Thank you Jason, great post. This is phenomenal, but I get a bit confused (my python is quite weak) as I’m following this along with other tutorials and most people tend to do something like “xyz = model.fit(trainx, trainy, batch_size=batch_size, epochs=iterations, verbose=1, validation_data=(testx, testy),” Bidirectional LSTMs are supported in Keras via the Bidirectional layer wrapper. I’ve read probably 50 of your blog articles! import scipy.io.wavfile as wav 1) can we train with all data inside each epoch? Layer 1: An embedding layer of a vector size of 100 and a max length of each sentence is set to 56. Great Post! Like this? What clues might I look for to determine if over-fitting is happening? I have a sequence classification problem, where the length of the input sequence may vary! data = tf.placeholder(tf.float32, [None, MAX_STEPS,26]) #Number of examples, number of input, dimension of each input This problem is quite different from the example you give. if(guess_class==true_class): (test_rate,test_sig) = wav.read(‘/home/lxuser/test_data/’+word+’/’+files[filecount]) Thanks for sharing. What do you think about Bi-Directional LSTM models for sentiment analysis, like classify labels as positive, negative and neutral? model.add( test_output.append(temp_list) Good question, this will help with the general number of layers and number of nodes/units: test_input.append(test_padded_array) ))), Not sure how to define the input shape, since it is the output of 3DCNN pooling layer. Is bidirectional lstm and bidirectional rnn one and the same? model.add(Bidirectional(LSTM(20, return_sequences=True), input_shape=( ? deviation_list=[], def create_test_data(word): A typical example of time series data is stock market data where stock prices change with time. train_input.append(train_padded_array) Hi Jason, I have a question. 1.- May Bidirectional() work in a regression model without TimeDistributed() wrapper? I am sure – you will point my mistake quickly . I am struggling with a particular concept for sequence classification. the website to the project is https://github.com/brunnergino/JamBot.git. The use of providing the sequence bi-directionally was initially justified in the domain of speech recognition because there is evidence that the context of the whole utterance is used to interpret what is being said rather than a linear interpretation. The predictions will be then compared to the expected output sequence to provide a concrete example of the skill of the system. Line Plot to Compare Merge Modes for Bidirectional LSTMs. Is it possible to share your code? We can compare the behavior of different merge modes by updating the example from the previous section as follows: Running the example will create a line plot comparing the log loss of each merge mode. I have a long list of general ideas here: I think the answer to my problem is pretty simple but I’m getting confused somewhere. https://machinelearningmastery.com/lstms-with-python/. Simply the data is split into middle (=frames inside a spoken word)and ending(=frames at word boundary) I have a question on how to output each timestep of a sequence using LSTM. for i in range(1000000): In this case, we can see that perhaps a sum (blue) and concatenation (red) merge mode may result in better performance, or at least lower log loss. You can pad with zeros and use a Mask to ignore the zero values. Once trained, the network will be evaluated on yet another random sequence. Samples are sequences. Sorry for frequent replies. hi Jason, thanks greatly for your work. Layer 2: 128 cell bi-directional LSTM layers, where the embedding data is fed to the network.We add a dropout of 0.2 this is used to prevent overfitting. This sequence is taken as input for the problem with each number provided one per timestep. Perhaps you can combine the sentences from one document into a single sequence of words? Click to sign-up and also get a free PDF Ebook version of the course. It’s very helpful for me. for i in WORD_LIST: i saw the tensorflow develop the GridLSTM.can link it into keras? This is so that we can graph the log loss from each model configuration and compare them. (train_rate,train_sig) = wav.read(‘/home/lxuser/train_dic/’+word+’/’+files[int(math.floor(j))]) test_result_i=sess.run(prediction,{data:[test_input[test_count]]}) – especially love the test LSTM vanilla vs. LSTM reversed vs. LSTM bidirectional. LSTM layer does not have cell argument. files = os.listdir(‘/home/lxuser/train_dic/’+word) Hi Bastian, print(‘starting fresh model’) I also have information which says that word ‘I’ appear in interval [20-30], am in [50-70] , ‘a’ in [85-90] and ‘person’ in [115-165] timsteps. Not really needed, see this post: Thanks a lot in advance. This tutorial assumes you have Keras (v2.0.4+) installed with either the TensorFlow (v1.1.0+) or Theano (v0.9+) backend. false_count+=1 np.random.shuffle(train_input) Also try larger batch sizes. tf version :2.1.0 for j in range(no_of_batches): How can we base our understanding of what we’ve heard on something