CNN Long Short-Term Memory Networks

Last Updated on

Gentle introduction to CNN LSTM recurrent neural networks
with example Python code.

Input with spatial structure, like images, cannot be modeled easily with the standard Vanilla LSTM.

The CNN Long Short-Term Memory Network or CNN LSTM for short is an LSTM architecture specifically designed for sequence prediction problems with spatial inputs, like images or videos.

In this post, you will discover the CNN LSTM architecture for sequence prediction.

After completing this post, you will know:

  • About the development of the CNN LSTM model architecture for sequence prediction.
  • Examples of the types of problems to which the CNN LSTM model is suited.
  • How to implement the CNN LSTM architecture in Python with Keras.

Discover how to develop LSTMs such as stacked, bidirectional, CNN-LSTM, Encoder-Decoder seq2seq and more in my new book, with 14 step-by-step tutorials and full code.

Let’s get started.

Convolutional Neural Network Long Short-Term Memory Networks

Convolutional Neural Network Long Short-Term Memory Networks
Photo by Yair Aronshtam, some righs reserved.

CNN LSTM Architecture

The CNN LSTM architecture involves using Convolutional Neural Network (CNN) layers for feature extraction on input data combined with LSTMs to support sequence prediction.

CNN LSTMs were developed for visual time series prediction problems and the application of generating textual descriptions from sequences of images (e.g. videos). Specifically, the problems of:

  • Activity Recognition: Generating a textual description of an activity demonstrated in a sequence of images.
  • Image Description: Generating a textual description of a single image.
  • Video Description: Generating a textual description of a sequence of images.

[CNN LSTMs are] a class of models that is both spatially and temporally deep, and has the flexibility to be applied to a variety of vision tasks involving sequential inputs and outputs

Long-term Recurrent Convolutional Networks for Visual Recognition and Description, 2015.

This architecture was originally referred to as a Long-term Recurrent Convolutional Network or LRCN model, although we will use the more generic name “CNN LSTM” to refer to LSTMs that use a CNN as a front end in this lesson.

This architecture is used for the task of generating textual descriptions of images. Key is the use of a CNN that is pre-trained on a challenging image classification task that is re-purposed as a feature extractor for the caption generating problem.

… it is natural to use a CNN as an image “encoder”, by first pre-training it for an image classification task and using the last hidden layer as an input to the RNN decoder that generates sentences

Show and Tell: A Neural Image Caption Generator, 2015.

This architecture has also been used on speech recognition and natural language processing problems where CNNs are used as feature extractors for the LSTMs on audio and textual input data.

This architecture is appropriate for problems that:

  • Have spatial structure in their input such as the 2D structure or pixels in an image or the 1D structure of words in a sentence, paragraph, or document.
  • Have a temporal structure in their input such as the order of images in a video or words in text, or require the generation of output with temporal structure such as words in a textual description.
Convolutional Neural Network Long Short-Term Memory Network Architecture

Convolutional Neural Network Long Short-Term Memory Network Architecture

Need help with LSTMs for Sequence Prediction?

Take my free 7-day email course and discover 6 different LSTM architectures (with code).

Click to sign-up and also get a free PDF Ebook version of the course.

Start Your FREE Mini-Course Now!

Implement CNN LSTM in Keras

We can define a CNN LSTM model to be trained jointly in Keras.

A CNN LSTM can be defined by adding CNN layers on the front end followed by LSTM layers with a Dense layer on the output.

It is helpful to think of this architecture as defining two sub-models: the CNN Model for feature extraction and the LSTM Model for interpreting the features across time steps.

Let’s take a look at both of these sub models in the context of a sequence of 2D inputs which we will assume are images.

CNN Model

As a refresher, we can define a 2D convolutional network as comprised of Conv2D and MaxPooling2D layers ordered into a stack of the required depth.

The Conv2D will interpret snapshots of the image (e.g. small squares) and the polling layers will consolidate or abstract the interpretation.

For example, the snippet below expects to read in 10×10 pixel images with 1 channel (e.g. black and white). The Conv2D will read the image in 2×2 snapshots and output one new 10×10 interpretation of the image. The MaxPooling2D will pool the interpretation into 2×2 blocks reducing the output to a 5×5 consolidation. The Flatten layer will take the single 5×5 map and transform it into a 25-element vector ready for some other layer to deal with, such as a Dense for outputting a prediction.

This makes sense for image classification and other computer vision tasks.

LSTM Model

The CNN model above is only capable of handling a single image, transforming it from input pixels into an internal matrix or vector representation.

We need to repeat this operation across multiple images and allow the LSTM to build up internal state and update weights using BPTT across a sequence of the internal vector representations of input images.

