Keras Backend Print Tensor

Keras only asks that you provide the dimensions of the input tensor(s), and it figure out the rest of the tensor dimensions automatically. mean时,默认axis=None,会在整个batch级别做平均 完整的测试代码,可以尝试变更Lamda层的注释体会默认batch级别平均的坑爹之处. Convolutional variational autoencoder with PyMC3 and Keras¶ In this document, I will show how autoencoding variational Bayes (AEVB) works in PyMC3’s automatic differentiation variational inference (ADVI). Keras backend API. Lancaster stemming library is used to collapse distinct word forms: import nltk from nltk. 04 LTS with CUDA 8 and a NVIDIA TITAN X (Pascal) GPU, but it should work for Ubuntu Desktop 16. 0 module has been loaded in workflow. Theano can be used separately via the theano/0. backend import * from keras. input_tensor: optional Keras tensor (i. A Keras tensor is a tensor object from the underlying backend (Theano, TensorFlow or CNTK), which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model. You want the model to save each epoch if and only if the validation loss is lower than all previous epochs. 케라스 Basic [1] 케라스의 모델 정의 방법은 크게 2가지가 있다. Installing Keras, Theano and TensorFlow with GPU on Windows 8. shape minus last dimension => (1,2,3) concatenated with. 解决方法 本人在写Django RESful API时,碰到一个难题,老出现,整合Keras,报如下错误;很纠结,探索找资料近一个星期,皇天不负有心人,解决了. cast keras. input选用一层的输入张量为模型的输入张量. feature_column. 케라스 소개 케라스는 거의 모든 종류의 딥러닝 모델을 간편하게 만들고 훈련시킬 수 있는 파이썬을 위한 딥러닝 프레임워크입니다. A simple example showing how to explain an MNIST CNN trained using Keras with DeepExplainer. summary() to print the shapes of all of the layers in your model. input选用一层的输入张量为模型的输入张量. model_selection import train_test_split # -- Keras Import from keras. Contribute to keras-team/keras development by creating an account on GitHub. The Python programming language is one of the most popular languages for programmers in the 21st century. I've shuffled the training set, divided it. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xHxb-dlhMIzW" }, "source": [ "## Overview\n", "\n", "`tf. callbacks import Callback import tensorflow as tf CPU_0. This is a useful tool when trying to understand what is going on inside the layers of a neural network. 预训练权重由我们自己训练而来,基于MIT License. Rationale ¶. Join GitHub today. , **, /, //, % for Theano. While Keras is great to start with deep learning, with time you are going to resent some of its limitations. I'm kinda new to this field, so I started tinkering with some models in Keras (using Tensorflow backend). dtype: Tensor type. In order to train your own custom neural networks, Keras required a backend. To use Keras and Tensor Processing Units (TPUs) to build your custom models faster. mean时,默认axis=None,会在整个batch级别做平均 完整的测试代码,可以尝试变更Lamda层的注释体会默认batch级别平均的坑爹之处. 90 KB from keras. Oct 28, such as objectives loss function, 2018 - backend tensor. 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. Variational auto-encoder for "Frey faces" using keras Oct 22, 2016 In this post, I'll demo variational auto-encoders [Kingma et al. 케라스 튜토리얼 29 Jun 2018 | usage Keras. While the Keras FAQ suggests building partial models to achieve this, and this post suggests setting up functions to evaluate a layer, I was tempted to look into the backend's and TensorFlow's Print function to display the values of a tensor during the actual computation, as this creates an actual node in the computational graph, and prints the values as the model is used. one_hot must be an integer tensor, but by default Keras passes around float tensors. TypeError: x and y must have the same dtype, got tf. backend to build functions that, provided with a valid input tensor, return the corresponding output tensor. So this talk will mix information about how to use the Keras API in TensorFlow and how the Keras API is implemented under the hood. py定義されています。. But was it hard? With the whole session. feature_column. Returns: A variable instance (with Keras metadata included). backend; Functions. batch_dot keras. We have detected your current browser version is not the latest one. A 2-dimensions tensor is a matrix. Keras tensor x has the. layers import Dense, Activation, Dropout. In particular, a shape of [-1] flattens into 1-D. 