input_shape=(64, 7, 339), --> input_shape=(7, 339). To create a hidden-to-hidden LSTM, can we do: Say d1 has “a,b,c,d” and d2 has “P,Q,R,S”. This is really a big help. In Keras, when an LSTM(return_sequences = True) layer is followed by Dense() layer, this is equivalent to LSTM(return_sequences = True) followed by TimeDistributed(Dense()). 1.return_sequences=False && return_state=False. model = Model(inputs=[input1 , input2],outputs=output1). https://machinelearningmastery.com/stacked-long-short-term-memory-networks/. The CodeLab is very similar to the Keras LSTM CodeLab. input1 = Input(shape=(25,)) For a model that takes 2 inputs, they must be provided to fit() as an array. from keras.models import Sequential from keras.layers import LSTM, Dense import numpy as np data_dim = 16 timesteps = 8 nb_classes = 10 batch_size = 32 # expected input batch shape: (batch_size, timesteps, data_dim) # note that we have to provide the full batch_input_shape since the network is stateful. That sounds complex. I want to study that is there any advantage of communicating cells states in each time steps of both streams rather than without communicate (just as normal 2-stream network) as part of my research. Django According to the documentation, the output of LSTM should be a 3D array: if return_sequences: 3D tensor with shape (nb_samples, timesteps, output_dim). Got it. We can see the output array's shape of the LSTM layer is (1,3,1) which stands for (#Samples, #Time steps, #LSTM units). batch_size=128,callbacks=[logger_tb] When you use return state, you are only getting the state for the last time step. Keras LSTM is pattern 2 (previous output to current hidden) by default? Amazing explanation! Twitter |
return_sequences: Boolean. Thank you so much, Jason. Whether to return the last output. What does setting the initial state mean for a LSTM network? I mean shouldn’t there be 3 neurons/LSTM(3) to process the (1,3,1) shape data? Afterwards update next time step with this previous time step’s average value + existing cell state value. Search, Making developers awesome at machine learning, Click to Take the FREE LSTMs Crash-Course, Long Short-Term Memory Networks With Python, How to Use the TimeDistributed Layer for Long Short-Term Memory Networks in Python, A ten-minute introduction to sequence-to-sequence learning in Keras, Long Short-Term Memory Networks with Python, How to Index, Slice and Reshape NumPy Arrays for Machine Learning, http://proceedings.mlr.press/v37/jozefowicz15.pdf, https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/, https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me, https://machinelearningmastery.com/prepare-univariate-time-series-data-long-short-term-memory-networks/, https://stackoverflow.com/questions/54850854/keras-restore-lstm-hidden-state-for-a-specific-time-stamp, https://machinelearningmastery.com/gentle-introduction-backpropagation-time/, https://machinelearningmastery.com/truncated-backpropagation-through-time-in-keras/, https://machinelearningmastery.com/stacked-long-short-term-memory-networks/, https://machinelearningmastery.com/get-help-with-keras/, https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input, https://machinelearningmastery.com/faq/single-faq/how-is-data-processed-by-an-lstm, https://machinelearningmastery.com/faq/single-faq/how-do-i-calculate-accuracy-for-regression, How to Reshape Input Data for Long Short-Term Memory Networks in Keras, How to Develop an Encoder-Decoder Model for Sequence-to-Sequence Prediction in Keras, How to Develop an Encoder-Decoder Model with Attention in Keras, How to Use the TimeDistributed Layer in Keras, A Gentle Introduction to LSTM Autoencoders. https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me, https://stackoverflow.com/questions/49313650/how-could-i-get-both-the-final-hidden-state-and-sequence-in-a-lstm-layer-when-us, Awesome Work Jason. [[Node: embedding_layer_input = Placeholder[dtype=DT_FLOAT, shape=[], _device=”/job:localhost/replica:0/task:0/gpu:0″]()]] This article will see how to create a stacked sequence to sequence the LSTM model for time series forecasting in Keras/ TF 2.0. In early 2015, Keras had the first reusable open-source Python implementations of LSTM and GRU. In the example below, “output” has the same value as the last hidden state state_h.It is redundant. My code has three output of lstm : output, hidden_state, cell_state. or the prediction on t3? By default, the