The CNN could be fixed in the case of using an existing pre-trained model like VGG for feature extraction from images. The CNN may not be trained, and we may wish to train it by backpropagating error from the LSTM across multiple input images to the CNN model.

In both of these cases, conceptually there is a single CNN model and a sequence of LSTM models, one for each time step. We want to apply the CNN model to each input image and pass on the output of each input image to the LSTM as a single time step.

We can achieve this by wrapping the entire CNN input model (one layer or more) in a TimeDistributed layer. This layer achieves the desired outcome of applying the same layer or layers multiple times. In this case, applying it multiple times to multiple input time steps and in turn providing a sequence of “image interpretations” or “image features” to the LSTM model to work on.

We now have the two elements of the model; let’s put them together.

CNN LSTM Model

We can define a CNN LSTM model in Keras by first defining the CNN layer or layers, wrapping them in a TimeDistributed layer and then defining the LSTM and output layers.

We have two ways to define the model that are equivalent and only differ as a matter of taste.

You can define the CNN model first, then add it to the LSTM model by wrapping the entire sequence of CNN layers in a TimeDistributed layer, as follows:

An alternate, and perhaps easier to read, approach is to wrap each layer in the CNN model in a TimeDistributed layer when adding it to the main model.

The benefit of this second approach is that all of the layers appear in the model summary and as such is preferred for now.

You can choose the method that you prefer.

Further Reading

This section provides more resources on the topic if you are looking go deeper.

Papers on CNN LSTM

Keras API

Posts

Summary

In this post, you discovered the CNN LSTN model architecture.

Specifically, you learned:

  • About the development of the CNN LSTM model architecture for sequence prediction.
  • Examples of the types of problems to which the CNN LSTM model is suited.
  • How to implement the CNN LSTM architecture in Python with Keras.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

Develop LSTMs for Sequence Prediction Today!

Long Short-Term Memory Networks with Python

Develop Your Own LSTM models in Minutes

...with just a few lines of python code

Discover how in my new Ebook:
Long Short-Term Memory Networks with Python

It provides self-study tutorials on topics like:
CNN LSTMs, Encoder-Decoder LSTMs, generative models, data preparation, making predictions and much more...

Finally Bring LSTM Recurrent Neural Networks to
Your Sequence Predictions Projects

Skip the Academics. Just Results.

See What's Inside

182 Responses to CNN Long Short-Term Memory Networks

  1. Erick August 21, 2017 at 6:07 pm #

    Would this architecture, with some adaptations, also be suitable to do speech recognition, speaker separation, language detection and other natural language processing tasks?

  2. birol August 22, 2017 at 6:27 pm #

    what is difference with ConvLSTM2D layer ?
    https://github.com/fchollet/keras/blob/master/examples/conv_lstm.py

    • Jason Brownlee August 23, 2017 at 6:45 am #

      As far as I know, that layer is not yet supported. I have tried to stay away from it until all the bugs are worked out of it.

    • Dan Lim August 25, 2017 at 2:59 pm #

      ConvLSTM is variant of LSTM which use convolution to replace inner procut within LSTM unit
      while CNN LSTM is just stack of layer; CNN followed by LSTM.

      • Jason Brownlee August 25, 2017 at 3:58 pm #

        Have you used it on a project Dan?

        • Dan Lim August 30, 2017 at 2:09 pm #

          Not yet, I’m just waiting next tensorflow release since it seems that convlstm would be provided as tf.contrib.rnn.ConvLSTMCell, instead I’ve used cnn + lstm on simple speech recognition experiments and it gives better results than stack of lstm. It really works!

          • Jason Brownlee August 30, 2017 at 4:18 pm #

            Thanks Dan.

            I hope to try some examples myself for the blog soon.

          • miled June 20, 2018 at 7:50 am #

            @Dan Lim can you share me your script in speech recognition and thanks you.

  3. Miles August 25, 2017 at 7:13 am #

    Hi, Jason.
    Do you think the CNNLSTM can solve the regression problem, whose inputs are some time series data and some properties/exogenous data (spatial), not image data? If yes, how to deal with the properties/exogenous data (2D) in CNN. Thank you.

    • Tahir August 25, 2017 at 2:24 pm #

      I m having the same question

    • Jason Brownlee August 25, 2017 at 3:56 pm #

      Perhaps, I have not tried using CNN LSTMs for time series.

      Perhaps each series could be processed by a 1D-CNN.

      It might not make sense given that the LSTM is already interpreting the long term relationships in the data.

      It might be interesting if the CNN can pick out structure that is new/different from the LSTM. Perhaps you could have both a CNN and LSTM interpretation of the series and use another model to integrate and interpret the results.