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. inception_v3 import InceptionV3 from keras. Lancaster stemming library is used to collapse distinct word forms: import nltk from nltk. To be more précised, Keras act as a wrapper for these frameworks. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the "backend engine" of Keras. ResNet-50 training throughput (images per second) comparing Keras using the MXNet backend (green bars) to a native MXNet implementation (blue bars). You can vote up the examples you like or vote down the ones you don't like. Good software design or coding should require little explanations beyond simple comments. I sort of thought about moving to Tensorflow. To understand the concept of a backend, consider building a website from scratch. eval in your loss function because the tensors are not initialized. layers as layers # 定义网络层就是:设置网络权重和输出到输入的计算过程 class MyLayer (layers. sequence_categorical_column_with_identity tf. returns a tensor of size. In this blog post, we’ll demonstrate how to deploy a trained Keras (TensorFlow or MXNet backend) or TensorFlow model using Amazon SageMaker, taking advantage of Amazon SageMaker deployment capabilities, such as selecting the type and number of instances, performing A/B testing, and Auto Scaling. 13, Theano, and CNTK. 我们从Python开源项目中,提取了以下50个代码示例,用于说明如何使用keras. - 使用Lambda自定义Layer时,Lambda中使用Backend语法操作对象,操作的是Tensor - 当使用K. import tensorflow as tf from keras import backend as K x = K. Pretty much I want to do something like this:. tensorflow_backend import _preprocess_conv3d_input, _preprocess_conv3d_kernel, _preprocess_border_mode, _postprocess_conv3d_output. backend to build functions that, provided with a valid input tensor, return the corresponding output tensor. a Inception V1). Lancaster stemming library is used to collapse distinct word forms: import nltk from nltk. Please ask usage questions on stackoverflow, slack, or the google group. Everything fine. I'm kinda new to this field, so I started tinkering with some models in Keras (using Tensorflow backend). import tensorflow as tf from keras import backend as K x = K. models import Sequential from keras. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. Keras is a Python library that provides, in a simple way, the creation of a wide range of Deep Learning models using as backend other libraries such as TensorFlow, Theano or CNTK. Front Page DeepExplainer MNIST Example¶. A tutorial about setting up Jetson TX2 with TensorFlow, OpenCV, and Keras for deep learning projects. This script is part of a 3-part workflow, see example here: Panther 3-step Keras workflow. The code below has the aim to quick introduce Deep Learning analysis with TensorFlow using the Keras back-end in R environment. io ) § High-level API § Focus on user experience § "Deep learning accessible to everyone" § History § Announced at Feb. Lancaster stemming library is used to collapse distinct word forms: import nltk from nltk. Keras Tutorial, Keras Deep Learning, Keras Example, Keras Python, keras gpu, keras tensorflow, keras deep learning tutorial, Keras Neural network tutorial, Keras shared vision model, Keras sequential model, Keras Python tutorial. 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. feature_column. ResNet-50 training throughput (images per second) comparing Keras using the MXNet backend (green bars) to a native MXNet implementation (blue bars). function( inputs, outputs, updates=None, name=None_来自TensorFlow官方文档,w3cschool编程狮。. Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape. from keras import backend as K print(K. In this post, you will discover the Keras Python. The Python programming language is one of the most popular languages for programmers in the 21st century. import numpy as np import pandas as pd import theano import theano. For a single GPU, the difference is about 15%. print_tensor keras. '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. Description. sequence_categorical_column_with_vocabulary_list tf. The example here is borrowed from Keras example, where convolutional variational autoencoder is applied to the MNIST dataset. com uses the latest web technologies to bring you the best online experience possible. I understand that you cannot do K. lancaster import LancasterStemmer stemmer = LancasterStemmer() # things we need for Tensorflow import numpy as np from keras. one_hot), but this has a few caveats - the biggest one being that the input to K. CHRYSLER OEM Front Seat-Cushion Cover-Top Back Left 1JL661J8AA,EBC Brakes S13KF1432 S13 Kits Yellowstuff and RK Rotors,NAJS3T 3 Ton Aluminum Ratcheting Jack Stands. It does not handle itself low-level operations such as tensor products, convolutions and so on. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. axis: Position where to add a new axis. In order to do that I am trying to print the value of a tensor. But was it hard? With the whole session. function creates theano/tensorflow tensor functions which is later used to get the output from the symbolic graph given the input. 解决方法 将keras模型在django中应用时出现的小问题. 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. The behavior of tf. I made a simple piece of code to show what I mean. TensorFlow includes a special feature of image recognition and these images are stored in a specific folder. In this blog post, we'll demonstrate how to deploy a trained Keras (TensorFlow or MXNet backend) or TensorFlow model using Amazon SageMaker, taking advantage of Amazon SageMaker deployment capabilities, such as selecting the type and number of instances, performing A/B testing, and Auto Scaling. TensorFlow升级之后出现:在github中按照如下方式进行卸载重装:依然出现上述错误!!!大家有遇到这种情况每?. output]) Llamamos esta función con la imagen de entrada. Keras Tutorial, Keras Deep Learning, Keras Example, Keras Python, keras gpu, keras tensorflow, keras deep learning tutorial, Keras Neural network tutorial, Keras shared vision model, Keras sequential model, Keras Python tutorial. Let's take a way keras library. Conclusion and Further reading. get_session()。. Convolutional variational autoencoder with PyMC3 and Keras¶ In this document, I will show how autoencoding variational Bayes (AEVB) works in PyMC3's automatic differentiation variational inference (ADVI). feature_column. h5 模型文件,然后用 tensorflow 的 convert_variables_to_constants 函数将所有变量转换成常量,最后再 write_graph 就是一个包含了网络以及参数值的. GoogLeNet in Keras. serving or just tf) apply optimizations (freezing, quantitization etc) Theoretically you could even train as Keras Model, convert to tf. TensorFlow, CNTK, Theano, etc. sequence_categorical_column_with_vocabulary_list tf. 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. Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. There is also a pure-TensorFlow implementation of Keras with deeper integration on the roadmap for later this year. Posted on March 17, 2017 March 17, 2017 Deep Learning, GPU, Keras, TensorFlow Just upgraded Tensorflow 1. Keras backends What is a "backend"? Keras is a model-level library, providing high-level building blocks for developing deep learning models. You can vote up the examples you like or vote down the ones you don't like. feature_column. Convolutional Recurrent Neural Networks for Music Classification; License. It doesn't handle low-level operations such as tensor manipulation and differentiation. Oct 19, 2018. - 使用Lambda自定义Layer时,Lambda中使用Backend语法操作对象,操作的是Tensor - 当使用K. At most one component of shape can be -1. h5 模型文件,然后用 tensorflow 的 convert_variables_to_constants 函数将所有变量转换成常量,最后再 write_graph 就是一个包含了网络以及参数值的. These are some examples. So this talk will mix information about how to use the Keras API in TensorFlow and how the Keras API is implemented under the hood. Dynamically switch Keras backend in Jupyter notebooks Christos - Iraklis Tsatsoulis January 10, 2017 Keras 5 Comments Recently, I was looking for a way to dynamically switch Keras backend between Theano and TensorFlow while working with Jupyter notebooks; I thought that there must be a way to work with multiple Keras configuration files , but. optimizers import RMSprop import numpy as np import random def splitted_text (t): # Split text on spaces and remove whitespace and empty words. The same tensor x, unchanged. Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. returns a tensor of size. 5; osx-64 v2. Run the following code in your Python shell with Keras Python installed,. While the Keras FAQ suggests building partial models to achieve this, and this post suggests setting up functions to evaluate a layer, I was tempted to look into the backend's and TensorFlow's Print function to display the values of a tensor during the actual computation, as this creates an actual node in the computational graph, and prints the values as the model is used. backend import * from keras. 