return_sequencesis set to False in Keras RNN layers, and this means the RNN layer will only return the last hidden state output a. Thank you very much. def _get_model(input_shape, latent_dim, num_classes): inputs = Input(shape=input_shape) histogram_freq=5 is causing this error, this is a bug in keras, set histogram_freq=0 and it should work fine. That return sequences return the hidden state output for each input time step. so in order to do classification by using the 2 embeddings, can i use this mathematique formule: softmax(V tanh(W1*E1 + W2*E2)) ? The return_state argument only controls whether the state is returned. The LSTM cell output depends on the return_sequences atribute. That return state returns the hidden state output and cell state for the last input time step. 2. model.add(LSTM(200, activation=’relu’, return_sequences=True)) lstm, forward_h, forward_c, backward_h, backward_c= Bidirectional(..)(Embedding) So I was wrong and the hidden state and the cell state is never the same? Understand the Difference Between Return Sequences and Return States for LSTMs in KerasPhoto by Adrian Curt Dannemann, some rights reserved. but when I write model.fit like that: model.fit(trainX, trainY=[lstm1, state_h, state_c], epochs=10, batch_size=1, verbose=2). import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, LSTM #, CuDNNLSTM mnist = tf. One-to-many sequence problems are the type of sequence problems where input data has one time-step and the output contains a vector of multiple values or multiple time-steps. 3. state variables as target variables in a call to fit. model = Model(inputs=[input_x, h_one_in , h_two_in], outputs=[y1,y2,state_h,state_c]). Layer 1, LSTM(128), reads the input data and outputs 128 features with 3 timesteps for each because return_sequences=True. Return sequences refer to return the cell state c. Hi Jason, is it possible to access the internal states through return_state = True and return_sequences = True with the Sequencial API? “Generally, we do not need to access the cell state unless we are developing sophisticated models where subsequent layers may need to have their cell state initialized with the final cell state of another layer, such as in an encoder-decoder model.” from keras.models import Sequential from keras.layers import LSTM, Dense import numpy as np data_dim = 16 timesteps = 8 nb_classes = 10 batch_size = 32 # expected input batch shape: (batch_size, timesteps, data_dim) # note that we have to provide the full batch_input_shape since the network is stateful. Ask your questions in the comments below and I will do my best to answer. log_dir=”logs_sentiment_lstm”, In the graph above we can see given an input sequence to an RNN layer, each RNN cell related to each time step will generate output known as the hidden state, a. For the rest of this tutorial, we will look at the API for access these data. This git repo includes a Keras LSTM summary diagram that shows: the use of parameters like return_sequences, batch_size, time_step... the real structure of lstm layers ; the concept of these layers in keras What Made You Believe In God,
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1 total views, 1 views today" />
input_shape=(64, 7, 339), --> input_shape=(7, 339). To create a hidden-to-hidden LSTM, can we do: Say d1 has “a,b,c,d” and d2 has “P,Q,R,S”. This is really a big help. In Keras, when an LSTM(return_sequences = True) layer is followed by Dense() layer, this is equivalent to LSTM(return_sequences = True) followed by TimeDistributed(Dense()). 1.return_sequences=False && return_state=False. model = Model(inputs=[input1 , input2],outputs=output1). https://machinelearningmastery.com/stacked-long-short-term-memory-networks/. The CodeLab is very similar to the Keras LSTM CodeLab. input1 = Input(shape=(25,)) For a model that takes 2 inputs, they must be provided to fit() as an array. from keras.models import Sequential from keras.layers import LSTM, Dense import numpy as np data_dim = 16 timesteps = 8 nb_classes = 10 batch_size = 32 # expected input batch shape: (batch_size, timesteps, data_dim) # note that we have to provide the full batch_input_shape since the network is stateful. That sounds complex. I want to study that is there any advantage of communicating cells states in each time steps of both streams rather than without communicate (just as normal 2-stream network) as part of my research. Django According to the documentation, the output of LSTM should be a 3D array: if return_sequences: 3D tensor with shape (nb_samples, timesteps, output_dim). Got it. We can see the output array's shape of the LSTM layer is (1,3,1) which stands for (#Samples, #Time steps, #LSTM units). batch_size=128,callbacks=[logger_tb] When you use return state, you are only getting the state for the last time step. Keras LSTM is pattern 2 (previous output to current hidden) by default? Amazing explanation! Twitter |