      • Jen Liu June 13, 2018 at 4:33 am #

        I tried to use CNN + LSTM for timeseries forecasting, hoping that CNN can uncover some structure in the input signals. So far, it seems to perform worse than a 2-layered LSTM model, even after tuning hyperparameters. I thought I would get your book to look at the details, but sounds like this was not covered in the book? Your previous posting on LSTM model was very helpful. Thank you!

        • Jason Brownlee June 13, 2018 at 6:21 am #

          Generally, LSTMs perform worse on every time series problem I have tried them on (20+).

          You can learn why here:
          https://machinelearningmastery.com/suitability-long-short-term-memory-networks-time-series-forecasting/

          I recommend exhausting classical time series methods first, then try sklearn models, and then perhaps try neural nets.

        • boratonaj July 24, 2018 at 10:23 am #

          @Jen Liu, would like to see you manage to uncover some of the hidden signals for your implementation. Can you please share some insight on your CNN + LSTM for time series forecasting? thank you.

          • Jason Brownlee July 24, 2018 at 2:30 pm #

            I have been using the approach recently with great success.

            I have posts scheduled on the topic.

    • Anna March 21, 2018 at 5:39 pm #

      Hi,Miles.
      I m having the same question. Do you have some research progress on time series using the CNN LSTMs?

  4. Shamane Siriwardana September 28, 2017 at 1:27 pm #

    Hi do you have a github implementation ?

    • Jason Brownlee September 28, 2017 at 4:45 pm #

      I have a full code example in my book on LSTMs.

  5. Elisa October 6, 2017 at 1:10 pm #

    Hi Jason,
    Thank you for the great work and posts.

    I’m starting my studies with deep learning, python and keras.
    I would like knowing how to implement the CNN with ELM (extreme learning machine) architecture in Python with Keras for classification task. Do you have a github implementation?

  6. gana October 12, 2017 at 4:16 pm #

    Thank you for your great examples…

    May i ask you full code of the CNN LSTM you explained above?
    Because,..i am having errors related to dimensions of CNN and LSTM.

    I have followed your previous examples and trying to build VGG-16Net stacked with LSTM.

    My database is just 10 different human motion (10 classes) such as walking and running etc…

    My code is as below:

    # dimensions of our images.
    img_width, img_height = 224, 224

    train_data_dir = ‘db/train’
    validation_data_dir = ‘db/test’
    nb_train_samples = 400
    nb_validation_samples = 200
    num_timesteps = 10 # length of sequence
    num_class = 10
    epochs = 10
    batch_size = 8

    lstm_input_len = 224 * 224
    input_shape=(224,224,3)
    num_chan = 3

    # VGG16 as CNN
    cnn = Sequential()
    cnn.add(ZeroPadding2D((1,1),input_shape=input_shape))
    cnn.add(Conv2D(64, 3, 3, activation=’relu’))
    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(64, 3, 3, activation=’relu’))
    cnn.add(MaxPooling2D((2,2), strides=(2,2),dim_ordering=”th”))

    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(128, 3, 3, activation=’relu’))
    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(128, 3, 3, activation=’relu’))
    cnn.add(MaxPooling2D((2,2), strides=(2,2),dim_ordering=”th”))

    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(256, 3, 3, activation=’relu’))
    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(256, 3, 3, activation=’relu’))
    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(256, 3, 3, activation=’relu’))
    cnn.add(MaxPooling2D((2,2), strides=(2,2),dim_ordering=”th”))

    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(512, 3, 3, activation=’relu’))
    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(512, 3, 3, activation=’relu’))
    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(512, 3, 3, activation=’relu’))
    cnn.add(MaxPooling2D((2,2), strides=(2,2),dim_ordering=”th”))

    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(512, 3, 3, activation=’relu’))
    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(512, 3, 3, activation=’relu’))
    cnn.add(ZeroPadding2D((1,1)))
    cnn.add(Conv2D(512, 3, 3, activation=’relu’))
    cnn.add(MaxPooling2D((2,2), strides=(2,2),dim_ordering=”th”))

    cnn.add(Flatten())
    cnn.add(Dense(4096, activation=’relu’))
    cnn.add(Dropout(0.5))
    cnn.add(Dense(4096, activation=’relu’))

    #LSTM
    model = Sequential()
    model.add(TimeDistributed(cnn, input_shape=(num_timesteps, 224, 224,num_chan)))
    model.add(LSTM(num_timesteps))
    model.add(Dropout(.2)) #added
    model.add(Dense(num_class, activation=’softmax’))

    model.compile(loss=’binary_crossentropy’, optimizer=’adam’, metrics=[‘accuracy’])

