the predefined computation engines to perform computations on tensors. Name of the output activation function. You can vote up the examples you like or vote down the ones you don't like. layers import Dense, Activation,Conv2D,MaxPooling2D,Flatten,Dropout model = Sequential() 2. Af-ter training, the weights are directly exportable. shallow, intermedia, deep (4-layers) CNN (8-layers), ResNet, etc). js weights manifest. There are many popular activation functions among them relu and sigmoid are in front. Using Custom Activation Functions in Keras. TensorBoard where the training progress and results can be exported and visualized with. The network ends with a Dense without any activation because applying any activation function like sigmoid will constrain the value to 0~1 and we don't want that to happen. [Michael Bernico] -- This book is a practical guide to applying deep neural networks including MLPs, CNNs, LSTMs, and more in Keras and TensorFlow. Let say you want to add your own activation function (which is not built-in Keras) to a layer. Because TF argmax have not gradient, we cannot use it in keras custom loss function. Step 9: Fit model on training data. It's used to define the order of the data flow in Keras. At the end of my comparison — TensorFlow 1. The following are code examples for showing how to use keras. In other words, a class activation map (CAM) lets us see which regions in the image were relevant to this class. As keras supports all theano operators as activations, I figured it would be the easiest to implement my own theano operator. You will also use another API – Keras, which is built on top of TensorFlow, to make deep learning more user-friendly and easier. To create a custom Keras model, you call the keras_model_custom() function, passing it an R function which in turn returns another R function that implements the custom call() (forward pass) operation. Loss Function and Optimizer. Inspired by Swish Activation Function (Paper), Mish is a Self Regularized Non-Monotonic Neural Activation Function. Because the model aims to produce a positive continuous value for the option price, we cannot use the standard squashing functions that are used in TensorFlow, such as the sigmoid function. Table of Contents. This notebook will be a documentation of the model I made, using TensorFlow and Keras, with some insight into the custom activation function I decided to use in some of the layers called 'Swish'. datasets import cifar10 from keras. “kwargs” specifies keyword arguments to the function, except arguments named “t” or “t_list”. Many activation functions are nonlinear, or a combination of linear and nonlinear - and it is possible for some of them to be linear, although that is unusual. Such an extent that number of research papers published about machine learning is growing faster than Moore’s law. The core data structure of Keras is a model, a way to organize layers. Custom Layers and Optimisers This notebook will provide details and examples of Keras internals. Understand and apply dropout regularization to deep learning models in Python. The DSSIM loss is limited between 0 and 0. An arbitrary number of covariates and response variables as well as of hidden lay-ers can theoretically be included. 01, momentum=0. 9, nesterov=True)). layers import Dense, Activation Here, we are bringing two important things to the table: dense( ) will allow us to summon layers with a chosen number of neurons, and activation( ) is for choosing a function that is applied to a layer of neurons. The activation function was replaced with a custom designed nonlinear transfer function resulting from our ab initio ITO based electroabsorption modulator model. compile(loss=keras. Dropout of 40% is used on all layers except the final output to prevent overfitting. Consider an example of a simple this linear equation : Y = a. To make this work in keras we need to compile the model. models import Sequential from keras. For example, you cannot use Swish based activation functions in Keras today. Subscribe to this blog. layers import Dense, Activation Here, we are bringing two important things to the table: dense( ) will allow us to summon layers with a chosen number of neurons, and activation( ) is for choosing a function that is applied to a layer of neurons. Minimalworkingexample Speaking of importing functions from python modules,let’s load these four functions from keras: >>> from keras. Keras, the deep learning framework for Python that I prefer due to its flexibility and ease of use, supports the creation of custom activation functions. In this case, it will be helpful to design a custom loss function that implements a large penalty for predicting price movements in the wrong direction. g sigmoid or tanh) squash their input into a very small output range in a very non-linear fashion. It can also have a regularization term added to the loss function that shrinks model parameters to prevent overfitting. exp((-2*x)))-1 tanhsigma=np. Upload your model artifacts and your custom code to Cloud Storage; Deploy your custom prediction routine to AI Platform; Create a custom predictor. callbacks will be explained. The following are code examples for showing how to use keras. You might need to specify the output shape of your Lambda layer, especially your Keras is on Theano. custom_layer (incoming, custom_fn, **kwargs) A custom layer that can apply any operations to the incoming Tensor or list of Tensor. The last point I'll make is that Keras is relatively new. TensorFlow Lite for mobile and embedded devices For Production TensorFlow Extended for end-to-end ML components. There are many popular activation functions among them relu and sigmoid are in front. ReLU(max_value=None, negative_slope=0. We can do this by writing a custom function and wrapping it in a Keras Lambda layer. Again, a "neuron" computes a weighted sum of all of its inputs, adds a value