2。 captcha. I'm going to be talking about TensorFlow Keras. input_tensor: optional Keras tensor (i. axis: Position where to add a new axis. CHRYSLER OEM Front Seat-Cushion Cover-Top Back Left 1JL661J8AA,EBC Brakes S13KF1432 S13 Kits Yellowstuff and RK Rotors,NAJS3T 3 Ton Aluminum Ratcheting Jack Stands. Calculate the outer product/bilinear projection in Keras - outer_product_keras. datasets import mnist from keras import backend as K class Antirectifier(layers. backend APIs. Hand-Gesture Classification using Deep Convolution and Residual Neural Network (ResNet-50) with Tensorflow / Keras in Python January 20, 2018 February 14, 2018 / Sandipan Dey In this article, first an application of convolution net to classify a set of hand-sign images is going to be discussed. feature_column. Join GitHub today. Thus, using Keras as a simplified interface to Tensorflow is more or less a lie, at least if we want to use the graph definition + session execution. rnn( step_function, inputs, initial_states, go_backward TensorFlow函数教程:tf. Running on Power-8 Panther or Paragon. 케라스 튜토리얼 29 Jun 2018 | usage Keras. one_hot must be an integer tensor, but by default Keras passes around float tensors. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. It is made with focus of understanding deep learning techniques, such as creating layers for neural networks maintaining the concepts of shapes and mathematical details. import numpy as np import keras. 4 § Characteristics § "Simplified workflow for TensorFlow users. TensorFlow is an open-source software library. ResNet-50 training throughput (images per second) comparing Keras using the MXNet backend (green bars) to a native MXNet implementation (blue bars). TensorFlow, CNTK, Theano, etc. layers and the 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. Keras is a Python library that provides, in a simple way, the creation of a wide range of Deep Learning models using as backend other libraries such as TensorFlow, Theano or CNTK. No idea what the problem is. Given an input tensor, returns a new tensor with the same values as the input tensor with shape shape. batch_dot(c, h, [2, 2]) print(p2. axis: Position where to add a new axis. GoogLeNet paper: Going deeper with convolutions. And I work on the Keras team. If you have already built a model, you can use the model. Understand shape inference in deep learning technologies. backend to build functions that, provided with a valid input tensor, return the corresponding output tensor. But I only get back the following: Using TensorFlow backend. get_session()。. FRANCOIS CHOLLET: Hello, everyone. io/) is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. Session() `print. Let's try to re-implement the Logistic Regression Model using the keras. returns a tensor of size. Keras Backend. This is a useful tool when trying to understand what is going on inside the layers of a neural network. 케라스 소개 케라스는 거의 모든 종류의 딥러닝 모델을 간편하게 만들고 훈련시킬 수 있는 파이썬을 위한 딥러닝 프레임워크입니다. Otherwise the print operation is not taken into account during evaluation. It doesn't handle low-level operations such as tensor manipulation and differentiation. Note that this tutorial assumes that you have configured Keras to use the TensorFlow backend (instead of Theano). print_tensor( x, message= '') Note that print_tensor returns a new tensor identical to x which should be used in the following code. The keras package contains the following man pages: activation_relu application_densenet application_inception_resnet_v2 application_inception_v3 application_mobilenet application_mobilenet_v2 application_nasnet application_resnet50 application_vgg application_xception backend bidirectional callback_csv_logger callback_early_stopping callback_lambda callback_learning_rate_scheduler callback. Keras backend API. It depends on your input layer to use. This tutorial will demonstrate how you can reduce the size of your Keras model by 5 times with TensorFlow model optimization, which can be particularly important for deployment in resource-constraint environments. 