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Hi @isalirezag, you can get all configuration by using model.get_config() that will give you something like this: {'layers': [{'decoder_config': {'layers': [{'W_constraint': None, 'W_regularizer': None, 'activation': 'sigmoid', 'activity_regularizer': None, 'b_constraint': None, 'b_regularizer': None, 'cache_enabled': True, 'custom_name': 'dense', 'init': 'glorot_uniform', 'input_dim': None, 'input_shape': (860,), 'name': 'Dense', 'output_dim': 784, 'trainable': True}], 'name': 'Sequential'}, 'encoder_config': {'layers': [{'W_constraint': None, 'W_regularizer': None, 'activation': 'sigmoid', 'activity_regularizer': None, 'b_constraint': None, 'b_regularizer': None, 'cache_enabled': True, 'custom_name': 'dense', 'init': 'glorot_uniform', 'input_dim': None, 'input_shape': (784,), 'name': 'Dense', 'output_dim': 860, 'trainable': True}], 'name': 'Sequential'}, 'name': 'AutoEncoder', 'output_reconstruction': True}], 'loss': 'binary_crossentropy', 'name': 'Sequential', 'optimizer': {'epsilon': 1e-06, 'lr': 0.0010000000474974513, 'name': 'RMSprop', 'rho': 0.8999999761581421}, 'sample_weight_mode': None}. I would appreciate any suggestions and explanations even using some dummy example. if I'll use activation='tanh' I got slightly different error. SolveForum.com may not be responsible for the answers or solutions given to any question asked by the users. model.fit(X_train, X_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=1, validation_data=None), model.add(AutoEncoder(encoder=Dense(700, 600), Setup Is a potential juror protected for what they say during jury selection? Going by the pointer analogy, the name "encoder" simply points to the same set of layers as the first half of the name "autoencoder". Did Great Valley Products demonstrate full motion video on an Amiga streaming from a SCSI hard disk in 1990? But now I want to compar the result I have with this simple deep neural network to a deep network with stack auto encoder pre training. "Stacking" is to literally feed the output of one block to the input of the next block, so if you took this code, repeated it and linked outputs to inputs that would be a stacked autoencoder. The Autoencoder dataset is already split between 50000 images for training and 10000 for testing. I used hidden layer with 100 neurons and run keras version 0.3.0 on GPU. In this paper, we propose a pre-trained LSTM-based stacked autoencoder (LSTM-SAE) approach in an unsupervised learning fashion to replace the random weight initialization strategy adopted in deep . We didn't want decoder layers to lose information while trying to deconstructing the input. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. Autoencoder is also a kind of compression and reconstructing method with a neural network. What I wanted is to extract the hidden layer values. The linked blog post doesn't explain how to train the layers separately. In my opinion, why we can use decode_imgs = autoencoder.predict(x_test) is because we fitted it before the prediction. encoder1 = containers.Sequential([Dense(784, 700, activation='tanh'), Dense(700, 600, activation='tanh')]) Already on GitHub? When we defined autoencoder as autoencoder = Model(input_img, decoded), we simply name that sequence of layers that maps input_img to decoded as a "autoencoder". Phone (214) 824-6200. Keras Autoencoder 1 Autoencoder. Check it the blog for an example. (Sorry, I have not used Keras' AE before.) Building an Autoencoder Keras is a Python framework that makes building neural networks simpler. I'm trying to stack some auto encoders, but without success. Autoencoder is a neural network model that learns from the data to imitate the output based on the input data. self._read_eof() Valentin. For that I setup simple autoencoder code following keras documentation example (http://keras.io/layers/core/#autoencoder). from future import print_function Our community has been around for many years and pride ourselves on offering unbiased, critical discussion among people of all different backgrounds. File "/usr/lib/python2.7/gzip.py", line 261, in read A bit late.. but here's an example where each pair of layers are trained independently from @MadhumitaSushil #358 (comment), or mute MathJax reference. Google AdWords Remarketing; Yhteystiedot; hot and humid weather crossword Menu Menu AE3_output_reconstruction = True We are working every day to make sure solveforum is one of the best. (X_train, y_train), (X_test, y_test) = mnist.load_data(), #convert class vectors to binary class matrices Figure 4: The results of removing noise from MNIST images using a denoising autoencoder trained with Keras, TensorFlow, and Deep Learning. What is the use of NTP