called "bias" and feeds the result through the activation function. Let’s look at a concrete example for our traffic flow predictions. There are many other activation functions but Relu is one of the most popular in this kind of networks. Is there any way like adding gradient or equivalent function? I want to have my loss in keras. get_custom_activation (activation_str) [source] ¶ If activation_str describes a custom activation function, import this function from snntoolbox. apply_modifications for better results. In fact, tf. 0 - a Python package on PyPI - Libraries. to_json() a full model JSON in the format of keras. Brief overview of neural networks; What is an activation function ? Can we do without an activation function ? Popular types of activation functions and when to use them Identity. For instance, the parametric rectified linear unit or PReLu function was invented to surpass human-level performance on ImageNet classification. The function contains four arguments (samples, channels, height, width) , where channels is 0 or 3 , which means, gray-scale or RGB mode, respectively. optimization. GradientTape() as gt. fit whereas it gives proper values when used in metrics in the model. We use the binary_crossentropy loss and not the usual in multi-class classification used categorical_crossentropy loss. All the control logic for the demo program is contained in a single main() function. h5) or JSON (. Custom Layers and Optimisers This notebook will provide details and examples of Keras internals. callbacks import LearningRateScheduler import numpy as np. In this article, we'll walk through building a convolutional neural network (CNN) to classify images without relying on pre-trained models. However, with the release of the much anticipated TensorFlow 2. The activation parameter here specifies the function we want to perform on top of the layer to calculate the output = activation(X * W + bias). This includes such things as predicting customer behavior, evaluation of content, fraud detection and feeding usage and data back into algorithms to automatically improve them. The mse loss function, it computes the square of the difference between the predictions and the targets, a widely used loss function for regression tasks. learnable activations, configurable activations, etc. Treating any overfitting or regularization problem and other model tuning are discussed in different sections. The Primary LSTM Gates Input, Forget, and Output gates (FIO) The main operational block of LSTMs an "information filter" a multivariate input gate some inputs are blocked some inputs "go through" "remember" necessary information FIO gates use the sigmoid activation function The cell state uses the tanh activation function. Github repo for gradient based class activation maps. An important argument to note is the data_format. For instance, the parametric rectified linear unit or PReLu function was invented to surpass human-level performance on ImageNet classification. This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. Github repo for gradient based class activation maps. Keras Tutorial: The Ultimate Beginner's Guide to Deep Posted: (4 days ago) We tried to make this tutorial as streamlined as possible, which means we won't go into too much detail for any one topic. default is (100,) which means one hidden layer of 100 neurons. learnable activations, configurable activations, etc. 14 Keras’ API versus Julia’s Flux. Loading a model with custom activation function (or custom_objects) in Keras 1. As I dug deeper and deeper into the material, I'd leave behind mountain of scratch paper where I'd jotted along. Long Short-Term Memory Network (LSTM), one or two hidden LSTM layers, dropout, the output layer is a Dense layer using the softmax activation function, DAM optimization algorithm is used for speed: Keras: Text Generation. Incoming tensor. "linear" activation: a(x) = x). 0] I decided to look into Keras callbacks. Work with. Today, there is a great need for the introduction of AI into all aspects of software, making the enterprise software smart. Starting on page 144 you can see the different settings for possible Activation Functions, and more options. See the Python converter function save_model() for more details. models import Sequential from keras. Interface to 'Keras' , a high-level neural networks 'API'. Keras Tutorial Contents. The function accepts the input tensor as its argument and returns the output tensor after applying the required operations. The importKerasLayers function displays a warning and replaces the unsupported layers with placeholder layers. How to make a custom activation function? I know this has been covered before but i guess my main question is where is the source code for something like relu in 2. Activation functions. Github repo for gradient based class activation maps. It provides visualization tools to create machine learning models. Keras has a set of callbacks to extract statistics and internal states during training and allows the user to build his own callbacks. Instead we will define a custom layer in our network that applies the correct activation to each parameter. To create a custom Keras model, you call the keras_model_custom() function, passing it an R function which in turn returns another R function that implements the custom call() (forward pass) operation. Today, various tools exist for generating these visualizations - allowing engineers and researchers to generate them either by hand, or even (partially) automated. Loading a model with custom activation function (or custom_objects) in Keras 1. In this case, it will be helpful to design a custom loss function that implements a large penalty for predicting price movements in the wrong direction. pdf from SAP ARCHIV S/N at Adrian College. Specifically, it allows you to define multiple input or output models as well as models that share layers.