Keras Convert To Tensor

They are extracted from open source Python projects. I'm not sure what your get_train_gen() function is doing, but it should be returning an ImageDataGenerator object. Keras-Tuner. If you're using Keras, you can skip ahead to the section Converting Keras Models to TensorFlow. Being able to go from idea to result with the least possible delay is key to doing good research. The behavior of tf. The only change that I made to the VGG16 existing architecture is changing the softmax layer with 1000 outputs to 16 categories suitable for our problem and re-training the. We’ll build a custom model and use Keras to do it. Keras' foundational principles are modularity and user-friendliness, meaning that while Keras is quite powerful, it is easy to use and scale. This post is focused on converting our model to an Estimator — if we wanted to improve accuracy we could try tuning our model’s hyperparameters, changing our vocabulary size, or adding dropout to our input layer. Contribute to Open Source. layers import Input input_img = Input(shape = (32, 32, 3)) Now, we feed the input tensor to each of the 1x1, 3x3, 5x5 filters in the inception module. 5, 3, 15, 20]). matmul resulting tensors. tensorflow freeze graph (6). Refer to numpy. Printing a layer. It does not handle low-level operations such as tensor products, convolutions and so on itself. Keras follows at #2 with Theano all the way at #9. Convert Keras model to TensorFlow Lite with optional quantization. convert_to_tensor, and we pass in our Python list, and the result of this operation will be assigned to the Python variable tensor_from_list. I can't find anywhere in the documentation a succinct explaination of how to convert a numpy array into a keras tensor via the backend API. Tensor([ 1 2 6 24 120], shape=(5,), dtype=int32) So that’s eager execution. Our goal is to create a network that will be able to determine which of these reviews are positive and which are negative. 23 11:49:19 字数 340 阅读 1646 前言:移动端的模型迁移最基本的就是生成tflite文件,以本文记录一次转换过程。. Reading and transforming data are TensorFlow graph operations, so are executed in C++ and in parallel with model training. TensorFlow provides multiple APIs. Custom models. Deep Learning for Text Classification with Keras Two-class classification, or binary classification, may be the most widely applied kind of machine-learning problem. Note that the same result can also be achieved via a Lambda layer. Every Sequence must implement the __getitem__ and the __len__ methods. 1 Developer Guide demonstrates how to use the C++ and Python APIs for implementing the most common deep learning layers. to_categorical. You can create a Sequential model by passing a list of layer instances to the constructor: from keras. So in Python land, now we have a big change: With TF 2, Keras (as incorporated in the TensorFlow codebase) is now the official high-level API for TensorFlow. Tensorflow/Keras, How to convert tf. Automatically convert 64-bit R floats to backend default float type. Hundreds of people have contributed to the Keras codebase. convert keras h5 model to tflite 2018. Deep Learning for Text Classification with Keras Two-class classification, or binary classification, may be the most widely applied kind of machine-learning problem. scale¶ sklearn. As such, Keras does not handle itself low-level tensor operations, such as tensor products and convolutions. you can use keras backend to save the model as follows: [code]from keras. This post is focused on converting our model to an Estimator — if we wanted to improve accuracy we could try tuning our model's hyperparameters, changing our vocabulary size, or adding dropout to our input layer. I am also using keras, and actually wanted to use the output tensor from my model for Hamming loss evaluation (for which I intend to use sklearn metrics - hence the need to convert to the numpy array). tensor_list (a list or tuple of Tensors that all have the same shape in the axes not specified by the axis argument. PyTorch Tutorial: PyTorch NumPy to tensor - Convert a NumPy Array into a PyTorch Tensor so that it retains the specific data type PyTorch NumPy to tensor - Convert a NumPy Array into a PyTorch Tensor so that it retains the specific data type. concat on both pairs of tensors and tf. The first thing we need to do is transfer the parameters of our PyTorch model into its equivalent in Keras. If you want to modify your dataset between epochs you may implement on_epoch_end. Asserts and boolean checks BayesFlow Entropy BayesFlow Monte Carlo BayesFlow Stochastic Graph BayesFlow Stochastic Tensors BayesFlow Variational Inference Building Graphs Constants, Sequences, and Random Values Control Flow Copying Graph Elements CRF Data IO FFmpeg Framework Graph Editor Higher Order Functions Histograms Images Inputs and. Great, we have. The HDF5-format Keras model file must include both the model architecture and the weights. Keras provides a set of functions called callbacks: you can think of callbacks as events that will be triggered at certain training states. In raw keras it should be done replacing import tensorflow. If this is unspecified then R doubles will be converted to the default floating point type for the current Keras backend. This is from a Custom Keras Callback casted=K. Everything fine. keras and Tensorflow 2. File "/snap/pycharm-community/128/helpers/pydev/pydevd. NewCheckpointReader then call set_weights() method for each corresponding layer. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems [Aurélien Géron] on Amazon. It provides clear and actionable feedback for user errors. Must have strides[0] = strides[3] = 1. dim – a dimension along which the tensors will be concatenated. The following are code examples for showing how to use keras. model: Keras model object | str | (str, str) A trained Keras neural network model which can be one of the following: a Keras model object; a string with the path to a Keras model file (h5) a tuple of strings, where the first is the path to a Keras model; architecture (. The Jaccard index, also known as Intersection over Union and the Jaccard similarity coefficient, is a statistic used for gauging the similarity and diversity of sample sets. Refer to numpy. This function is part of a set of Keras backend functions that enable lower level access to the core operations of the backend tensor engine (e. backend as K import tensorflow as tf import uff. This seems like a fairly big oversight since the backend docs only discuss methods (very briefly at that), and there is little explanation given to how the system functions. When training with Keras's Model. What is the correct method to specify input shapes of a n_dimensional tensor of features in Keras Sequential models? ## ---- INTRO ---- I'm new to Team Treehouse and I primarily created an account here because I received really positive feedback about the community, forums and support. metrics import accuracy_score import theano import theano. Sequence() Base object for fitting to a sequence of data, such as a dataset. keras / examples / mnist mlp. In this article I’ll show how this can be accomplished with next to no knowledge of Tensorflow on the Windows operating system. You can also use a plain HTMLImageElement or even a video. First, need to define a model building function that returns a compiled keras model. There are tools and concepts in computing that are very powerful but potentially confusing even to advanced users. The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. To call a function repeatedly on a numpy array we first need to convert the function using vectorize. If both arguments are 2-D they are multiplied like conventional matrices. The Jaccard coefficient measures similarity between finite sample sets, and is defined as the size of the intersection divided by the size of the union of the sample sets: J = | A ∩ B | | A ∪ B | = | A ∩ B | | A | + | B | − | A ∩ B |. Export the pruned model by striping pruning wrappers from the model. One such concept is data streaming (aka lazy evaluation), which can be realized neatly and natively in Python. I would highly recommend checking also the other toco/tflite_convert_tool params. you can use keras backend to save the model as follows: [code]from keras. This example demonstrates how to write custom layers for Keras. As such, Keras does not handle itself low-level tensor operations, such as tensor products and convolutions. concat on both pairs of tensors and tf. Requirements. js Documentation. We will use NumPy to create an array like this: import numpy as np arr = np. They were collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. Basically, the sequential methodology allows you to easily stack layers into your network without worrying too much about all the tensors (and their shapes) flowing through the model. After another set of similar convolution and max-pooling layers, the input has now been transformed into a tensor with shape (batch size, 7, 7, 64). It provides clear and actionable feedback for user errors. h5 file into a Tensorflow. It helps to understand one of the most important technology that is edge computing which enables to run the model on the devices instead of running from the server. pb" extension only. As with any neural network, we need to convert our data into a numeric format; in Keras and TensorFlow we work with tensors. It is a very simple concept. Printing a layer. I just found a great video for converting. A tensor located on destination device, that is a result of concatenating tensors along dim. I can't find anywhere in the documentation a succinct explaination of how to convert a numpy array into a keras tensor via the backend API. JPEG is a standard for compressing pictures; it is defined in ISO 10918. pb file; Load. To bring this across has been a major point of Google's TF 2 information campaign since the early stages. Documentation¶. If you want to convert only weights (suppose you have code for the same model), you have to create model with random weights (you can find InceptionV3 in keras. Next, the image is converted to an array, which is then resized to a 4D tensor. This