return_sequences: Boolean. Thank you so much, Jason. Whether to return the last output. What does setting the initial state mean for a LSTM network? I mean shouldn’t there be 3 neurons/LSTM(3) to process the (1,3,1) shape data? Afterwards update next time step with this previous time step’s average value + existing cell state value. Search, Making developers awesome at machine learning, Click to Take the FREE LSTMs Crash-Course, Long Short-Term Memory Networks With Python, How to Use the TimeDistributed Layer for Long Short-Term Memory Networks in Python, A ten-minute introduction to sequence-to-sequence learning in Keras, Long Short-Term Memory Networks with Python, How to Index, Slice and Reshape NumPy Arrays for Machine Learning, http://proceedings.mlr.press/v37/jozefowicz15.pdf, https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/, https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me, https://machinelearningmastery.com/prepare-univariate-time-series-data-long-short-term-memory-networks/, https://stackoverflow.com/questions/54850854/keras-restore-lstm-hidden-state-for-a-specific-time-stamp, https://machinelearningmastery.com/gentle-introduction-backpropagation-time/, https://machinelearningmastery.com/truncated-backpropagation-through-time-in-keras/, https://machinelearningmastery.com/stacked-long-short-term-memory-networks/, https://machinelearningmastery.com/get-help-with-keras/, https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input, https://machinelearningmastery.com/faq/single-faq/how-is-data-processed-by-an-lstm, https://machinelearningmastery.com/faq/single-faq/how-do-i-calculate-accuracy-for-regression, How to Reshape Input Data for Long Short-Term Memory Networks in Keras, How to Develop an Encoder-Decoder Model for Sequence-to-Sequence Prediction in Keras, How to Develop an Encoder-Decoder Model with Attention in Keras, How to Use the TimeDistributed Layer in Keras, A Gentle Introduction to LSTM Autoencoders. https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me, https://stackoverflow.com/questions/49313650/how-could-i-get-both-the-final-hidden-state-and-sequence-in-a-lstm-layer-when-us, Awesome Work Jason. [[Node: embedding_layer_input = Placeholder[dtype=DT_FLOAT, shape=[], _device=”/job:localhost/replica:0/task:0/gpu:0″]()]] This article will see how to create a stacked sequence to sequence the LSTM model for time series forecasting in Keras/ TF 2.0. In early 2015, Keras had the first reusable open-source Python implementations of LSTM and GRU. In the example below, “output” has the same value as the last hidden state state_h.It is redundant. My code has three output of lstm : output, hidden_state, cell_state. or the prediction on t3? By default, the return_sequencesis set to False in Keras RNN layers, and this means the RNN layer will only return the last hidden state output a. Thank you very much. def _get_model(input_shape, latent_dim, num_classes): inputs = Input(shape=input_shape) histogram_freq=5 is causing this error, this is a bug in keras, set histogram_freq=0 and it should work fine. That return sequences return the hidden state output for each input time step. so in order to do classification by using the 2 embeddings, can i use this mathematique formule: softmax(V tanh(W1*E1 + W2*E2)) ? The return_state argument only controls whether the state is returned. The LSTM cell output depends on the return_sequences atribute. That return state returns the hidden state output and cell state for the last input time step. 2. model.add(LSTM(200, activation=’relu’, return_sequences=True)) lstm, forward_h, forward_c, backward_h, backward_c= Bidirectional(..)(Embedding) So I was wrong and the hidden state and the cell state is never the same? Understand the Difference Between Return Sequences and Return States for LSTMs in KerasPhoto by Adrian Curt Dannemann, some rights reserved. but when I write model.fit like that: model.fit(trainX, trainY=[lstm1, state_h, state_c], epochs=10, batch_size=1, verbose=2). import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, LSTM #, CuDNNLSTM mnist = tf. One-to-many sequence problems are the type of sequence problems where input data has one time-step and the output contains a vector of multiple values or multiple time-steps. 