Keras Print Tensor Not Working

In this tutorial, we're going to be finishing up by building. With high-level neural network libraries like Keras, we will not need to implement this formula. This issue also left me perplexed for quite some time. it took me the whole weekend to get the install working. These are materials for a webinar given Wednesday May 24, 2017. TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. In order to train GoogLeNet in Keras, you need to feed three copies of your labels into the model. Personally, I am not yet in a situation where GPU support is really required. Stretch Fresh install Keras with Tensorflow as backend Python 3. Building TRT engine Resnet50 from Keras not working but from ONNX yes. This is included in the example file test_keras. com/jeffheaton/t81_558 Please. Share Tweet To leave a comment for the author, please follow the link and comment on their blog: R - Quality and Innovation. This makes debugging and trying out things in pytorch much easier. There are two main types of models available in Keras: the Sequential model, and the Model class used with the functional API. Finally made a system for working with Keras Layers. 1 and 10 in less than 4 hours Introduction If you want to install the main deep learning libraries in 4 hours or less and start training your own models you have come to the right place. Using print_tensor prints only the first three elements in a tensor. Dense(10, activation='softmax') Go deep. Programs on 0. Here may be a list of Keras optimizers from the documentation. Apple disclaims any and all liability for the acts, omissions and conduct of any third parties in connection with or related to your use of the site. As shown in the graph, TensorFlow is the most popular and widely used deep learning framework right now. In the first part of this tutorial, we’ll discuss the concept of an input shape tensor and the role it plays with input image dimensions to a CNN. There are two main types of models available in Keras: the Sequential model, and the Model class used with the functional API. device=cuda2. Print(input_, data)[/code] requires the “input_” as the tensor it evaluates and “data” as a list of what to actually print out. I am not aware of any incompatibilities with taking a model trained with an older version of Tensorflow and using it for inference in a new version of Tensorflow. There is an excellent gist by Bohumír Zámečník working around these issues, but it uses the. When one starts working on a specific problem where a small amount of training data is available, one takes these pre-trained weights and continue training. Print() and neither work. eval in your loss function because the tensors are not initialized. Tensorflow Implementation Note: Installing Tensorflow and Keras on Windows 4 minute read Hello everyone, it's been a long long while, hasn't it? I was busy fulfilling my job and literally kept away from my blog. by Jaime Sevilla @xplore. I was happy to find that tensorflow detected the GPU (as posted below) BUT our code still runs painfully slow. " In the code above, since y_pred was assigned to x and x was no longer used, the print failed. Create a tensor of size (5 x 7) with uninitialized memory: Some operations like narrow do not have in-place versions, print (a) print (b) # see how the numpy. There are many tutorials with directions for how to use your Nvidia graphics card for GPU-accelerated Theano and Keras for Linux, but there is only limited information out there for you if you want to set everything up with Windows and the current CUDA toolkit. Print doesn’t print the whole tensor ) and they were mostly zero. But was it hard? With the whole session. (200, 200, 3) would be one valid value. Printing statistics of tensors. Create a tensor of size (5 x 7) with uninitialized memory: Some operations like narrow do not have in-place versions, print (a) print (b) # see how the numpy. Please use a supported browser. However my experience with Keras and machine learning is limited and I'm unable to run code below and getting error: AttributeError: 'Tensor' object has no attribute '_keras_shape'. Return a list of Tensor which are all weights of this Layer. print_tensor() and tf. This is convenient in interactive shells and IPython notebooks, as it is not required to pass an explicit session object to run an operation. Choice is matter of taste and particular task; We'll be using Keras to predict handwritten digits with the mnist. $\endgroup$ - André Bergner Aug 5 '17 at 12:21. Image Recognition (Classification). I am not aware of any incompatibilities with taking a model trained with an older version of Tensorflow and using it for inference in a new version of Tensorflow. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. In particular, a shape of [-1] flattens into 1-D. You have no items in your shopping cart. one_hot must be an integer tensor, but by default Keras passes around float tensors. Otherwise the print operation is not taken into account during evaluation. If you are familiar with Machine Learning and Deep Learning concepts then Tensorflow and Keras are really a playground to realize your ideas. If the learning_phase is set statically, Keras will be locked to whichever mode the user selected. Use the version below. Generating interesting arrays can be difficult, but images provide a great option. This issue also left me perplexed for quite some time. TensorFlow is an open source library for neural networks and deep learning developed by the Google Brain team. To begin, install the keras R package from CRAN as. This function will also work for larger tensors, as long as abs(K. Learning AI if You Suck at Math — Part 2 — Practical Projects — This article guides you through getting started with your first projects. 5 Developer Guide demonstrates how to use the C++ and Python APIs for implementing the most common deep learning layers. However, Keras is more restrictive than the lower-level frameworks, so there are some very complex models that you can implement in TensorFlow but not (without more difficulty) in Keras. importing tensorflow will not work in jupyter (check if. 1 Description Interface to 'Keras' , a high-level neural networks 'API'. Keras and TensorFlow can be configured to run on either CPUs or GPUs. In this post we explain the basic concept and general usage of RoI (Region of Interest) pooling and provide an implementation using Keras layers and the TensorFlow. Keras can get you to a working model faster. I have changed my package code of Keras, and I changed the code tf. If the category doesn't exist in ImageNet categories, there is a method called fine-tuning that tunes MobileNet for your dataset and classes which. 0: models migration and new design that will guide you through the differences between the 1. This function will also work for larger tensors, as long as abs(K. The following are code examples for showing how to use keras. If you haven’t seen the last five, have a look now. This lesson introduces you to the concept of TensorFlow. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. print_tensor(x, message='') Prints message and the tensor value when evaluated. 5 (3 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect. In order to make our neuron learn, we need something to progressively change from 0 to 1: a continuous (and derivative) function. When we start using neural networks we use activation functions as an essential part of a neuron. By Dana Mastropole, Robert Schroll, and Michael Li TensorFlow has gathered quite a bit of attention as the new hot toolkit for building neural networks. Two of the top numerical platforms in Python that provide the basis for Deep Learning research and development are Theano and TensorFlow. It is a reference to a literary image from ancient Greek and Latin literature, first found in the Odyssey, where dream spirits (Oneiroi, singular Oneiros) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive. OpenAI Gym is a Python-based toolkit for the research and development of reinforcement learning algorithms. Welcome to the next tutorial covering deep learning with Python, Tensorflow, and Keras. As shown in the graph, TensorFlow is the most popular and widely used deep learning framework right now. These are materials for a webinar given Wednesday May 24, 2017. Discover why Python is popular, how all major deep learning frameworks support Python, including the platforms TensorFlow, Keras, and PyTorch. Build the Layer. For example, packages for CUDA 8. ImageDataGenerator is an in-built keras mechanism that uses python generators ensuring that we don't load the complete dataset in memory, rather it accesses the training/testing images only when it needs them. This function is intended for advanced use cases where a custom loss is desired. Deep Learning Installation Tutorial - Part 3 - CNTK, Keras and PyTorch. Learn about Python text classification with Keras. See 2 tutorials. This page explains what 1D CNN is used for, and how to create one in Keras, focusing on the Conv1D function and its parameters. The flip side of this convenience is that programmers may not realize what the dimensions are, and may make design errors based on this lack of understanding. Convolutional Neural Networks are very popular in Deep Learning applications. The value to watch is not acc but val_acc, or Validation Accuracy. In this tutorial, we'll cover the theory behind text generation using a Recurrent Neural Networks. print_tensor. print_tensor(x, message='') Prints message and the tensor value when evaluated. It is also encouraged to set the floating point precision to float32 when working on the GPU as that is usually much faster. You can vote up the examples you like or vote down the ones you don't like. This post introduces how to install Keras with TensorFlow as backend on Ubuntu Server 16. Pytorch Write Tensor To File. TensorFlow can be configured to run on either CPUs or GPUs. Deep Learning with Keras. TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. 