post is focused on converting our model to an Estimator — if we wanted to improve accuracy we could try tuning our model’s hyperparameters, changing our vocabulary size, or adding dropout to our input layer. Being able to go from idea to result with the least possible delay is key to doing good research. But the inference time is around 2 seconds per frame, and the RAM usage ramps up until 3. dtype: NumPy data type (e. They are extracted from open source Python projects. TensorFlow 2. The first two parts of the tutorial walk through training a model on AI Platform using prewritten Keras code, deploying the trained model to AI Platform, and serving online predictions from the deployed model. share | improve this answer edited Oct 9 at 21:34. convert() End-to-end MobileNet conversion The following example shows how to convert and run inference on a pre-trained tf. NewCheckpointReader then call set_weights() method for each corresponding layer. Originally developed at the National Center for Supercomputing Applications, it is supported by The HDF Group, a non-profit corporation whose mission is to ensure continued development of HDF5 technologies and the continued accessibility of data stored in HDF. Conclusion and Further reading. Keras’ Sequential() is a simple type of neural net that consists of a “stack” of layers executed in order. convert_to_tensor(initial_python_list) So tf. converter import Converter from webdnn. destination (int, optional) – output device (-1 means CPU, default: current device) Returns. We'll build a custom model and use Keras to do it. Tensor([ 1 2 6 24 120], shape=(5,), dtype=int32) So that’s eager execution. For example, importKerasNetwork(modelfile,'WeightFile',weights) imports the network from the model file modelfile and weights from the weight file weights. Keras provides a set of functions called callbacks: you can think of callbacks as events that will be triggered at certain training states. Importing a Keras model into TensorFlow. We need to specify two methods: compute_output_shape and call. The following are code examples for showing how to use keras. models import Sequential from keras. The Sequential model is a linear stack of layers. We build a custom activation layer called 'Antirectifier', which modifies the shape of the tensor that passes through it. serialize_model() and unserialize_model() functions for saving Keras models as 'raw' R objects. His project provides a script for converting the Inception ResNet v1 model from TensorFlow to Keras. Sequence() Base object for fitting to a sequence of data, such as a dataset. The behavior depends on the arguments in the following way. Being able to go from idea to result with the least possible delay is key to doing good research. '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. The power of Keras is that it abstracts a lot of things we had to take care while we were using TensorFlow. Refer to numpy. What is the correct method to specify input shapes of a n_dimensional tensor of features in Keras Sequential models? ## ---- INTRO ---- I'm new to Team Treehouse and I primarily created an account here because I received really positive feedback about the community, forums and support. TensorFlow Tutorial For Beginners Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. js layers format. How do I get around with this problem?. We’ll build a custom model and use Keras to do it. keras models, and concrete functions. To call a function repeatedly on a numpy array we first need to convert the function using vectorize. This is required because a layer may sometimes have more than one input/output tensors. This function converts Python objects of various types to Tensor objects. Keras does not include by itself any means to export a TensorFlow graph as a protocol buffers file, but you can do it using regular TensorFlow utilities. applications) then read the TensorFlow. Note: This page contains documentation on the converter API for TensorFlow 2. layers import Dense. With on-device training and a gallery of curated models, there’s never been a better time to take advantage of machine learning. js layers format (which we already did in lines 36-38). I have Keras layers. It then proceeded to grow from one user to one hundred thousand. keras is TensorFlow's implementation of this API. ckpt file with tf. The networks accept a 4-dimensional Tensor as an input of the form ( batchsize, height, width, channels). This is how you import Keras now, from TensorFlow:. GoogLeNet in Keras. 