    # this is the augmentation configuration we will use for training
    train_datagen = ImageDataGenerator(rescale=1. / 255)

    # this is the augmentation configuration we will use for testing:
    # only rescaling
    test_datagen = ImageDataGenerator(rescale=1. / 255)

    train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(224, 224),
    batch_size=batch_size,
    class_mode=’binary’)

    validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(224, 224),
    batch_size=batch_size,
    class_mode=’binary’)

    model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

    • gana October 12, 2017 at 4:18 pm #

      I forgot to put error which is :

      ValueError: Error when checking input: expected time_distributed_9_input to have 5 dimensions, but got array with shape (8, 224, 224, 3)

      • N1k31t4 November 25, 2017 at 2:13 am #

        You aso need to specify a batch size in the input dimensions to that layer I guess, to get the fifth dimension. Try using: model.add(TimeDistributed(cnn, input_shape=(None, num_timesteps, 224, 224,num_chan))). The None will then allow variable batch size.

        • Mohamed November 25, 2017 at 2:15 am #

          yes that worked for me. Thanks

        • Emre April 14, 2019 at 9:33 pm #

          It doesn’t work for me. Anyone solved the same problem?

          • Jason Brownlee April 15, 2019 at 7:52 am #

            What problem are you having exactly?

          • stone May 5, 2019 at 4:34 pm #

            I met this dimension error too, have you solved it?

      • Yi Li April 16, 2018 at 6:05 am #

        I got the same error, have you solved it? May I ask you the way to solve it?

    • Jason Brownlee October 13, 2017 at 5:43 am #

      Sorry, I cannot debug your code. I list some places to get help with code here:
      https://machinelearningmastery.com/get-help-with-keras/

    • pmp September 24, 2018 at 11:55 pm #

      Hello. How do you feed inputs to your network ? How is it sure that they are fed sequentially?

  7. Long October 15, 2017 at 12:33 pm #

    Hi Jason,

    Assuming there are a data set with time series data (e.g temperature, rainfall) and geographic data(e.g. elevation, slope) for many grid positions, I need to use the data set to predict(regression) future weathers.

    I think of a method with LSTM (for time series data) + auxiliary (geographic data) to be a solution. But the results of forecast is not very good. Do you have other better methods? Or do you have a related lessons?

    Thank you very much.

  8. Ravindra December 7, 2017 at 8:58 pm #

    Hi Jason, Thanks a lot for this. I am having trouble implementing the same architecture of TimeDistributed CNN with LSTM using functional API. It is throwing an error when I pass the TImeDistributed layer to maxpooling step saying the input is not a tensor. Could you please put few lines of code for the Timedistributed CNN output into LSTM using functional API?

  9. Alex December 9, 2017 at 5:27 am #

    Hi Jason,

    How would I implement a CNN-LSTM classification problem with variable input lengths?

  10. Alex December 9, 2017 at 5:57 am #

    With the padding approach, I am worried the LSTM might learn a dependency between sequence length and classification.

    My data is structured such that sequences with more inputs are MUCH more likely to be a certain class than sequences with less inputs. However, I don’t want my model to learn this dependency.

    Is my intuition correct? I remember reading in your earlier article that the LSTM will learn to ignore the padded sequences, but I wasn’t sure to what extent.

  11. Rui December 21, 2017 at 4:05 am #

    How to apply conv operation to the sequence itself instead of features (time sample data) ?

  12. Alex February 18, 2018 at 10:26 am #

    Nice intro, but it’s very incomplete. After reading this I know how to build a CNN LSTM, but I still don’t have any concept of what the input to it looks like, and therefore I don’t know how to train it. What does the input to the network look like, exactly? How do I reconcile the concepts of having a batch size but at the same time my input being a sequence? For someone who has never used RNNs before, this is not at all clear.

    • Jason Brownlee February 19, 2018 at 9:00 am #

      It really depends on the application, e.g. the specifics of the problem to which you wish to apply this method.

  13. Mary March 9, 2018 at 6:06 am #

    what is the difference between using the LSTM you show here and using the encoder decoder LSTM model in case of Video and image description?

  14. Vinay Rajpoot March 9, 2018 at 5:38 pm #

    Can it be used for video summarization. Do you have a code for it?

    • Jason Brownlee March 10, 2018 at 6:23 am #

      Perhaps. I don’t have a worked example for video summarization.

  15. Kanika March 20, 2018 at 8:21 am #

    You say : ” In both of these cases, conceptually there is a single CNN model and a sequence of LSTM models, one for each time step”

    Can you please explain me on how is back propogation working here ? Assuming my sequence length is T, I have confusion as follow :

    First interpretation : If a interpret in a way that for each LSTM unit I have corresponding CNN unit. So if input sequence of length T, I have T LSTM’s and corresponding T CNN’s. Then if I am assuming that I am learning weights by back propagation, then shouldn’t all the CNN’s have different weights ? How could all CNN have weight shared across time ?