5 I typed: conda create -n tf-keras python=3. Rd Note that print_tensor returns a new tensor identical to x which should be used in the following code. Google's TensorFlow provides a Dataset API that is very useful for importing machine learning datasets. Change input shape dimensions for fine-tuning with Keras. This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i. You can vote up the examples you like or vote down the ones you don't like. GPU versions from the TensorFlow website: TensorFlow with CPU support only. version Tensorflow and Tensor Board - working together. TensorFlow is an open source software library created by Google that is used to implement machine learning and deep learning systems. Use hyperparameter optimization to squeeze more performance out of your model. If you haven’t read that blog post, we used Stack Overflow data from BigQuery to train a model to predict the tag of a Stack Overflow question. Open the \lib\site-packages\keras\utils\visualize_util. Mastering TensorFlow 1. There is also a pure-TensorFlow implementation of Keras with deeper integration on the roadmap for later this year. The number of elements in the tensors must match, but the sizes do. 06 TensorFlow container on a DGX-2 (or other systems having more than 8 GPUs) with RHEL 7. Hence, in this tutorial, I'll stick to TensorFlow Mobile. Once I did this, though, I got only the first 3 or 4 values (By default, tf. Here's the guidance on CPU vs. Deep Learning with Keras. Here is the function:. visualize_activation_with_losses(input_tensor, losses, wrt_tensor=None, seed_input=None, \ input_range=(0, 255), **optimizer_params) Generates the input_tensor that minimizes the weighted losses. But was it hard? With the whole session. To the beginner, it may seem that the only thing that rivals this interest is the number of different APIs which you can use. Keras and TensorFlow can be configured to run on either CPUs or GPUs. ValueError: Tensor Tensor("predictions/Softmax:0", shape=(?, 1000), dtype=float32) is not an element of this graph. Getting deeper with Keras Tensorflow is a powerful and flexible tool, but coding large neural architectures with it is tedious. This is true even for a single constant. Note something cool - we defined operations d and e which need to be calculated before we can figure out what a is. Once you login to your system, go to the control panel, and then to the 'Uninstall a program' link. These two names contain a series of powerful algorithms that share a common challenge—to allow a computer to learn how to automatically spot complex patterns and/or to make best possible decisions. which will allow you to refer to the metric by name just like you do with built in keras {# create metric using backend tensor. Tensorflow Implementation Note: Installing Tensorflow and Keras on Windows 4 minute read Hello everyone, it's been a long long while, hasn't it? I was busy fulfilling my job and literally kept away from my blog. The number of elements in the tensors must match, but the sizes do. This issue also left me perplexed for quite some time. Instead, it relies on a specialized, well-optimized tensor library to do so, serving as the backend engine of Keras. Pytorch Save Tensor To Text File. Tensorflow 1. Albeit there exist many How-To's, most of the newer once are covering finetuning VGG or Inception Models and not AlexNet. You can optionally target a specific gpu by specifying the number of the gpu as in e. Convolutional Neural Networks are very popular in Deep Learning applications. Otherwise the print operation is not taken into account during evaluation. Course Overview Hi, my name is Jerry Kurata and welcome to my course Deep Learning with Keras. Getting Started Installation. Since the input data for a deep learning model must be a single tensor (of shape e. As written in the Keras documentation, "If you are running on the TensorFlow backend, your code will automatically run on GPU if any available GPU is detected. If you carefully consider, there may be two major reasons by which your neural network may not work correctly - Your neural network architecture is incorrect. This lesson introduces you to the concept of TensorFlow. This site contains user submitted content, comments and opinions and is for informational purposes only. On Windows 10 x64 I have installed Anaconda python 3. Let's imagine we have the following type of data to work with a hypothetical example. Clearly this was not working! Now, my years of experience came in handy! 🙂 The steps which I mention below are those which I usually follow when I'm stuck with this kind of problem. Pretty much I want to do something like this:. This packages contains tensor operation (conv2d, conv1d, depthwise conv2d, fully connected, conv2d transpose), tensor losses (softmax & sigmoid cross entropy, MSE), and tensor metrics (accuracy). Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. As you train the neural net, Keras will output running stats on what epoch you're in, how much time is left in that epoch of training, and current accuracy. It doesn’t handle low-level operations such as tensor manipulation and differentiation. tegrastats. by Jaime Sevilla @xplore. com/jeffheaton/t81_558 Please. 