5 I typed: conda create -n tf-keras python=3. As a rule, the attributes in this module should not be modified by user code. A notable example is Keras FaceNet by Hiroki Taniai. create a keras model that matches the tf model; figure out the exact name string of each tf model layer and its corresponding keras layer; extract the numpy array from each tf layer, and load it into each keras layer; save the keras model; That's the way to do the conversion. Then, you can give in something like the following command to convert your notebooks: jupyter nbconvert --to html Untitled4. Keras is also integrated into TensorFlow from version 1. Converting multiple # functions is under development. tensors (Iterable) – iterable of tensors to gather. For the first step we are going to want to convert the Keras. Run your Keras models in C++ Tensorflow So you've built an awesome machine learning model in Keras and now you want to run it natively thru Tensorflow. If this dataset disappears, someone let me know. I am trying to build a custom loss function in keras. a Inception V1). Converting a torch Tensor to a numpy array and vice versa is a breeze. I would highly recommend checking also the other toco/tflite_convert_tool params. In this post, we take a look at what deep convolutional neural networks (convnets) really learn, and how they understand the images we feed them. js Layers format, and then load it into TensorFlow. We will us our cats vs dogs neural network that we've been perfecting. Unfortunately they do not support the &-operator, so that you have to build a workaround: We generate matrices of the dimension batch_size x 3, where (e. Keras uses the PIL format for loading images. 12 applications to TensorFlow 2. 5, 3, 15, 20]). Introducing Keras 2. As default, keras handle the batch size as place holder (undetermined) value. They are mostly used with sequential data. Let's see how. The next step is to make the code run with multiple GPUs. We will learn about client-server deep learning architectures, converting Keras models to TFJS models, serving models with Node. Our goal is to create a network that will be able to determine which of these reviews are positive and which are negative. Obtain a reference to the keras. Within the function I would like to create a matrix whose size is determined by the value of a scalar tensor variable, but of course input (to either numpy or tensorflow zeros) must be integers. keras is TensorFlow's implementation of this API. In particular, the submodule scipy. This post is focused on converting our model to an Estimator — if we wanted to improve accuracy we could try tuning our model’s hyperparameters, changing our vocabulary size, or adding dropout to our input layer. Ok, let us create an example network in keras first which we will try to port into Pytorch. import traceback from collections import defaultdict from typing import List, Optional import numpy as np from webdnn. TensorBoard callback ensures that logs are created and stored. When compared to TensorFlow, Keras API might look less daunting and easier to work with, especially when you are doing quick experiments and build a model with standard layers. Thus, the image is in width x height x channels format. It is a very simple concept. I would highly recommend checking also the other toco/tflite_convert_tool params. You can also use a plain HTMLImageElement or even a video. Before deploying a keras model in web, we need to convert the Keras mobilenet python model into tf. k_dtype() Returns the dtype of a Keras tensor or variable, as a string. You can vote up the examples you like or vote down the ones you don't like. The latest Keras functional API allows us to define complex models. I'm tyring to mix TensorFlow tensor and Keras tensor using this blog's info: But the problems occurs at the last layer when output needs to be Keras tensor not TensorFlow tensor. Keras to single TensorFlow. extracting weights:. Given that deep learning models can take hours, days and even weeks to train, it is important to know how to save and load them from disk. GoogLeNet paper: Going deeper with convolutions. Tensors are a type of data structure used in linear algebra, and like vectors and matrices, you. I showed the code below. With powerful numerical platforms Tensorflow and Theano, Deep Learning has been predominantly a Python. 5, 3, 15, 20]). Many TensorFlow function parameters require integers (e. TensorFlow 2. These two engines are not easy to implement directly, so most practitioners use Keras. Keras layers and models are fully compatible with pure-TensorFlow tensors, and as a result, Keras makes a great model definition add-on for TensorFlow, and can even be used alongside other TensorFlow libraries. The config module contains many attributes that modify Theano’s behavior. Keras is a high-level API for building and training deep learning models. Last year we released the first free to use public demo based on the groundbreaking neural style transfer paper—just days after the first one was published!. If this dataset disappears, someone let me know. This post is focused on converting our model to an Estimator — if we wanted to improve accuracy we could try tuning our model's hyperparameters, changing our vocabulary size, or adding dropout to our input layer. You will use the Keras deep learning library to train your first neural network on a custom image dataset, and from there, you'll implement your first Convolutional Neural Network (CNN) as well. The first two parts of the tutorial walk through training a model on AI Platform using prewritten Keras code, deploying the trained