解决方法 将keras模型在django中应用时出现的小问题. FRANCOIS CHOLLET: Hello, everyone. eval in your loss function because the tensors are not initialized. TensorFlow - Which one is better and which one should I learn? In the remainder of today's tutorial, I'll continue to discuss the Keras vs. A Neon one might be coming soon as well. output of `layers. 継承元: Dense 、 Layer tensorflow/python/keras/_impl/keras/layers/core. Purchase Order Number SELECT PORDNMBR [Order ID], * FROM PM10000 WITH(nolock) WHERE DEX_ROW_TS > '2019-05-01';. models import Sequential. Note that this behavior is specific to Keras dot. This tutorial assumes that you are slightly familiar convolutional neural networks. '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. layers should be always the same as tf. GoogLeNet paper: Going deeper with convolutions. shape minus last dimension => (1,2,3) concatenated with. Two of the top numerical platforms in Python that provide the basis for Deep Learning research and development are Theano and TensorFlow. This tutorial will show you how. They are extracted from open source Python projects. In particular, a shape of [-1] flattens into 1-D. The same tensor x, unchanged. Print(x,[x], 'x') The goal is to have the value '2' printed(the absolute value of -2). 我们也可以通过定义环境变量KERAS_BACKEND来覆盖上面配置文件中定义的后端:. Keras tensor with dtype dtype. returns a tensor of size. Keras runs training on top of TensorFlow backend. To use with "tensorflow/keras" it is necessary to convert the matrix into a Tensor (generalization of a vector), in this case we have to convert to 4D-Tensor, with dimensions of "n x 28 x 28 x 1", where: "n" is the "case number" "28 x 28" are the width and height of the image, and. k_print_tensor: Prints 'message' and the tensor value when evaluated. It is made with focus of understanding deep learning techniques, such as creating layers for neural networks maintaining the concepts of shapes and mathematical details. Conclusion and Further reading. models import Sequential from keras. Keras Backend. array([1, 2]) >>> K. It does not handle itself low-level operations such as tensor products, convolutions and so on. layers, no matter how I defined the input of the model. Package 'keras' October 8, 2019 Type Package Title R Interface to 'Keras' Version 2. Print(x,[x], 'x') The goal is to have the value '2' printed(the absolute value of -2). Variational auto-encoder for "Frey faces" using keras Oct 22, 2016 In this post, I'll demo variational auto-encoders [Kingma et al. 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. " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "xHxb-dlhMIzW" }, "source": [ "## Overview\n", "\n", "`tf. pyplot as plt from sklearn. from keras. Metrics, which can be used to monitor various important variables during the training of deep learning networks (such as accuracy or various losses), were somewhat unwieldy in TensorFlow 1. cast(x, dtype) Casts a tensor to a different dtype and returns it. utils import multi_gpu_model from keras. Theano can be used separately via the theano/0. where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively. feature_column. tensorflow_backend import _preprocess_conv3d_input, _preprocess_conv3d_kernel, _preprocess_border_mode, _postprocess_conv3d_output. shape(x) to get the shape of a tensor or use model. This release comes with a. Keras is a high-level python API which can be used to quickly build and train neural networks using either Tensorflow or Theano as back-end. Print() and. Note that print_tensor returns a new tensor identical to x which should be used in the following code. layers and the keras. rnn_TensorFlow官方文档_w3cschool 下载APP 随时随地学编程. Front Page DeepExplainer MNIST Example¶. So I think it might have some problem. py定義されています。. The behavior of tf. Metrics, which can be used to monitor various important variables during the training of deep learning networks (such as accuracy or various losses), were somewhat unwieldy in TensorFlow 1. They are used in a lot of more advanced use of Keras but I couldn’t find a simple explanation of what they mean inside Keras. cast keras. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. models import Sequential from keras import layers from keras. backend; Functions. layers, no matter how I defined the input of the model. I understand that you cannot do K. 这一环节我们使用 keras_to_tensorflow [2] 转换工具进行模型转换,其实这个工具原理很简单,首先用 Keras 读取. 如果你的模型包含这样的层,你需要指定你希望模型工作在什么模式下,通过Keras的backend你可以了解当前的工作模式: from keras import backend as K print K. backend APIs. import tensorflow as tf from keras import backend as K x = K. Also, I wonder which result is actually correct? using localhost Jupyter. A simple example showing how to explain an MNIST CNN trained using Keras with DeepExplainer. The example here is borrowed from Keras example, where convolutional variational autoencoder is applied to the MNIST dataset. eval in your loss function because the tensors are not initialized. The good news about Keras and TensorFlow is that you don't need to choose between them! The default backend for Keras is TensorFlow and Keras can be integrated seamlessly with TensorFlow workflows. Dataset API and the TFRecord format to load training data efficiently. import tensorflow as tf from keras import backend as K x = K. squeeze(x,axis) Removes a 1-dimension from the tensor at index "axis". Trying to run a simple code working with mnist datasets using keras with theano backend in python. 1; win-64 v2. backend; Functions. models import Sequential from keras. keras from tensorflow. Keras is a model-level library, providing high-level building blocks for developing deep learning models. feature_column. 解决方法 将keras模型在django中应用时出现的小问题. - 使用Lambda自定义Layer时,Lambda中使用Backend语法操作对象,操作的是Tensor - 当使用K. Front Page DeepExplainer MNIST Example¶. Install Keras with GPU TensorFlow as backend on Ubuntu 16. This is a useful tool when trying to understand what is going on inside the layers of a neural network. com uses the latest web technologies to bring you the best online experience possible. It is a reproduction of. Keras is a model-level library, providing high-level building blocks for developing deep learning models. tensorflow_backend for keras monkey patch for SELU - activations. 5; osx-64 v2. Strategy` is a. It was not Pythonic at all. In this blog post, we'll demonstrate how to deploy a trained Keras (TensorFlow or MXNet backend) or TensorFlow model using Amazon SageMaker, taking advantage of Amazon SageMaker deployment capabilities, such as selecting the type and number of instances, performing A/B testing, and Auto Scaling. The Keras can have the TensorFlow as its backend and has the Tensor board for its Visualization that can be used in Keras. Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. This tutorial assumes that you are slightly familiar convolutional neural networks. However, I have tried both K. From the official TensorFlow model optimization documentation. from __future__ import absolute_import, division, print_function import tensorflow as tf tf. Weight pruning means eliminating unnecessary values in weight tensors. Keras Tutorial About Keras Keras is a python deep learning library. Working with Keras is easy as working with Lego blocks. For a single GPU, the difference is about 15%. layers import Input, Dense >>> np_var = numpy. Keras is a Python library that provides, in a simple way, the creation of a wide range of Deep Learning models using as backend other libraries such as TensorFlow, Theano or CNTK. A backend is a computational engine — it builds the network graph/topology, runs the optimizers, and performs the actual number crunching. Put another way, you write Keras code using Python. is_keras_tensor (k_var) # A variable indirectly created outside of keras is not a Keras tensor. Keras is a popular programming framework for deep learning that simplifies the process of building deep learning applications. optimizers import RMSprop import numpy as np import random def splitted_text (t): # Split text on spaces and remove whitespace and empty words. Join GitHub today. Please ask usage questions on stackoverflow, slack, or the google group. TensorFlow, CNTK, Theano, etc. batch_dot(c, h, [2, 2]) print(p2. layers import LSTM from tensorflow. 如果你的模型包含这样的层,你需要指定你希望模型工作在什么模式下,通过Keras的backend你可以了解当前的工作模式: from keras import backend as K print K. input选用一层的输入张量为模型的输入张量. output]) Llamamos esta función con la imagen de entrada.