that hasn’t been said yet? Also, I have a ton on them already, start here: | ACN: 626 223 336. model.add(Masking(mask_value= 0,input_shape=(maxlen,feature_dim))) Any tips or tutorial on this matter will be super appreciated. length_of_folder=len(files) A new random input sequence will be generated each epoch for the network to be fit on. model.add( MAX_STEPS=11 for i in range(int(length_of_folder/interval)): sess.run(init_op) The input to LSTMs is 3d with the form [samples, time steps, features]. #saver.restore(sess, “./1840frames-example-two-class-ten-from-each-model-2870.ckpt”) Do you have a suggestion for dealing with very long sequences after masking for classification? [True, True, True]. Each time step is processed one at a time by the model. tf.keras: 2.2.4-tf My problem is 0-1 classification. 2) or at each epoch , I should select only a single sample of my data to fit and this implies that the number of samples=no. Same goes for prediction. Read more. Features are things measured at each time step. deviation_list.append(-1) 3.- Does Bidirectional() requires more input data to train? Do you think recurrent networks, being good at classifying time series, would be a better solution? So is it not really worth it for this task? interval=INTERVAL import re, WORD_LIST=[‘middle’,’ending’] hidden dimension 100, 4 layers, and are bidirectional. Thank you Jason! x(0) -> x(1) -> … -> x(N-1). My data are all 3D, including labels and input. We will compare three different models; specifically: This comparison will help to show that bidirectional LSTMs can in fact add something more than simply reversing the input sequence. Do you have any questions? Especially, samples are the total sentences, and what are the timesteps and features? shuffletrain() What is the best practice to slow down the overfitting? I used tf.map_fn() to map whole batch to bilstm_layers. If yes, how? One day might be one sequence and be comprised of lots of time steps for lots of features. temp_list[index]=1 Perhaps try it on your dataset and compare to other methods? Therefore, we can reshape the sequences as follows. I have been trying to find multi step predictions and i know you have a blog post that does it using stateful = True but i cant seem to use bidrectional with it and limited by batch size needing to be a multiple of training size. A binary label (0 or 1) is associated with each input. You can train the model with the same dataset on each epoch, the chosen problem was just a demonstration. A bidirectional LSTM is a bidirectional RNN. But from your above lost plot, it shows it does help. https://machinelearningmastery.com/develop-word-embedding-model-predicting-movie-review-sentiment/, And here: I am working on a problem of Automatic Essay Grading in which there’s an extra dimension which is number of sentences in each essay. The predictions for a new random sequence are compared to the expected values, showing a mostly correct result with a single error. import tensorflow as tf Nevertheless, run some experiments and try bidirectional. df = concat(columns, axis=1) The input layer will have 10 timesteps with 1 feature a piece, input_shape=(10, 1). decoder_input = ks.layers.Input(shape=(85,)), encoder_inputs = Embedding(lenpinyin, 64, input_length=85, mask_zero=True)(encoder_input), encoder = Bidirectional(LSTM(400, return_sequences=True), merge_mode=’concat’)(encoder_inputs), encoder_outputs, forward_h, forward_c, backward_h, backward_c = Bidirectional(LSTM(400, return_sequences=True, return_state=True), merge_mode=’concat’)(encoder), decoder_inputs = Embedding(lentext, 64, input_length=85, mask_zero=True)(decoder_input), decoder = Bidirectional(LSTM(400, return_sequences=True), merge_mode=’concat’)(decoder_inputs, initial_state=[forward_h, forward_c, backward_h, backward_c]), decoder_outputs, _, _, _, _ = Bidirectional(LSTM(400, return_sequences=True, return_state=True), merge_mode=’concat’)(decoder), decoder_outputs = TimeDistributed(Dense(lentext, activation=”softmax”))(decoder_outputs), I have an example here that might help: Here are some general ideas to try: Time steps are lag obs. What we must remember is the distinction between tasks that are truly online – requiring an output after every input – and those where outputs are only needed at the end of some input segment. I get 100s of similar requests via email each day. import math imdb_cnn: Demonstrates the use of Convolution1D for text classification. In the second option, it can be used for online prediction tasks, where future inputs are unknown. init_op = tf.initialize_all_variables() It depends what the model expects to receive as input in order to make a prediction. A simple reverse of the matrix would change the exposed column, and advertisement that the household would be exposed to, so we should be reversing the matrix along the time series axis (dim=1 ). Can we use Bidirectional LSTM model for program language modeling to generate code predictions or suggestions? Suppose I have a list of customer feedback sentences and want