06 TensorFlow container on a DGX-2 (or other systems having more than 8 GPUs) with RHEL 7. In Keras, MobileNet resides in the applications module. The following are code examples for showing how to use keras. Print and K. and the last one tells keras how much info to print out during. This book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. Once I did this, though, I got only the first 3 or 4 values (By default, tf. IllegalArgumentException: You must feed a value for the placeholder tensor 'ls1/keras_learning_phase' with dtype bool. When it comes to Keras, it’s not working independently. Bounty: 100. pip install keras This was followed by. TensorFlow is an open source library for neural networks and deep learning developed by the Google Brain team. In this post, we’ll update the code we wrote in the article building a text classification model with Keras. Many thanks to ThinkNook for putting such a great resource out there. Can you use Distribution Strategies directly with the Keras model, instead of passing through Estimator? There are likely bad effects here resulting from the mixing of many APIs (including v1 optimizers), and if you can swap to the fully v2 version (Dist Strat + keras optimizers + keras model + keras LSTM), this should work. , a deep learning model that can recognize if Santa Claus is in an image or not): Part 1: Deep learning + Google Images for training data. At most one component of shape can be -1. Two of the top numerical platforms in Python that provide the basis for Deep Learning research and development are Theano and TensorFlow. Welcome to the next tutorial covering deep learning with Python, Tensorflow, and Keras. It seemed like a good transition as TF is the backend of Keras. They are extracted from open source Python projects. Print you may control this with the summarize parameter, but print_tensor does not take any such parameters. When one starts working on a specific problem where a small amount of training data is available, one takes these pre-trained weights and continue training. 04 Last updated: 11 Sep 2016 Source Using GPUs to process tensor operations is one of the main ways to speed up training of large, deep neural networks. Pre-trained models and datasets built by Google and the community. Instead of providing all the functionality itself, it uses either TensorFlow or Theano behind the scenes and adds a standard, simplified programming interface on top. This is not possible in real scenarios because in real life all we learn step-by-step. Print doesn't print the whole tensor ) and they were mostly zero. layers is a flattened list of the layers comprising the model. This function is intended for advanced use cases where a custom loss is desired. com/watch?v=59duI You can find the instructions here (from the video): https://github. Showing 1-5 of 5 messages. Given a input tensor, returns a new tensor with the same values as the input tensor with shape shape. Pass save_format='h5' to use HDF5. I decided to post an answer with an autoencoder walk-through, but for the reasons of simplicity I used Keras (running on top of Tensorflow) instead of raw Tensorflow. This tutorial will help you to get started with TensorBoard, demonstrating some of its capabilities. It turns out [code ]tf. Part of the compilation process is to select operation variants that depend on the number of dimensions. keras version in the latest TensorFlow release might not be the same as the latest keras version from PyPI. I want to check some values of my Keras tensor. It is similar to the one in Yoni's tutorial, and it also helps you with the Keras Learning Phase error, which happens when you run your model on android. Image classification is not just about classifying images into categories, it has a broader and deeper meaning of giving machines the power to visualize the world. bitwise_not Note that the default value of 2 is not guaranteed to work for all values of a and b. The visual cortex is not convolutional to begin with, and while it is structured in layers, the layers are themselves structured into cortical columns whose exact purpose is still not well understood --a feature not found in our artificial networks (although Geoff Hinton is working on it). Download with Google Download with Facebook or download with email. With that using an. python - Using Keras & Tensorflow with AMD GPU - Stack Overflow. Share Tweet To leave a comment for the author, please follow the link and comment on their blog: R - Quality and Innovation. Arrays are powerful structures, as we saw briefly in the previous tutorial. This doesn't work when using the Tensorflow backend because K. Build a simple model. It seemed like a good transition as TF is the backend of Keras. Build a simple model. Keras is a high-level neural networks API, developed with a focus on enabling fast experimentation and not for final products. In particular, a shape of [-1] flattens into 1-D. In this tutorial, we will learn the basics of Convolutional Neural Networks ( CNNs ) and how to use them for an Image Classification task. If you haven’t seen the last five, have a look now. I've learned the hard way that even if everything to this point is perfect, your autopilot won't work if you don't train it correctly. [2] 다음 단계에서는 Loss Function, Optimizer, Accuracy Metrics를 정의하고 학습시킨다. Keras Functions for Image Processing Keras has a function called ImageDataGenerator that provides you with batches of tensor image data with real-time data augmentation. It's for beginners because I only know simple and easy ones ;) 1. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Pre-trained models and datasets built by Google and the community. $\endgroup$ - André Bergner Aug 5 '17 at 12:21. You can optionally target a specific gpu by specifying the number of the gpu as in e. This function will also work for larger tensors, as long as abs(K. Using print_tensor prints only the first three elements in a tensor. I think both the libraries are fascinating with their pros one over the other. You are going to learn step by step how to freeze and convert your trained Keras model into a single TensorFlow pb file. run(a) command and assign the output to a_out, the value of which we then print out. Since the Keras module in TensorFlow is tf. Learn about Python text classification with Keras. Let us begin with the objectives of this lesson. It turns out [code ]tf. Numpy was more closely resemble. Now, when we want to restore it, we not only have to restore the graph and weights, but also prepare a new feed_dict that will feed the new training data to the network. It should go without saying that you can obviously develop your own custom checkpoint strategy based on your experiment needs!. Keras - printing intermediate tensors in loss function (tf. If you want an intro to neural nets and the "long version" of what this is and what it does, read my blog post. Develop Your First Neural Network in Python With this step by step Keras Tutorial!. This is true even for a single constant. Keras' backend is set in a hidden file stored in your home path. device=cuda2. Choice is matter of taste and particular task; We'll be using Keras to predict handwritten digits with the mnist. 1 and Keras 1. I do not claim to have nailed the order of operations here, but definitely one that works. If you do not select NV/NC options, then you are not using a GPU instance and the setup scripts later will fail. You can find it at $/. The purpose of this tutorial is to help anybody write their first RNN LSTM model without much background in Artificial Neural Networks or Machine Learning. However, the weights file is automatically downloaded ( one-time ) if you specify that you want to load the weights trained on ImageNet data. print_tensor(x, message='') Prints message and the tensor value when evaluated. A few days ago after upgrading to Ubuntu 16. one_hot), but this has a few caveats - the biggest one being that the input to K. Welcome everyone to an updated deep learning with Python and Tensorflow tutorial mini-series. I want to check some values of my Keras tensor. How to run Keras code in TensorFlow Modify import. 06 TensorFlow container on a DGX-2 (or other systems having more than 8 GPUs) with RHEL 7. On Windows 10 x64 I have installed Anaconda python 3. Note that print_tensor returns a new tensor identical to x which should be used in the following code. This TensorRT 5. print_tensor(softmax_layer, message='My softmax values: ') yields: My softmax values: [[-0. In this tutorial, we will learn the basics of Convolutional Neural Networks ( CNNs ) and how to use them for an Image Classification task. MX BSP Porting Guide available as part of the BSP documentation and the Yocto Project documentation. As a "non-trivial" example of using this setup we'll go through training LeNet-5 with Keras using TensorFlow with GPU acceleration. From a cursory look, it seems that OpenCL is not supported directly however some searching reveals: How can I install and work with Tensor Flow with a machine that does not have an NVIDIA graphics card? - Quora. To do this, we'll use the Keras class Model. It provides a mechanism to represent, transform and build complex machine learning data…. Once you installed the GPU version of Tensorflow, you don't have anything to do in Keras. I've learned the hard way that even if everything to this point is perfect, your autopilot won't work if you don't train it correctly. 0 then you can import it in your project with following code: from __future__ import absolute_import, division, print_function, unicode_literals import tensorflow as tf from tensorflow. On a side note: TensorFlow creates a default graph for you, so we don't need the first two lines of the code above. However, it was not working from my Jupyter notebook. python - Using Keras & Tensorflow with AMD GPU - Stack Overflow. It just defined the model, but no process ran to calculate the result. Pass save_format='h5' to use HDF5. This is going to be a tutorial on how to install tensorflow 1. If you carefully consider, there may be two major reasons by which your neural network may not work correctly - Your neural network architecture is incorrect. 