    Second interpretation : Only one CNN and T LSTM. Features across T frames extracted using the same CNN and passed on to T LSTM’s with different weights. But then how is this kind of network learning weights for the CNN.

    I have really spent alot of time to understand but I am still confused. Would be really really helpful if you could answer 🙂

  16. Shivali Goel March 21, 2018 at 7:16 am #

    What should the input look like in terms of shape?

    for e.g. for a 45*45 image:
    x_train.shape = (num_images, 45,45,num_channels)

    y_train.shape =???

    • Shivali Goel March 21, 2018 at 7:17 am #

      heres the code & image is actually 56*56*1

      print “building model…”

      model = Sequential()
      # define CNN model
      model.add(TimeDistributed(Conv2D(32, (3, 3), activation = ‘relu’),input_shape = (None, 56, 56, 1)))
      model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
      model.add(TimeDistributed(Flatten()))

      # define LSTM model
      model.add(LSTM(256,activation=’tanh’, return_sequences=True))
      model.add(Dropout(0.1))
      model.add(LSTM(256,activation=’tanh’, return_sequences=True))
      model.add(Dropout(0.1))
      model.add(Dense(2))
      model.add(Activation(‘softmax’))

      model.compile(loss=’binary_crossentropy’,
      optimizer=’adam’,
      class_mode=’binary’, metrics=[‘accuracy’])

      print model.summary()
      batch_size=1
      nb_epoch=100
      print len(final_input)
      print len(final_input1)

      X_train = numpy.array(final_input)
      X_test = numpy.array(final_input1)

      #y_train = numpy.array(y_train)
      #y_test = numpy.array(y_test)

      #y_train = y_train.reshape((10000,1))
      #y_test = y_test.reshape((1000,1))

      print “printing final shapes…”
      print “X_train: “, X_train.shape
      print “y_train: “, y_train.shape
      print “X_test: “, X_test.shape
      print “y_test: “, y_test.shape
      print

      print(‘Train…’)

      model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch,
      validation_data=(X_test, y_test))

      print(‘Evaluate…’)
      score, acc = model.evaluate(X_test, y_test, batch_size=batch_size,
      show_accuracy=True)
      print(‘Test score:’, score)
      print(‘Test accuracy:’, acc)

    • Jason Brownlee March 21, 2018 at 3:05 pm #

      shape = num_images, k

      Where k is the number of classes or 1 for binary classification.

  17. Fathi April 9, 2018 at 6:28 am #

    Hi, I’m working on a CNN LSTM Network. When I compile the following code I get the error below. I have an input_shape but I still get an error when I compile the code. Can you please help me.

    Thank you.

    Code :

    # Importing the Keras libraries and packages

    from keras.models import Sequential
    from keras.layers import Conv2D
    from keras.layers import MaxPooling2D
    from keras.layers import Flatten
    from keras.layers import Dense
    from keras.layers import LSTM
    from keras.layers import Dropout
    from keras.layers import TimeDistributed

    # Initialising the CNN

    classifier = Sequential()

    # Step 1 – Convolutionclassifier = Sequential()

    classifier.add(TimeDistributed(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = ‘relu’)))

    Error :

    ValueError: The first layer in a Sequential model must get an input_shape or batch_input_shape argument.

    • Jason Brownlee April 10, 2018 at 6:08 am #

      That is odd, I’m not sure what is going on there.

      • Fathi April 10, 2018 at 7:43 am #

        Do you have some advice for this situation please ?

        • Jason Brownlee April 11, 2018 at 6:28 am #

          Yes, I would recommend carefully debugging your code.

    • shahzeb May 9, 2018 at 5:54 am #

      Hi Fathy, did you get the solution of your problem? I am going through same trouble. if you get solution then let me know please

    • Philip Li June 20, 2018 at 10:47 am #

      You need to specify the time dimension: input_shape = (#time steps per sample, 64, 64, 3)

  18. Skye April 11, 2018 at 12:54 pm #

    Hi Jason,

    Thanks for your share!

    And is the convLSTM appropriate to solve the sea surface temperature prediction? I mean that we will input a sequence of grid maps and get the next temperature grid map?

    • Jason Brownlee April 11, 2018 at 4:19 pm #

      Perhaps. Try it and see.

      • Skye April 13, 2018 at 10:23 am #

        OK. Thank you! And do you have any suggestions for how the model should be modified for this problem?