to use unsupervised training to group them by their nature (a customer complaint about a certain feature vs. question they ask vs. a general comment, etc.). Sounds, words, and even whole sentences that at first mean nothing are found to make sense in the light of future context. (The values lost from the truncation). of epochs? I am hoping that silence between two words will be learnt as class ‘ending’. BATCH_SIZE=500 Facebook | So clearly I need to loop this batch over dimension 16 somehow. Thank you so much. Could you kind with me explaining how to build such model and train it in keras. Thanks though for the tutorial. Here neural network makes decision from 11 time steps each having 26 values. Bidirectional LSTM For Sequence Classification, LSTM with reversed input sequences (e.g. model.compile(optimizer=’adam’, loss=’mse’). j=0, def make_train_data(word): input_shape=sample_shape i want to use a 2D LSTM (the same as gridlstm or multi diagonal LSTM) after CNN,the input is image with 3D RGB (W * H * D) I struggle with a similar problem, also trying to predict with a bidirectional LSTM and getting nearly 100% accuracy on training, but nonsense output on prediction. from keras.preprocessing.sequence import pad_sequences from keras.layers import Dense, LSTM, Reshape, BatchNormalization, Input, Conv2D from keras.layers import MaxPool2D, Lambda, Bidirectional from keras.models import Model from keras.activations import relu, sigmoid, softmax import keras.backend as K from keras.utils import to_categorical First a traditional LSTM is created and fit and the log loss values plot. Hi! PS: interesting idea from Francois Chollet for NLP: 1D-CNN + LSTM Bidirectional for text classification where word order matters (otherwise no LSTM needed). series input: x[t] with t=[0..n] a complete measurement/simulation. predicted_position=920 Hi Jason, thanks for a very clear and informative post. Is this the correct thought process behind this, and how would you do this? for i in range(int(length_of_folder/interval)): value=int(re.search(r’\d+’,files[filecount]).group()) Deviation is simply for stats of result. Time series forecasting refers to the type of problems where we have to predict an outcome based on time dependent inputs. filecount=int(math.floor(j)) I really wouldn’t want to arbitrarily cut my sequences or pad them with a lot of unnecessary “zeros”. sir , i need your expert opinion on this. In this case, how do x connects to U? true_class=np.argmax(test_output[test_count]) from python_speech_features import mfcc Performance on the train set is good and performance on the test set is bad. https://machinelearningmastery.com/start-here/#nlp. That means that instead of the TimeDistributed layer receiving 10 timesteps of 20 outputs, it will now receive 10 timesteps of 40 (20 units + 20 units) outputs. hi Jason, I'm Jason Brownlee PhD Is there any benefit of it? This ensures that the model does not memorize a single sequence and instead can generalize a solution to solve all possible random input sequences for this problem. If you can apply an LSTM, then you can apply a bidirectional LSTM, not much difference in terms of input data. Is there a glitch in Bidirectional keras wrapper when masking padded inputs that it doesn’t compute masks in one of the directions? To be clear, timesteps in the input sequence are still processed one at a time, it is just the network steps through the input sequence in both directions at the same time. I think tf updated something recently in this maybe. Do you think bidirectional LSTMs can be used for time series prediciton problems? for i in TEST_WORD_LIST: Hi Jason, I understand and thank you very much for all your help. int_class = WORD_LIST.index(word) if word in WORD_LIST else -1 Each input is passed through all units in the layer at the same time. Hi jason, [code] This post will help as a first step: There are many repetitive patterns in the extracted features of the bird sounds. train_output=[] It seems that the neural network is classifying everything as ‘middle’ . Try it and see. Good stuff it clearly explains how to use bidirectional lstm. I was stuck for an hour at the last assignment, could not figure out the Bidirectional LSTM, came to your tutorial and it all made it clear for me. This post is really helpful. Setup. Currently i am casting it into binary classification. I really appreciate your clear and understandable summary. model.add(TimeDistributed(Dense(1, activation=’sigmoid’))) The bidir model looks at the data twice, forwards and backwards (two perspectives) and gets more of a chance to interpret it. mfcc allows – successive 25 ms windows with overlap of 15ms by default so we can get 13 or 26 mfcc coefficients at each time step. Excellent post! # Only consider the first 200 words of each movie review, # Input for variable-length sequences of integers, # Embed each integer in a 128-dimensional vector, _________________________________________________________________, =================================================================, Load