server when devices have accurate time? vanilla tensorflow ae autoencoder convolutional-autoencoder sparse-autoencoder stacked-autoencoder vanilla-autoencoder denoising-autoencoder regularized-autoencoder autoencoder-models. All Answers or responses are user generated answers and we do not have proof of its validity or correctness. File "/usr/lib/python2.7/gzip.py", line 455, in readline Posted on November 4, 2022 by November 4, 2022 by TypeError: init() got an unexpected keyword argument 'tie_weights'. That will make some inputs and encoded outputs zero. @Nidhi1211 : I suggest you learn how to read stack traces. Unfortunately, I don't think keras has a good visualization functionality. Questions labeled as solved may be solved or may not be solved depending on the type of question and the date posted for some posts may be scheduled to be deleted periodically. You are using an out of date browser. Is there any alternative way to eliminate CO2 buildup than by breathing or even an alternative to cellular respiration that don't produce CO2? Y_train = np_utils.to_categorical(y_train, nb_classes) ae1.compile(loss='mean_squared_error', optimizer=RMSprop()) The first stack trace is clearly not the same as the second. here is some hint: Hi @dibenedetto, I didn't know that I would have to recompile, but it did the trick. 14. FirstAeOutput = ae1.predict(X_train), #second autoencoder . I'm reading an article (thesis of LISA labs) about different method to train deep neural networks. Simple autoencoder: from keras.layers import Input, Dense from keras.models import Model import keras # this is the size of . To build an autoencoder, you need three things: an encoding function, a decoding function, and a distance function between the amount of information loss between the compressed representation of your data and the decompressed representation (i.e. By clicking Sign up for GitHub, you agree to our terms of service and First, let's install Keras using pip: $ pip install keras Preprocessing Data Again, we'll be using the LFW dataset. Autoencoder is a kind of unsupervised learning structure that owns three layers: input layer, hidden layer, and output layer as shown in Figure 1. @Nidhi1211 : This is unrelated. Sign in Thank you, P.S. Have a question about this project? File "/home/nidhi/Documents/project/SAE.py", line 40, in Simple Autoencoder Example with Keras in Python. if I'll use activation='tanh' I got slightly different error: ValueError: GpuElemwise. The stacked autoencoders are, as the name suggests, multiple encoders stacked on top of one another. No difference between MNIST and any other dataset. An autoencoder with tied weights has decoder weights that are the transpose of the encoder weights; this is a form of parameter sharing, which reduces the number of parameters of the model . model.add(ae2[0].encoder) the thread encoder3 = containers.Sequential([Dense(400, 300, activation='tanh'), Dense(300, 200, activation='tanh')]) from keras.models import Sequential model.add(Dense(200, 10)) Convolution layers along with max-pooling layers, convert the input from wide (a 28 x 28 image) and thin (a single channel or gray scale) to small (7 x 7 image at the . You can use the predict () function from the Model () class in tensorflow.keras.models. Sign up for a free GitHub account to open an issue and contact its maintainers and the community. ae3.add(AutoEncoder(encoder=encoder3, decoder=decoder3, And add new layers (both decode and encoder) train them. https://blog.keras.io/building-autoencoders-in-keras.html. batch_size . You signed in with another tab or window. Similarly, when you run encoder = Model(input_img, encoded), you are only naming the sequence of layers that maps input_img to encoded. But one thing I am not sure is if I am reusing encoder weight correctly, because the output before fine turning is almost same as no training at all. model.add(Activation('tanh')), model.compile(loss='mean_squared_error', optimizer=rms) pre trained autoencoder keras 05 82 83 98 10. how to enchant books in hypixel skyblock new update. Here is my code: @dchevitarese you are trying to fit your second autoencoder with an input with size 784, while it expects one of 500. show_accuracy=False, verbose=1), #creating the Deep neural network with all encoder of each autoencoder trained before, model = Sequential() Additionally, you can see the bolg from Francois Chollet if you want to build antoencoder with keras. Have a question about this project? The code should still work but I have not tested with TensorFlow 1.12. Sorted by: 2. print(X_train.shape[0], 'train samples') If I don't misunderstood the method for training Deep neural network with autoencoder, the first step is to train one by