3. state variables as target variables in a call to fit. model = Model(inputs=[input_x, h_one_in , h_two_in], outputs=[y1,y2,state_h,state_c]). Layer 1, LSTM(128), reads the input data and outputs 128 features with 3 timesteps for each because return_sequences=True. Return sequences refer to return the cell state c. Hi Jason, is it possible to access the internal states through return_state = True and return_sequences = True with the Sequencial API? “Generally, we do not need to access the cell state unless we are developing sophisticated models where subsequent layers may need to have their cell state initialized with the final cell state of another layer, such as in an encoder-decoder model.” from keras.models import Sequential from keras.layers import LSTM, Dense import numpy as np data_dim = 16 timesteps = 8 nb_classes = 10 batch_size = 32 # expected input batch shape: (batch_size, timesteps, data_dim) # note that we have to provide the full batch_input_shape since the network is stateful. Ask your questions in the comments below and I will do my best to answer. log_dir=”logs_sentiment_lstm”, In the graph above we can see given an input sequence to an RNN layer, each RNN cell related to each time step will generate output known as the hidden state, a. For the rest of this tutorial, we will look at the API for access these data. This git repo includes a Keras LSTM summary diagram that shows: the use of parameters like return_sequences, batch_size, time_step... the real structure of lstm layers ; the concept of these layers in keras What Made You Believe In God,
Walk Froghall To Consall,
Gourmet 2 Go,
Gourmet 2 Go,
Cheerio Crossword Clue,
Lowe's Rubbermaid Shed Accessories,
Keter Factor 8x11 Foot Large Resin Outdoor Shed,
Revolut Exchange Rate Vs Transferwise,
2 total views, 2 views today" />
keras lstm return_sequences

and I help developers get results with machine learning. The output of the LSTM layer has three components, they are (a, a, c), "T" stands for the last timestep, each one has the shape (#Samples, #LSTM units). Sequence problems can be broadly categorized into the following categories: 1. See the Keras RNN API guide for details about the usage of RNN API.. Based on available runtime hardware and constraints, this layer will choose different implementations (cuDNN-based or pure-TensorFlow) to maximize the performance. The major reason you want to set the return_state is an RNN may need to have its cell state initialized with previous time step while the weights are shared, such as in an encoder-decoder model. Keras LSTM is an output-to-hidden recurrent by default, e.g. Great question, here is an example: perhaps but to decrease complexity, i removed the two Bi-LSTM so i use the embeddings only for encoding. lstm1, state_h, state_c = LSTM(1, return_sequences=True, return_state=True)(inputs1) Hi so in the above example our network consist of only one lstm node or cell If you have used Input then do not mention input shape in LSTM layer. Hi Alex, did u find how to handle the fit in this case? Greatly appreciate if you could explain me how do we update LSTM cell states(as each time steps) by giving additional value. No complex coding and point to point. Basic Data Preparation 3. Its initial_state. If by hidden states you mean those states that are internal to the LSTM layers, then I don’t think there is an effective way to pass them to a dense. Back to me question: I use random initialization but the results are disappointing. Thanks for the great post. [0.2] Or is the LSTM going to process each input one after the other in sequence? in the another your post,encoder-decoder LSTM Model code as fllower: [[Node: output_layer_2/bias/read/_237 = _Recv[client_terminated=false, recv_device=”/job:localhost/replica:0/task:0/cpu:0″, send_device=”/job:localhost/replica:0/task:0/gpu:0″, send_device_incarnation=1, tensor_name=”edge_1546_output_layer_2/bias/read”, tensor_type=DT_FLOAT, _device=”/job:localhost/replica:0/task:0/cpu:0″]()]]. weights=[embedding_matrix], trainable=False)(input2 ) When you define the model like this: model = Model(inputs=inputs1, outputs=[lstm1, state_h, state_c]) and then fit, the fit() function expects three values for the output instead of 1. Sorry for the confusion. Great. 