model to AI Platform, and serving online predictions from the deployed model. #machinelearning #tensorflow #keras #python. js is a two-step process. TensorFlow Tutorial For Beginners Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. The IMDB example data from the keras package has been preprocessed to a list of integers, where every integer corresponds to a word arranged by descending word frequency. Keras backends What is a "backend"? Keras is a model-level library, providing high-level building blocks for developing deep learning models. So in Python land, now we have a big change: With TF 2, Keras (as incorporated in the TensorFlow codebase) is now the official high-level API for TensorFlow. Convolutional neural network is a useful topic to learn nowadays , from image recognition ,video analysis to natural language processing , their applications are everywhere. Modular and. This is from a Custom Keras Callback casted=K. h5" model in Keras. The CIFAR-10 and CIFAR-100 are labeled subsets of the 80 million tiny images dataset. csr_matrix Convert this matrix to Compressed Sparse Column format. Ok, let us create an example network in keras first which we will try to port into Pytorch. You’ll find more examples and information on all functions. Is there any way how I can achieve it?. This TensorRT 6. I have already applied the ANN model strange effects to a highly nonlinear regression problem and encountered some strange effects which I was not able to get rid of. fit(), adding the tf. Keras is a high-level API to build and train deep learning models. Keras Tutorial: The Ultimate Beginner's Guide to Deep Learning in Python Share Google Linkedin Tweet In this step-by-step Keras tutorial, you'll learn how to build a convolutional neural network in Python!. Big deep learning news: Google Tensorflow chooses Keras Written: 03 Jan 2017 by Rachel Thomas. A notable example is Keras FaceNet by Hiroki Taniai. Guide to Keras Basics. How do I get around with this problem?. Being able to go from idea to result with the least possible delay is key to doing good research. The callback we need for checkpointing is the ModelCheckpoint which provides all the features we need according to the checkpointing strategy we adopted in our example. Ok, let us create an example network in keras first which we will try to port into Pytorch. There are two options when using TFLite converter to convert the Keras model to the tflite format - 1) from the command line or 2) convert directly in your python. In this tutorial, we walked through how to convert, optimized your Keras image classification model with TensorRT and run inference on the Jetson Nano dev kit. metrics import accuracy_score import theano import theano. I'm not sure what your get_train_gen() function is doing, but it should be returning an ImageDataGenerator object. This computes the internal data stats related to the data-dependent transformations, based on an array of sample data. tensorflow import TensorFlowConverter from webdnn. You have to use Keras backend functions. Finally this merged layer goes through multiple layers of LSTMs. Under the hood it turns the pixels into a 3D matrix of numbers. fit fit(x, augment=False, rounds=1, seed=None) Fits the data generator to some sample data. This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i. h5" model in Keras. We'll build a custom model and use Keras to do it. The power of Keras is that it abstracts a lot of things we had to take care while we were using TensorFlow. I performed transfer learning using ssd + mobilenet as my base model in tensorflow and freezed a new model. The original Keras simple model has 14KB and the tflite version has only 1. As a keras user, probably you’re familiar with the sequential and functional styles of building a model. If you have X_train and Y_train and a generator datagen defined using. Pre-trained models and datasets built by Google and the community. js layers format (which we already did in lines 36-38). Keras Embedding layer Failed to convert object of type to Tensor. I tried var. In this tutorial, we walked through how to convert, optimized your Keras image classification model with TensorRT and run inference on the Jetson Nano dev kit. Named Tensor (experimental) (experimental) Introduction to Named Tensors in PyTorch; Reinforcement Learning. Of course, even if we can’t modify the elements of a tuple, we can always make the julia variable reference a new tuple holding different information. To complete François Chollet's answer and to give a little bit more on why you should consider using tf-slim: First, tf-slim is more than ju. Convert class vector (integers from 0 to nb_classes) to binary class matrix, for use with categorical_crossentropy. Requirements. saved_model import builder as saved_model_builder. import keras import keras. js is a two-step process. applications) then read the TensorFlow. The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. convert() End-to-end MobileNet conversion The following example shows how to convert and run inference on a pre-trained