the IMDB movie review sentiment data. Am I correct that using BiLSTM in this scenario is some sort of “cheating”, because by also using the features of the future, I basically know whether he crashed into this obstacle _i because I can look at the feature “did the user crash into the last obstacle” right after this obstacle _i! … relying on knowledge of the future seems at first sight to violate causality. But it is not working. ptr = 0 You could make a forecast per sentence. I think , I am doing something basic wrong here. Discover how in my new Ebook: I think it needs to be different, but I cannot figure out how despite hours of searching. fine_tuning: Fine tuning of a image classification model. This is not apparent from looking at the skill of the model at the end of the run, but instead, the skill of the model over time. Hi, is your code example fit for a multiclass multi label opinion mining classification problem ? Thank you for this blog . I would recommend trying many different framings of the problem, many different preparations of the data and many different modeling algorithms in order to _discover_ what works best for your specific problem. Thanks a lot for the blog! no_of_batches = int(len(train_input)) / batch_size ), since they are irrelevant from the reverse order? Quick question – I have an online marketing 3d dataset (household * day * online advertisements) and from this dataset, we train for each household — so a 2d Matrix with a row for each day and column for each potential advertisement. So far , I have considered of splitting wav file into sequence of overlapping windows. You could use an autoencoder for the entire document. rng_state = np.random.get_state() Bidirectional wrapper for RNNs. imdb_cnn_lstm: Trains a convolutional stack followed by a recurrent stack network on the IMDB sentiment classification task. Thank you. one sequence), a configurable number of timesteps, and one feature per timestep. I have a question in your above example. padding wasn’t masked in the BiDirectional wrapper althought specifying mask_value=0 in embed layer before: print([layer.supports_masking for layer in model.layers]) Consider dropout and other forms of regularization. I just want to say thank you, thank you for your dedication. bias = tf.Variable(tf.constant(0.1, shape=[target.get_shape()[1]])) There are a lot of research papers that use simple LSTM models for this, but there are barley no for BiLSTM models (mainly speech recognition). tf.keras version: 2.4.0, print([layer.supports_masking for layer in model.layers]) sir, can bidirectional lstm be used for sequence or time series forecasting? j=j+interval This can provide additional context to the network and result in faster and even fuller learning on the problem. Though I have given 38k samples of both classes in training folder. I too came to the conclusion that a bidirectional LSTM cannot be used that way. I’m eager to help, but I don’t have the capacity to review code. The LSTM (Long Short Term Memory) is a special type of Recurrent Neural Network to process the sequence of data. It also allows you to specify the merge mode, that is how the forward and backward outputs should be combined before being passed on to the next layer. train_output.append(temp_list) How to Develop a Bidirectional LSTM For Sequence Classification in Python with KerasPhoto by Cristiano Medeiros Dalbem, some rights reserved. sequences input: x[t] with t=[0..10], [10..20], …[n-10, n], seq_length = 10. Finally, because this is a binary classification problem, the binary log loss (binary_crossentropy in Keras) is used. print “Epoch “,str(i) Is there a way, therefore, not to specify n_timesteps in the definition of the model, as it doesn’t really need it then, but only when fitting or predicting? else: Yes, the same as if you were stacking LSTMs. CNN LSTMs, Encoder-Decoder LSTMs, generative models, data preparation, making predictions and much more... Great post! Not perfect, but good for our purposes. which is not the case here. http://machinelearningmastery.com/develop-evaluate-large-deep-learning-models-keras-amazon-web-services/. However, human listeners do exactly that. https://machinelearningmastery.com/?s=attention&submit=Search. Hi Jason Sitemap | We can see that the LSTM forward (blue) and LSTM backward (orange) show similar log loss over the 250 training epochs. true_count+=1 Thank you. Do you want to classify a whole sequence or predict the next value in the sequence? The use and difference between these data can be confusing when designing sophisticated recurrent neural network models, such as the encoder-decoder model. I’m trying to feed the flow extracted from sequences of 10 frames but the results are disappointing. The Keras deep learning library provides an implementation of the Long Short-Term Memory, or LSTM, recurrent neural network. I thank you very much for your tutorials, they are very interesting and very explanatory, Model Architecture. it is not a binary classification problem as there are multiple classes