2. About Keras Getting started Developer guides Keras API reference Models API Layers API Callbacks API Data preprocessing Optimizers Metrics Losses Built-in small datasets Keras Applications Utilities Code examples Why choose Keras? You can use this tutorial as a starting point and change the LSTMs to GRUs: During handling of the above exception, another exception occurred: from keras.models import Sequential e.g. Coding LSTM in Keras. Hi Jason, Your specific output value will differ given the random initialization of the LSTM weights and cell state. One decoder(d1) gets input only from encoder while another one(d2) will get input from encoder and other decoder(d1). I'm trying to train LSTM network on data taken from a DataFrame. The basic understanding of RNN should be enough for the tutorial. You could use matplotlib and the plot() function. I am unsure how to go about defining that. It really solved my confusion. 1. generated in LSTM. or it can choose between teaching force and BPTT based on patterns? The LSTM hidden state output for the last time step. @ajanaliz.I took a quick look, and I believe that you need to remove the leading "64" from the input shape of the LSTM layer --> input_shape=(64, 7, 339), --> input_shape=(7, 339). To create a hidden-to-hidden LSTM, can we do: Say d1 has “a,b,c,d” and d2 has “P,Q,R,S”. This is really a big help. In Keras, when an LSTM(return_sequences = True) layer is followed by Dense() layer, this is equivalent to LSTM(return_sequences = True) followed by TimeDistributed(Dense()). 1.return_sequences=False && return_state=False. model = Model(inputs=[input1 , input2],outputs=output1). https://machinelearningmastery.com/stacked-long-short-term-memory-networks/. The CodeLab is very similar to the Keras LSTM CodeLab. input1 = Input(shape=(25,)) For a model that takes 2 inputs, they must be provided to fit() as an array. from keras.models import Sequential from keras.layers import LSTM, Dense import numpy as np data_dim = 16 timesteps = 8 nb_classes = 10 batch_size = 32 # expected input batch shape: (batch_size, timesteps, data_dim) # note that we have to provide the full batch_input_shape since the network is stateful. That sounds complex. I want to study that is there any advantage of communicating cells states in each time steps of both streams rather than without communicate (just as normal 2-stream network) as part of my research. Django According to the documentation, the output of LSTM should be a 3D array: if return_sequences: 3D tensor with shape (nb_samples, timesteps, output_dim). Got it. We can see the output array's shape of the LSTM layer is (1,3,1) which stands for (#Samples, #Time steps, #LSTM units). batch_size=128,callbacks=[logger_tb] When you use return state, you are only getting the state for the last time step. Keras LSTM is pattern 2 (previous output to current hidden) by default? Amazing explanation! Twitter |
return_sequences: Boolean. Thank you so much, Jason. Whether to return the last output. What does setting the initial state mean for a LSTM network? I mean shouldn’t there be 3 neurons/LSTM(3) to process the (1,3,1) shape data? Afterwards update next time step with this previous time step’s average value + existing cell state value. Search, Making developers awesome at machine learning, Click to Take the FREE LSTMs Crash-Course, Long Short-Term Memory Networks With Python, How to Use the TimeDistributed Layer for Long Short-Term Memory Networks in Python, A ten-minute introduction to sequence-to-sequence learning in Keras, Long Short-Term Memory Networks with Python, How to Index, Slice and Reshape NumPy Arrays for Machine Learning, http://proceedings.mlr.press/v37/jozefowicz15.pdf, https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/, https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me, https://machinelearningmastery.com/prepare-univariate-time-series-data-long-short-term-memory-networks/, https://stackoverflow.com/questions/54850854/keras-restore-lstm-hidden-state-for-a-specific-time-stamp, https://machinelearningmastery.com/gentle-introduction-backpropagation-time/, https://machinelearningmastery.com/truncated-backpropagation-through-time-in-keras/, https://machinelearningmastery.com/stacked-long-short-term-memory-networks/, https://machinelearningmastery.com/get-help-with-keras/, https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input, https://machinelearningmastery.com/faq/single-faq/how-is-data-processed-by-an-lstm, https://machinelearningmastery.com/faq/single-faq/how-do-i-calculate-accuracy-for-regression, How