        • guanyuan shuai May 8, 2018 at 1:55 pm #

          Hi, did you solve your problem? I am confused about importing these images as input for convLSTM? Can you share the code about data input, please?

  19. Hatef April 27, 2018 at 4:43 am #

    Hey there,
    Thanks for your informative post… It was very useful!
    I want to some similar task but a bit more complicated. Consider that we want to generalize or network to be able to use for different sizes. Therefore we need to look at frames in patch scale and then effect of patches of an image result image effect and then images result for the video. (Note that resizing is not possible in my case!)

    In other words consider we want to use video in the network in which each video has a different number of frames and also frames of different videos may have different number of patches considering different frame size for different videos. Therefore the input dimension should be e.g. [None(for batch),None(for frame), None(for patch),100,100,3]

    Actually I could not do its programming with Keras or TensorFlow! Would you please help with this?

    • Jason Brownlee April 27, 2018 at 6:14 am #

      With videos that have a different number of frames, you could:

      – normalize to have the same number of frames
      – pad videos to have the same number of frames and maybe use a masking layer
      – use a network that is indifferent to sequence length
      – … more ideas

      With different patch sizes, I think you mean different filter sizes. If so, you can use a multiple input model as described here:
      https://machinelearningmastery.com/keras-functional-api-deep-learning/

      Does that help?

  20. guanyuan shuai May 8, 2018 at 1:53 pm #

    Hi Jason,

    Thanks for your blog! I have some questions about how to apply this integrated model for my data. Now, I have time-series images with multiple bands for crop yield regression, how do I import these data as input for this model? Can you give me any examples or some references I can go to? Thanks so much!

  21. shahzeb May 9, 2018 at 5:51 am #

    i get an error “ValueError: The first layer in a Sequential model must get an input_shape or batch_input_shape argument.” when I run the following code of my model:

    model=Sequential()
    K.set_image_dim_ordering(‘th’)
    model.add(TimeDistributed(Convolution2D(64, (2,2), border_mode= ‘valid’ , input_shape=(1, 2, 2), activation= ‘relu’)))
    model.add(TimeDistributed(Convolution2D(64, (1,1), border_mode= ‘valid’, activation= ‘relu’)))
    model.add(TimeDistributed(MaxPooling2D(pool_size=(1,1))))
    model.add(TimeDistributed(Convolution2D(64, (1,1), activation= ‘relu’ )))
    model.add(TimeDistributed(MaxPooling2D(pool_size=(1,1))))
    model.add(TimeDistributed(Dropout(0.0)))
    model.add(TimeDistributed(Flatten()))
    model.add(TimeDistributed(Dense(16, activation= ‘relu’ )))
    model.add(TimeDistributed(Dense(16, activation= ‘relu’ )))
    #lstm
    m=Sequential()
    m.add(LSTM(units = 1, activation=’sigmoid’))

    • Jason Brownlee May 9, 2018 at 6:29 am #

      I’m eager to help, but I cannot debug your code for you.

      Perhaps post to stackoverflow?

  22. Cristián Irribarra May 19, 2018 at 6:51 am #

    Hey Jason, This example is very enlightening!
    I’m currently aiming to do anomaly detection on some radio-astronomic data, which consists is .tiff image files, where horizontal axis is the time stamp, and vertical is frequencies. In this case, using the frequencies axis as a space (since signals come in varied frequencies) do you think it would be better to apply a 1D convolutional layer than just using a normal LSTM layer when encoding the images?. I understand there is a spatial dependence in my data, but it’s only 1-dimensional. I would like to know your opinion about this.

    Btw, got your machine learning/deep learning/LSTM bundle, You’ve been my mentor these past months!

    • Jason Brownlee May 19, 2018 at 7:49 am #

      Yes, try 1D CNNs.

      For example, 1d CNNs are useful for sequences of words as input which has parallels with what you’re describing I think.

  23. Miguel Alba May 27, 2018 at 4:39 pm #

    Hey Jason your post are really good!, I was reading your book and try to apply the example for time series classification problem using a sequence of time series images like this guy does in his post: http://amunategui.github.io/unconventional-convolutional-networks/index.html

    my images are 20000 (each frame is adding next 30 minute price), “50×50″ 1 channel,
    The problem is that using all of regularization that I could, almost all of my architectures are about 0.51 accuracy, this is the last that I made:

    model = Sequential()
    model.add(TimeDistributed(Conv2D(5, (3,3), kernel_initializer=”he_normal”, activation= ‘relu’,kernel_regularizer=l2(0.0001)),
    input_shape=(None,img_rows,img_cols,1)))
    model.add(TimeDistributed(MaxPooling2D((2, 2), strides=(1,1))))
    model.add(TimeDistributed(Dropout(0.75)))
    model.add(TimeDistributed(BatchNormalization()))
    model.add(TimeDistributed(Conv2D(3, (2,2), kernel_initializer=”he_normal”, activation= ‘relu’,kernel_regularizer=l2(0.0001))))
    model.add(TimeDistributed(MaxPooling2D((2, 2), strides=(1, 1))))
    model.add(TimeDistributed(Dropout(0.75)))