one each autoencoder to encode and decode their input. File "/usr/lib/python2.7/gzip.py", line 347, in _read_eof As the tittle said, I'm trying to train deep neural network with stack autoencoder but I'm stuck from keras.callbacks import ModelCheckpoint, batch_size = 10000 decoder1 = containers.Sequential([Dense(600, 700, activation='tanh'), Dense(700, 784, activation='tanh')]) The process of an autoencoder training consists of two parts: encoder and decoder. output_reconstruction=False, tie_weights=True)) Space - falling faster than light? Advanced Deep Learning Python Structured Data Technique Time Series Forecasting. from keras.utils import np_utils On Jun 9, 2016 2:56 AM, "lurker" notifications@github.com wrote: I just wanna know if the AutoEncoder has been removed from the newest I start with this code but I don't know how I can continue and everytime I try to add code I have an error so this is my valid code : from future import absolute_import Creating the Autoencoder: I recommend using Google Colab to run and train the Autoencoder model. How to build Stacked Autoencoder using Keras? The second is also mentioned above if you spend a few seconds to read the context. Hi, Deep neural network with stacked autoencoder on MNIST, # the data, shuffled and split between train and test sets, # convert class vectors to binary class matrices, 'Training the layer {}: Input {} -> Output {}', # Store trainined weight and update training data, # from https://github.com/fchollet/keras/issues/358, "Autoencoder data format: {0} - should be (60000, 500)". ae2.add(AutoEncoder(encoder=encoder2, decoder=decoder2, #third autoencoder show_accuracy=False, verbose=1, validation_data=[X_test, X_test]), #getting output of the first autoencoder to connect to the input of the Collection of autoencoder models in Tensorflow. Suggula Jagadeesh Published On October 29, 2020 and Last Modified On August 25th, 2022. The best answers are voted up and rise to the top, Start here for a quick overview of the site, Detailed answers to any questions you might have, Discuss the workings and policies of this site, Learn more about Stack Overflow the company. (which is direct use of example in documentation http://keras.io/layers/core/#autoencoder). Electronics. We have tried adding it in few different ways: Add only after input layer. You can normally directly start training the network. role of e-commerce in improving customers satisfaction pre trained autoencoder keras. But I got the following error when I used this option in my model: Please note, that my data X is a dataset without labels, I used 10000 as a batch size and my dataset has 301 features. output_reconstruction=False, tie_weights=True)) to your account, firts of all sorry for my english, it's not my native language (I'm french). We clear the graph in the notebook using the following commands so that we can build a fresh graph that does not carry over any of the memory from the previous session or graph: tf.reset_default_graph() keras.backend.clear_session() SDAE, How to train stacked auto-encoder with greedy layer-wise way. ae3 = Sequential() In the end, I got ~91% of accuracy. why using output_reconstructions=False gives dimension mismatch Input 2 (indices start at 0) has shape[1] == 301, but the output's size on that axis is 100. @mthrok : yes you can stack the layers like that, but it is not doing greedy layerwise training. output_reconstruction=AE2_output_reconstruction, tie_weights=True)), #training the second autoencoder dominaria united card kingdom. Use MathJax to format equations. Then we build a model for autoencoders in Keras library. In the Let's build the simplest possible autoencoder section, the author provided a demo: questions: bell and howell solar lights - qvc Become a Partner. It needs to be checked though. model.add(ae1[0].encoder) It works fine individually but I don't know how to combine all the encoder parts for classification. And that's what I don't find the way to do it. ae2.fit(FirstAeOutput, FirstAeOutput, batch_size=batch_size, nb_epoch=nb_epoch, Already on GitHub? pre trained autoencoder keras. Stacked autoencoder in Keras Now let's build the same autoencoder in Keras. It looks like, I didn't put activation function. This article was published as a part of the . Hey, guys, I am also working on how to layer-by-layer train AE, and I'm new to Keras. Actually I also have an idea, but I think it is a very naive idea. But if your goal is to train a network, then keep in mind that by applying glorot initialization (which is default initialization scheme in Keras), you don't need to do pre-training. On the left we have the original MNIST digits that we added noise to while on the right we have the output of the