0407191925 -0. and the last one tells keras how much info to print out during. In fact you could even train your Keras model with Theano then switch to the TensorFlow Keras backend and export your model. sh shows gpu usage only from 0-12% while the keras python program is running, so I'd assume it is not in fact using the GPU?. First need download opencv or not? to read, resize, convert grayscale Need install numpy? Keras or tensor flow need to install? Keras is one lib that inside tensor flow? What to start first? I view many webpage and github code. Do not use URL shorteners, referral links or click-trackers. In the first part of this tutorial, we'll discuss the concept of an input shape tensor and the role it plays with input image dimensions to a CNN. Here may be a list of Keras optimizers from the documentation. Deep Learning with Keras. If you are not at the current version, you can always upgrade it using pip as explained earlier. This is why we used w as the Variable, and not two separate Variables a and b. How to access the new environment on Anaconda. One of the talks I attended was Introduction to Deep Learning for Images in Keras presented by Stephane Egly and Malaikannan (Malai) Sankarasubbu. print_tensor(softmax_layer, message='My softmax values: ') yields: My softmax values: [[-0. Why this name, Keras? Keras (κέρας) means horn in Greek. You get keras tensors from keras. …Including Keras and Tensorflow. I am not aware of any incompatibilities with taking a model trained with an older version of Tensorflow and using it for inference in a new version of Tensorflow. Using print_tensor prints only the first three elements in a tensor. To run any of the three defined operations, we need to create a session for that graph. They are not on the same scale. sh shows gpu usage only from 0-12% while the keras python program is running, so I'd assume it is not in fact using the GPU?. Once I did this, though, I got only the first 3 or 4 values (By default, tf. Getting Started Installation. Being able to go from idea to result with the least possible delay is key to doing good research. inputs is the list of input tensors of the model. Build a convolutional neural network in keras using the latest Tensorflow 2 API. If it still does not work, divide the learning rate by ten. Convert Keras Model to Tensorflow Serving. Return a list of Tensor which are all weights of this Layer. We were thinking how to get our great models to users. Learning AI if You Suck at Math — Part 1 — This article guides you through the essential books to read if you were never a math fan but you're learning it as an adult. python3 keras_script. By Dino Causevic, Toptal. The flip side of this convenience is that programmers may not realize what the dimensions are, and may make design errors based on this lack of understanding. Most solutions glossed over key steps, others just didn't work. We always assume the dimension to perform the dot is the last one, and that the masks have one fewer dimension than the tensors. In the first part of this tutorial, we’ll discuss the concept of an input shape tensor and the role it plays with input image dimensions to a CNN. All levels from Keras to Theano. You get keras tensors from keras. The visual cortex is not convolutional to begin with, and while it is structured in layers, the layers are themselves structured into cortical columns whose exact purpose is still not well understood --a feature not found in our artificial networks (although Geoff Hinton is working on it). That being said, Keras will work fine for many common models. Description. keras/keras. Convolutional Neural Networks are very popular in Deep Learning applications. It is one of the more recent of many open-source deep-learning libraries to be released, after Tensorflow and CNTK, and Amazon has since backed MxNet with AWS, so its future is not clear. It has the following models ( as of Keras version 2. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. 4 How did Keras implement Batch Normalization over time?. Keras and TensorFlow are the state of the art in deep learning tools and with the keras package you can now access both with a fluent R interface. packages(keras) in R which included tensorflow as a dependency. Updated version: https://www. I was forced to use another instance with a higher GPU capacity. We always assume the dimension to perform the dot is the last one, and that the masks have one fewer dimension than the tensors. The number of dimensions must be fixed at the time the Theano function is compiled. get_tensor_by_name() method. Let us begin with the objectives of this lesson. Print -> tf. This issue also left me perplexed for quite some time. Since the Keras module in TensorFlow is tf. Want to install TENSORFLOW for KERAS in Python. When saving a model's weights, tf. In summary, replace this line:. However my experience with Keras and machine learning is limited and I'm unable to run code below and getting error: AttributeError: 'Tensor' object has no attribute '_keras_shape'. Being able to go from idea to result with the least possible delay is key to finding good models. Other Optimisation. Why and how? 4. that the bottleneck is indeed the neural network's forward and backward operations on the GPU (and not data generation). Pytorch Save Tensor To Text File. This can be a problem if you want to embed a model in a non-Python application. Feeding your own data set into the CNN model in Keras print num_samples I am working with CNN in keras for face detection, specifically facial gestures. Most solutions glossed over key steps, others just didn't work.