tf. We build a custom activation layer called 'Antirectifier', which modifies the shape of the tensor that passes through it. Szegedy, Christian, et al. graph import Graph from webdnn. A blog about software products and computer programming. For example: import numpy as np def my_func(arg): arg = tf. Jun 14 ・1 min read. Converting multiple # functions is under development. It helps to understand one of the most important technology that is edge computing which enables to run the model on the devices instead of running from the server. I have fine-tuned inception model with a new dataset and saved it as ". Keras is also integrated into TensorFlow from version 1. Image captioning is a challenging task at intersection of vision and language. float32) return tf. To bring this across has been a major point of Google's TF 2 information campaign since the early stages. There are two options when using TFLite converter to convert the Keras model to the tflite format - 1) from the command line or 2) convert directly in your python. layers import Input input_img = Input(shape = (32, 32, 3)) Now, we feed the input tensor to each of the 1x1, 3x3, 5x5 filters in the inception module. nb_filter: number of filters growth_rate: growth rate bottleneck: bottleneck block dropout_rate: dropout rate weight_decay: weight decay factor grow_nb_filters: flag to decide to allow number of filters to grow return_concat_list: return the list of feature maps along with the actual output Returns: keras tensor with nb_layers of conv_block. 0 and to take a current deep learning project and convert it to something that runs smoothly and quickly on cloud TPUs. NewCheckpointReader then call set_weights() method for each corresponding layer. Read the elements of a using this index order, and place the elements into the reshaped array using this index order. Guide to Keras Basics. 0 (Sequential, Functional, and Model subclassing) In the first half of this tutorial, you will learn how to implement sequential, functional, and model subclassing architectures using Keras and TensorFlow 2. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. We will use NumPy to create an array like this: import numpy as np arr = np. TFlearn is a modular and transparent deep learning library built on top of Tensorflow. 8/ /usr/lib. These layers require the input to be a vector, whereas our data is a 3d cube of size 7x7x64. 0 is released to the public! Here is a blog post about the new changes. Purchase Order Number SELECT PORDNMBR [Order ID], * FROM PM10000 WITH(nolock) WHERE DEX_ROW_TS > '2019-05-01';. Print() won’t work because, well, I don’t have tensors. This post is focused on converting our model to an Estimator — if we wanted to improve accuracy we could try tuning our model’s hyperparameters, changing our vocabulary size, or adding dropout to our input layer. How to Convert Keras model into Tensorflow lite Koji. The latest Keras functional API allows us to define complex models. js layers format (which we already did in lines 36-38). {\displaystyle J={{|A\cap B|} \over {|A\cup B|}}={{|A\cap B|} \over {|A|+|B. tensor_list (a list or tuple of Tensors that all have the same shape in the axes not specified by the axis argument. import traceback from collections import defaultdict from typing import List, Optional import numpy as np from webdnn. float32, float64). now my goal is to run my model on android Tensorflow which accepts ". VGG model weights are freely available and can be loaded and used in your own models and applications. csr_matrix Convert this matrix to Compressed Sparse Column format. In gereral, Keras has no way to save its model to. A set of features or parameters can be initialized to the ImageDataGenerator such as rescale, shear_range, zoom_range etc. Keras’ Sequential() is a simple type of neural net that consists of a “stack” of layers executed in order. I can't find anywhere in the documentation a succinct explaination of how to convert a numpy array into a keras tensor via the backend API. cast((yPred), K. to_categorical (y, nb_classes). Sequence keras. Update: since my answer, tf-slim 2. Here I would like to give a piece of advice too. Now I want to convert that model into pytorch. There are two options when using TFLite converter to convert the Keras model to the tflite format - 1) from the command line or 2) convert directly in your python. You can also use a plain HTMLImageElement or even a video. In this excerpt from the book Deep Learning with R, you’ll learn to classify movie reviews as positive or negative, based on the text content of the reviews. >>> from torch. layers import Dense, Activation model = Sequential([ Dense(32, input_shape=(784,)), Activation('relu'), Dense(10), Activation('softmax'), ]). Converting a torch Tensor to a numpy array and vice versa is a breeze. They are mostly used with sequential data. I would highly recommend checking also the other toco/tflite_convert_tool params. Keras Implementation. autograd import Variable >>> a = Variable ( torch. Great, we have. Convert Keras model to tflite. I'm using keras 2.