to Reshape Input Data for Long Short-Term Memory Networks in Keras, How to Develop an Encoder-Decoder Model for Sequence-to-Sequence Prediction in Keras, How to Develop an Encoder-Decoder Model with Attention in Keras, How to Use the TimeDistributed Layer in Keras, A Gentle Introduction to LSTM Autoencoders. https://machinelearningmastery.com/faq/single-faq/why-does-the-code-in-the-tutorial-not-work-for-me, https://stackoverflow.com/questions/49313650/how-could-i-get-both-the-final-hidden-state-and-sequence-in-a-lstm-layer-when-us, Awesome Work Jason. [[Node: embedding_layer_input = Placeholder[dtype=DT_FLOAT, shape=[], _device=”/job:localhost/replica:0/task:0/gpu:0″]()]] This article will see how to create a stacked sequence to sequence the LSTM model for time series forecasting in Keras/ TF 2.0. In early 2015, Keras had the first reusable open-source Python implementations of LSTM and GRU. In the example below, “output” has the same value as the last hidden state state_h.It is redundant. My code has three output of lstm : output, hidden_state, cell_state. or the prediction on t3? By default, the return_sequencesis set to False in Keras RNN layers, and this means the RNN layer will only return the last hidden state output a. Thank you very much. def _get_model(input_shape, latent_dim, num_classes): inputs = Input(shape=input_shape) histogram_freq=5 is causing this error, this is a bug in keras, set histogram_freq=0 and it should work fine. That return sequences return the hidden state output for each input time step. so in order to do classification by using the 2 embeddings, can i use this mathematique formule: softmax(V tanh(W1*E1 + W2*E2)) ? The return_state argument only controls whether the state is returned. The LSTM cell output depends on the return_sequences atribute. That return state returns the hidden state output and cell state for the last input time step. 2. model.add(LSTM(200, activation=’relu’, return_sequences=True)) lstm, forward_h, forward_c, backward_h, backward_c= Bidirectional(..)(Embedding) So I was wrong and the hidden state and the cell state is never the same? Understand the Difference Between Return Sequences and Return States for LSTMs in KerasPhoto by Adrian Curt Dannemann, some rights reserved. but when I write model.fit like that: model.fit(trainX, trainY=[lstm1, state_h, state_c], epochs=10, batch_size=1, verbose=2). import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, LSTM #, CuDNNLSTM mnist = tf. One-to-many sequence problems are the type of sequence problems where input data has one time-step and the output contains a vector of multiple values or multiple time-steps. 3. state variables as target variables in a call to fit. model = Model(inputs=[input_x, h_one_in , h_two_in], outputs=[y1,y2,state_h,state_c]). Layer 1, LSTM(128), reads the input data and outputs 128 features with 3 timesteps for each because return_sequences=True. Return sequences refer to return the cell state c. Hi Jason, is it possible to access the internal states through return_state = True and return_sequences = True with the Sequencial API? “Generally, we do not need to access the cell state unless we are developing sophisticated models where subsequent layers may need to have their cell state initialized with the final cell state of another layer, such as in an encoder-decoder model.” from keras.models import Sequential from keras.layers import LSTM, Dense import numpy as np data_dim = 16 timesteps = 8 nb_classes = 10 batch_size = 32 # expected input batch shape: (batch_size, timesteps, data_dim) # note that we have to provide the full batch_input_shape since the network is stateful. Ask your questions in the comments below and I will do my best to answer. log_dir=”logs_sentiment_lstm”, In the graph above we can see given an input sequence to an RNN layer, each RNN cell related to each time step will generate output known as the hidden state, a. For the rest of this tutorial, we will look at the API for access these data. This git repo includes a Keras LSTM summary diagram that shows: the use of parameters like return_sequences, batch_size, time_step... the real structure of lstm layers ; the concept of these layers in keras