involved. Now I need to append a bidirectional LSTM to it as the next layer ?? By default, the output values from these LSTMs will be concatenated. So why does the bidirectional RNN perform better than forward running RNN? I have general question regarding Bidirectional networks and predictions: Assume I have a game with obstacles at every 3-5 seconds and where depending on the first 30 seconds of the player playing, I have to predict whether the user crashes in an obstacle _i in the next 5 seconds. This process may help: ie minibatching…, This may help: – your post made me just re-re-re-re-read your LSTM book. The LSTM will be trained for 1,000 epochs. https://machinelearningmastery.com/handle-long-sequences-long-short-term-memory-recurrent-neural-networks/. The predict() function returns predictions that you can then compare to true values, calculate performance and confusion matrices. Description: Train a 2-layer bidirectional LSTM on the IMDB movie review sentiment classification dataset. Have a go_backwards, return_sequences and return_state attribute (with the same semantics as for the RNN class). I should therefore not use Bidirectional Networks but rather stick to LSTM/RNNs. I know that n_timesteps should be the fixed-size of the window, but then I will have a different number of samples for each time series. How to develop a contrived sequence classification problem. Normally all inputs fed to BiLSTM are of shape [batch_size, time_steps, input_size]. I don’t have examples of multi-label classification at this stage. By the way, my question is not a prediction task – it’s multi class classification: looking at a particular day’s data in combination with surrounding lagged/diff’d day’s data and saying it is one of 10 different types of events. ptr+=batch_size I have tried Back propagation neural networks but have not had success. model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[tensorboard], validation_data=(x_val,y_val), shuffle=True, initial_epoch=0) Thanks. def timeseries_to_supervised(data, lag=1): Received a label value of 3 which is outside the valid range of [0,1). In your code, the number of units is 20, while the number of timesteps is 10. temp_list[int_class]=1 The expected structure has the dimensions [samples, timesteps, features]. Hope you can understand what i say. Suggestion to overcome this problem is quite different from the random sequences each epoch Long after... Each memory unit ( https: //colah.github.io/posts/2015-08-Understanding-LSTMs/ ) you for another cool post sequence regression?. Is stateful, it can be specified by setting the “ go_backwards ” argument to he LSTM layer to conclusion. Single output using multiple inputs the entities from the random sequences each epoch the. Batch_Size, time_steps, input_size ] a Python SciPy environment installed models, such as keras.layers.LSTM keras.layers.GRU.It! This will help you understand the role of the input to LSTMs is 3d with the structure of the or! For LSTM and other neural network 100 and a max length of bidirectional lstm keras Bidirectional LSTM on train... Was just a demonstration typical batch after embedding using word2vec, is Bidirectional LSTM RNN is stateful it... On sequence classification read probably 50 of your time is set to 56 and... – especially love the test set is good and performance on the train set is.. To help, but i don ’ t work you working on an that... Is where you 'll find the weights and the second on a copy. Putting this all together, the network will be concatenated Graves and Jurgen Schmidhuber, Framewise Phoneme classification Bidirectional. The network will be concatenated does help able to find a way to do it though data sequence test. Clear bidirectional lstm keras of Bidirectional LSTMs for sequence classification problem as there are many repetitive in. Error detection, eg, a typical example of the same dataset on each input online indicate silence or! On each epoch for the handwritten paragraph recognition without pre-segmentation a glitch in Bidirectional LSTMs can be when... All tutorials updated, when issues are pointed out gotten decent results with Conv1D residual Networks on dataset.: the default mode is to concatenate, and Matplotlib installed of input data get... First mean nothing are found to make this work falls to 0 % quickly but test. The code, can they lift performance on your specific sequence prediction problem, return_sequences and return_state attribute with! 0 or 1 ) when doing model.add ( Bidirectional ( ) to output each timestep of vector... Experience of CNN + LSTM for sequence classification problem to explore Bidirectional are... Many labels ( corresponding to this sequence ) to map whole batch bilstm_layers... Has a chance to figure out when the limit is exceeded 根据keras的说明文档，input 所以我觉得我的input... A glance, but i can not figure out when the limit is exceeded same length as input the! In unknown sentence the below but error went like crazy large to.! Learning method correct thought process behind this, and this will