    model.add(TimeDistributed(Flatten()))
    model.add(Bidirectional(LSTM(50)))
    model.add(Dropout(0.7))
    model.add(Dense(num_classes, activation=’softmax’))
    model.summary()
    model.compile(loss=’binary_crossentropy’, optimizer=keras.optimizers.Adam(lr=1e-6),metrics=[‘accuracy’])

    So I wanted to ask you, how could you avoid overffiting in this type of architectures, and if the height and length of the frames affect how the model identify all the patterns, as my problem where I don’t know if due to the very small details varying between my images (because are closely the same) could have an impact in the acc and the overfitting.

    It will be really nice if you know how to help me!

    Thank you, you have a nice books! 🙂

    • Jason Brownlee May 28, 2018 at 5:55 am #

      Interesting approach, I would prefer to model the data directly instead of the images. Perhaps with a 1D cnn.

      A good approach to stop overfitting with neural nets is early stopping against a validation dataset.

      Keras supports this here:
      https://keras.io/callbacks/#earlystopping

      • Hunghc2 June 4, 2018 at 10:31 am #

        Hello Jason,

        You help me a lot.

        I have a problem here. I have a project use CNN-LSTM model. However, when I use 1D cnn the performance of Maxpooling layer for the filter number is better than Maxpooling layer for the data size. So I have to resize of data after cnn layer by Pernute layer. How do you think about this?

        • Jason Brownlee June 4, 2018 at 2:37 pm #

          If it results in good performance, then go with it.

          Alternately, I wonder if you can explore alternate filter sizes in the CNN layer to change the output size?

  24. Hunghc2 June 4, 2018 at 5:40 pm #

    Thank you for the answer.

    Actually, I have already changed the filter sizes multiple times. I know normally, the Maxpooling layer is applied to reduce the data size not the number of filtes Even keras only support Maxpooling in cnn2D for width or height of data, so I little worry about this.

  25. Josh N July 20, 2018 at 12:14 am #

    Greeting Dr.Jason
    My thanks to your tutorial. I’ve got some question.
    According to your tutorial here https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/
    I wonder if I could implement your idea of CNN LSTM with that tutorial? If so, what should I change in code? I am trying to do implement it but somehow I stuck with it.
    Also, does it make sense to use this model for classification work?
    I would appreciated if you answering back Dr.Jason. Thank you so much.

    • Jason Brownlee July 20, 2018 at 6:00 am #

      Yes you could.

      I have some tutorials on this scheduled.

  26. jorge August 13, 2018 at 8:01 pm #

    Dear Jason

    Thanks again for your tutorial

    Sorry you don’t have tutorial that include dataset which implement CNN (conv2D) and LSTM together

    • Jason Brownlee August 14, 2018 at 6:17 am #

      I believe I have an example in the LSTM book and I have some examples scheduled for the blog soon.

  27. aafaq August 28, 2018 at 3:38 pm #

    Hi Jason,

    I am using GRUs for sequence learning in captioning problem. What is meant by training loss in GRU training ? and my loss starts from 9.### and drops down till 0.29## but if I keep training then it starts ti increase again. Any Idea what makes the loss increase again ?
    My loss function is

    loss_function = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y_true,
    logits=y_pred)
    loss_mean = tf.reduce_mean(loss_function)

    y_true and y_labels are tokens sequences of the captions

  28. Pelonomi Moiloa October 3, 2018 at 4:14 pm #

    Hellooo Mr Brownlee

    I see in the comments that you have mentioned that you might investigate the ConvLSTM layer now available in Keras. I first want to think you for such an immense contribution, your blog has been extremely useful to me in understanding LSTMs. It must take a lot of your time to keep up with all these comments on top of providing the content that you do. However, I have read many of your posts but the knowledge I have fails me!

    I am hoping to take advantage of the ConvLSTM2D for a video segmentation problem. It is a very different application to the sequence predictions frequented on this blog. I have N videos of 500 frames each and each video corresponds to a single 2D segmentation mask. I think it is a many to one problem:

    Input: (N, 500, cols, rows, 1)
    Output: (N, 1, cols, rows, 1)

    As per your post on how to deal with long sequences, I have adjusted my input to contain sequence “fragments” , for example of 50 time steps so that I now have:

    Input: (N, 10, 50, cols, rows, 1)
    Output (N, 1, 1, cols, rows, 1)

    Which does not work out so well because Keras LSTM expects a 5D array, not 6D. My understanding was that I would be able to feed a single sequence at a time into a stateful LSTM (500 images chopped up into fragments of 50) and that I could some how remember the state across the 500 images in this way in order to make a final prediction before deciding whether to update the gradients or not.