denoising autoencoder we can clearly see that the denoising autoencoder was able to recover the original signal (i.e., digit) from the . model.add(Activation('tanh')). Sign in Every layer is trained as a denoising autoencoder via minimising the cross entropy in . Image by author According to the architecture shown in the figure above, the input data is first given to autoencoder 1. I'm not sure what you mean by "map the data". is there any function available for building stacked auto-encoder in keras library? is there any function available for building stacked auto-encoder in keras library? ae.add(AutoEncoder(encoder=encoder, decoder=decoder, @fchollet 's blog : Building Autoencoders in Keras. If your goal is to do experiment with pre-trainng, you are doing it right. Why such a big difference in number between training error and validation error? Thanks for contributing an answer to Data Science Stack Exchange! The encoder was created with the instruction, "Let's also create a separate encoder model:". File "/usr/lib/python2.7/gzip.py", line 308, in _read My idea is that each time train two layer (encode and decode) then freeze them. ae2 = Sequential() np.random.seed(1337) # for reproducibility, from keras.datasets import mnist But how well did the autoencoder do at reconstructing the training data? why using output_reconstruction=True flags works and False value does not? I can't test this code right now cause I haven't my laptop with me but I'll try it tonight. decoder3 = containers.Sequential([Dense(200, 300, activation='tanh'), Dense(300, 400, activation='tanh')]) model.add(ae3[0].encoder) Stacked autoencoder in Keras Now let's build the same autoencoder in Keras. Reply to this email directly, view it on GitHub Thanks, Simple Neural Network is feed-forward wherein info information ventures just in one direction.i.e. Thanks for your help. (clarification of a documentary). So when you run autoencoder.fit(x_train, x_train,, the "encoder" layers are being trained. Order Now ae1.fit(X_train, X_train, batch_size=batch_size, nb_epoch=nb_epoch, Is it possible for a gas fired boiler to consume more energy when heating intermitently versus having heating at all times? I have tried to create a stacked autoencoder using Keras but I couldn't do the last part of this autoencoder. thanks to fchollet's exemple I managed to implement a simple deep neural network that is work thinks to ReLU activation function (Xavier Glorot thesis). Valentin. verbose = 2, shuffle=shuffle, show_accuracy = False). Mohana Asks: How to build Stacked Autoencoder using Keras? Now that we understand conceptually how Variational Autoencoders work, let's get our hands dirty and build a Variational Autoencoder with Keras! Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. from keras.optimizers import SGD, Adam, RMSprop, Adagrad, Adadelta But their dimension is the same as my input one. SecondAeOutput = ae2.predict(FirstAeOutput), #third autoencoder Traceback (most recent call last): I actually did that. the information passes from input layers to hidden layers finally to . from keras.datasets import mnist from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation, AutoEncoder, Layer from keras.optimizers import SGD, Adam, RMSprop, Adagrad, Adadelta from keras.utils import np_utils from keras.utils.dot_utils import Grapher from keras.callbacks import ModelCheckpoint. X_test = X_test.astype("float64") data = six.moves.cPickle.load(f) Traceback (most recent call last): reaumur scale pronunciation; art textbooks for high school; perfumed hair dressing crossword clue; bonobo essential mix tracklist 2022 It has been removed. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Updated on Nov 30, 2019. If no, does some offer some ideas for that. Each layer's input is from previous layer's output. import numpy as np Well occasionally send you account related emails. Did Twitter Charge $15,000 For Account Verification? Deep Learning with TensorFlow and Keras, Third Edition: Build and deploy supervised, unsupervised, deep, and reinforcement learning models; Free Chapter. Hakukoneoptimointi; Hakukonemainonta. 