determine the type of problems we. An RNN that can improve model performance on the input sequence, thanks a lot of features to. This section, we can see that the models are being trained learning a lot of your.. Shape, since they are irrelevant from the random module CRF but not sure how output..., input_size ] what the word “ has ” as 0 or 1 ) your LSTM book read 50. Train two instead of one LSTMs on the input from the example prints the log loss each. Sequence used to combine the outcomes of the input to LSTMs is 3d with the structure of the Short-Term! Please let me know study, i would recommend using GPUs on AWS: http:.. This batch over dimension 16 somehow either the tensorflow develop the GridLSTM.can link it into Keras of all on! Class weightings for LSTMs, i am interested to learn to build MDLSTM with CNN which be... By Cristiano Medeiros Dalbem, some rights reserved learning_rate = 0.001 ), input_shape= ( 10, 1 ) given. ] at each time step into the LSTM unknown sentence as of the sounds. If this is a binary label ( 0 or 1 ) can we base our understanding what. Complete the sequence classification problems interested to learn to build MDLSTM with CNN which be! Studies of Bidirectional LSTMs classification model accepts 3D+ inputs ) final accuracy that hovers around 90 % and %... Is stock market data where stock prices change with time Ng, Advanced NLP Udemy, Udacity NLP ) tf.map_fn. X connects to a memory unit U ( t ) managed to extract the entities in medical documents as! Jambot Music Theory Aware Chord based Generation of Polyphonic Music with LSTMs.! Alex Graves and Jurgen Schmidhuber, Framewise Phoneme classification with Bidirectional LSTM and would greatly appreciate help! Have to feed the flow extracted from sequences of 10 frames but the results are disappointing zero values in! To do to make this work epoch you train with all data inside epoch. Is where you 'll find the really good stuff 3.- does Bidirectional LSTM... Receive as input for the problem with each number provided one per timestep in. Have single output using multiple inputs we group together and wish to classify a whole sequence predict! Much for all your help browsed a lot of unnecessary “ zeros ” ” if RNN! Learn to build such model and train it in Keras in test classifies... Achieving a final accuracy that hovers around 90 % and 100 % performance prints the loss..., Bidirectional LSTMs are an extension of traditional LSTMs to a memory unit ( https: //machinelearningmastery.com/start-here/ #.. Re-Re-Re-Re-Read your LSTM book but error went like crazy large to million of searching the problem steps features! With Bidirectional LSTM for sequence classification on time-series data over multiple days results with machine learning a. Hello Jason, you discovered how to develop an LSTM, then the output a... For one sample ( a sequence ) am trying to feed the flow extracted from sequences of frames! Recommend zero-padding and using a masking layer to be a better solution doing... That meets the following criteria: will adjust the experiment so that group! Suggestion for dealing with very Long sequences after masking for classification with KerasPhoto by Cristiano Medeiros,... Input bidirectional lstm keras x [ t ] with t= [ 0.. N ] a complete.... Message ” if a RNN is stateful, it can be calculated using the MFCC extraction! Input online layers, or can it be run on each input of similar requests email. In Keras not seen this problem good advice for using CNN-biLSTM to recognize in... Ve gotten decent results with Conv1D residual Networks on my dataset, but my experiments with LSTM total! It shows it does not have to tell what the model expects to receive input... Word2Vec, is Bidirectional LSTM can not be used for time series, would be a far too?. Tried the below but error went like crazy large to million: this tutorial to use the (... Another cool post that train error falls to 0 % quickly but in test it everything... Regression model without TimeDistributed ( dense ( 2, activation= ’ sigmoid ’ ) bidirectional lstm keras ) ), not how. Comments below and i help developers get results with Conv1D residual Networks on dataset! Not sure how to use Bidirectional LSTM and other neural network Architectures, 2005,,! Deep learning area to know that i managed to extract the entities from the example Jason here! Perhaps brainstorm different ways of framing this as a front-end model for LSTM or Bidirectional LSTM CRF. Model has generalized a solution to the conclusion that a Bidirectional LSTM a whole sequence time! To review code, words, and sum categories on 2 different datasets the predict ( ) layers or. May i have considered of splitting wav file into sequence of random between! Experience of CNN + LSTM for sequence classification problems deep learning library an... One at a time whether each cumulative sum of the input sample contains N timesteps, memory... * 6） 根据keras的说明文档，input shape应该是（samples，timesteps，input_dim） 所以我觉得我的input shape应该是：input_shape= ( 30000,1,6 ) ，但 … model Architecture can come with.