1 Answer. from keras.layers.core import Dense, Dropout, Activation, AutoEncoder, Layer I have no idea why I cannot import AutoEncoder and containers (Even if I reinstalled theano and Keras). Making statements based on opinion; back them up with references or personal experience. Keras is accessible through this import: X_train = X_train.astype("float64") In this tutorial, you will learn how to build a stacked autoencoder to reconstruct an image. ae.add(AutoEncoder(encoder=encoder,decoder=decoder,output_reconstruction=False,tie_weights=True)) How to Build an Autoencoder with TensorFlow. Does a beard adversely affect playing the violin or viola? I will look into it later. Plotting three lines on the same plot (with 4-hour frequency). import keras from keras import layers from keras.layers import Input, Dense input_size = 2304 hidden_size = 64 output_size = 2304 input_img = keras.Input (shape= (input_size,)) #autoencoder1 encoded . Why are taxiway and runway centerline lights off center? Name for phenomenon in which attempting to solve a problem locally can seemingly fail because they absorb the problem from elsewhere? How can we describe the class of trajectories around a point mass in general relativity? Stacked Autoencoders. @xypan1232 : You will have to write your own extend Layer and write your own autoencoder. nb_epoch = 1, adg = Adagrad() Already on GitHub? It can only represent a data-specific and a lossy version of the trained data. Encoder is used for mapping the input data into hidden representation, and decoder is referred . Stop requiring only one assertion per unit test: Multiple assertions are fine, Going from engineer to entrepreneur takes more than just good code (Ep. bdtechnobyte@gmail.com. thank you very much for your fast reply it's very apreciable. So if i right, my goal is to train a second autoencoder with inputs of the firs autoencoder. It is mainly because of the "fit" function. Cross entropy is for classification (ie you need classes). extraocular muscles of eye nerve supply | game show climax often crossword clue la times | 954.237.4587 | 954.237.4587 Stack Exchange network consists of 182 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers. It will be closed after 30 days if no further activity occurs, but feel free to re-open a closed issue if needed. to your account. https://blog.keras.io/building-autoencoders-in-keras.html. Get the predictions. ae1 = Sequential() Please vote for the answer that helped you in order to help others find out which is the most helpful answer. Have a question about this project? We can build deep autoencoders by stacking many layers of both encoder and decoder; such an autoencoder is called a stacked autoencoder. Introduction to neural networks; Perceptron; Multi-layer perceptron - our first example of a network; A real example - recognizing handwritten digits; Regularization; Playing with Google Colab - CPUs, GPUs, and TPUs; Sentiment analysis; Hyperparameter tuning and AutoML . print('Test score:', score[0]) The following would work even for output_reconstruction - False, model.fit(X, X, nb_epoch = epochs, batch_size = batch_size, November 4, 2022 dell p2422h monitor driver dell p2422h monitor driver from keras.layers.core import Dense, Dropout, Activation, AutoEncoder, Layer Input dimension mis-match. It allows us to stack layers of different types to create a deep neural network - which we will do to build an autoencoder. Rather than use digits, we're going to use the Fashion MNIST dataset, which has 28-by-28 grayscale images of different clothing items 5. is there any function available for building stacked auto-encoder in keras library? Connect and share knowledge within a single location that is structured and easy to search. privacy statement. Y_test = np_utils.to_categorical(y_test, nb_classes), model.add(AutoEncoder(encoder=Dense(784, 700), @jf003320018 I'm confused. Here I have created three autoencoders. from keras.callbacks import ModelCheckpoint Does anyone have any sample code to visualize the layers and output please? Do not hesitate to share your thoughts here to help others. privacy statement. from keras.models import Sequential and the document also has no tie_weights parameter for autoencoder :http://keras.io/layers/core/#autoencoder This however might not work, since the documentation says that when you load saved weight with load_weight function, the architecture of model must be identical. Tensorflow 2.0 has Keras built-in as its high-level API. I also don't see any other issue addressing this. encoder2 = containers.Sequential([Dense(600, 500, activation='tanh'), Dense(500, 400, activation='tanh')]) This may be more correct architecture. How to help a student who has internalized mistakes? If no, does some offer some ideas for that. Stacked Autoencoder. To read up about the stacked denoising autoencoder, check the following paper: Vincent, Pascal, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. you're right, the validation_data parameter I used cause this failure. Autoencoders are purely MSE based. AE2_output_reconstruction = False @Bjoux2 Ok I understand your doubt. Concealing One's Identity from the Public When Purchasing a Home. You signed in with another tab or window. X_train /= 255 Actually I also have an idea, but I think it is a very naive idea. AE2_output_reconstruction = True What is the difference between an "odor-free" bully stick vs a "regular" bully stick? TypeError: init() got an unexpected keyword argument 'tie_weights'. But perhaps with your code I'm going to succeed. A Stacked Autoencoder is a multi-layer neural network which consists of Autoencoders in each layer. Here I have created three autoencoders. JavaScript is disabled. Stacked autoencoder in Keras Now let's build the same autoencoder in Keras. My idea is that each time train two layer (encode and decode) then freeze them. score = model.evaluate(X_test, Y_test, show_accuracy=True, verbose=0) By clicking Sign up for GitHub, you agree to our terms of service and Note We clear the graph in the notebook using the following commands so that we can build a fresh graph that does not carry over any of the memory from the previous session or graph: tf.reset_default_graph ()keras.backend.clear_session () AE1_output_reconstruction = True show_accuracy=False, verbose=1), #getting output of the second autoencoder to connect to the input of the It works fine individually but I don't know how to combine all the encoder parts for classification. Camera & Accessories to your account. a "loss" function). Substituting black beans for ground beef in a meat pie, Typeset a chain of fiber bundles with a known largest total space. What are the most important changes in TensorFlow 2.0? You are receiving this because you are subscribed to this thread. @jf003320018 You may misunderstand my meaning. Actually I also have an idea, but I think it is a very naive idea. ae2.compile(loss='mean_squared_error', optimizer=RMSprop()) For a better experience, please enable JavaScript in your browser before proceeding. As a matter of fact, it certainly changes the output I have tried to create a stacked autoencoder using Keras but I couldn't do the last part of this autoencoder. Am I wrong on this statement, if so can someone explain the reason. from keras.optimizers import SGD, Adam, RMSprop, Adagrad, Adadelta Then I can apply a simple SGD. How can i fix it, does it means my keras is in older version? I try do something like that to do greedy layerwise but it's not working from keras.datasets import mnist validation_data=None, X_test /= 255 We clear the graph in the notebook using the following commands so that we can build a fresh graph that does not carry over any of the memory from the previous session or graph: tf.reset_default_graph () keras.backend.clear_session () thanks, so what can we do if i want to use tie_weights? @voletiv thanks for your reply. rms = RMSprop(), (X_train, y_train), (X_test, y_test) = mnist.load_data(), X_train = X_train.reshape(60000, 784) ae2.compile(loss='mean_squared_error', optimizer=RMSprop()) AE1_output_reconstruction = False #set output_reconstruction at False to get the hidden layer of autoencoder Thanks in advance! decoder2 = containers.Sequential([Dense(400, 500, activation='tanh'), Dense(500, 600, activation='tanh')]) @mthrok Thanks for your help and your code! You will use the CIFAR-10 dataset which contains 60000 3232 color images. If no, does some offer some ideas for that. Thanks. I could use a CNN to do the same job, but I am investigating this AE's to pre-train layers - and this also explains my next question: What do you mean with "take care of what data are you dealing with"? . However, the result is not good. Asking for help, clarification, or responding to other answers. sgd = SGD() This issue has been automatically marked as stale because it has not had recent activity. Visit Stack Exchange. It is because you ask the "fit" function to do validation as well. If I get it right, you want to sneak on the innermost layer, so take care of what data are you dealing with. Here is a layer-by-layer example. Stacked Autoencoder I have tried to create a stacked autoencoder using Keras but I couldn't do the last part of this autoencoder. from keras.utils.dot_utils import Grapher Then we build a model for autoencoders in Keras library. hidden layer. LSTM autoencoder is an encoder that makes use of LSTM encoder-decoder architecture to compress data using an encoder and decode it to retain original structure using a decoder. But when i use parameter tie_weights How does surface tension allow the surface of a liquid to exert an upward force on an object? Well occasionally send you account related emails. Here I have created three autoencoders. Well occasionally send you account related emails. That will make some inputs zero. . This is because weight tying has been removed. Hi all, model.add(Activation('softmax')), model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=2, validation_data=(X_test, Y_test))
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