    My implementation approach did not work with Input: (10, 50, cols, rows, 1) as here “10” is considered as the number of samples and thus corresponding output is required to be (10, 1, cols, rows, 1) ie. a segmentation mask every 50 frames, which is not what I am looking for.

    I can duplicate the segmentation 10 times to produce the desired output but I am not sure that is the right way to go.

    Or should I wait for the blog posts?

    • Jason Brownlee October 3, 2018 at 4:21 pm #

      I do have some posts scheduled using the conv2dlstm for time series, but not video.

      Nevertheless, I’d encourage you to get the model working first by any means, then make it work well.

      Include all 500 frames of video as time steps or just the first 200. Each video is then a sample, then you can treat the rows, cols and channels like any image.

      Once you have the model working, check if you need all frames, maybe only use every 5th or 20th frame or something. Evaluate the impact on the model skill.

      • Pelonomi Moiloa October 5, 2018 at 4:15 pm #

        Okay thanks. I will see what happens

      • gideon August 8, 2019 at 8:39 am #

        Hi there, could you send a link to your conv2dlstm for time series? I am looking through your book and googling for blog posts but I cant find it. I am specifically looking for an example for conv2dlstm as the encoder part of an encoder/decoder architecture.
        Also, as an aside, can these architecture types be made stateful?

        Thank you Jason

  29. bhb October 4, 2018 at 12:16 am #

    I tried to apply cnn+lstm+ctc for scanned text line recognition. would you recommend me any source for better understanding?

    • Jason Brownlee October 4, 2018 at 6:18 am #

      Nice work!

      Perhaps try some searching on scholar.google.com

  30. Rahul Krishnan October 17, 2018 at 8:34 pm #

    Awesome article as always. I would like to clear a question that came up. Do convolutionalLSTMs [https://github.com/keras-team/keras/blob/master/examples/conv_lstm.py] mean the same as convolutional neural networks followed by an LSTM. I understand you are trying to extrapolate features using the CNN before passing it on to a LSTM, so it should technically be the same?

    • Jason Brownlee October 18, 2018 at 6:27 am #

      No, a ConvLSTM is different from a CNN-LSTM.

      A ConvLSTM will perform convolutions as part of the inputs to the LSTM unit.

      A CNN-LSTM is a model architecture that has a CNN model for the input and an LSTM model to process input time steps processed by the CNN model.

  31. Leo December 13, 2018 at 2:04 pm #

    Hi Jason,

    Thank you for the article.

    I was hoping to get your inputs and advice on the model I’m trying to build.

    The goal of the model is to act as a PoS tagger using a combination of CNN and LSTM.
    CNN portion receives as input, word vector representations from a Glove embedding and hopefully learns information about the word/sequence.

    BiLSTM will then process the output from CNN.
    A TimeDistributed layer is added at the dense layer for prediction.

    The model trains without issues but in terms of performance, the metrics are worse than a pure LSTM model.

    Am I constructing the model wrongly?

    • Jason Brownlee December 14, 2018 at 5:28 am #

      It’s hard for me to say. Develop then evaluate the model, then use that as feedback as to whether the model is constructed well.

      • Leo December 15, 2018 at 2:08 pm #

        Thanks for the reply Jason.

        I have a few iterations of the model ranging from 1 CNN layer + 2 BLSTM layers to 3 CNN + 2 BLSTM.

        In all cases, just a pure 2 BLSTM model outperforms them.
        I’m kind of stuck, not sure if its a CNN or LSTM issue.

  32. Amira January 21, 2019 at 4:14 am #

    Thanks for the Tutorial, I want to ask about your your keras backend, is it tensorflow or Theano? Thanks

    • Jason Brownlee January 21, 2019 at 5:35 am #

      I currently use and recommend TensorFlow, but sometimes it can be challenging to install on some platforms, in that case I recommend Theano.

  33. Subbulakshmi February 14, 2019 at 9:47 pm #

    how do we feed the video frames as input to cnn+lstm model? Im currently working with that and unaware of how this could be done.Could you guide me on this?Basically i want to know regarding the input part of the model.

    • Jason Brownlee February 15, 2019 at 8:04 am #

      Each image is one step in a sequence of images (e.g. time steps), each sample is one sequence of images.

  34. Csaba Kertesz April 3, 2019 at 3:17 am #

    I read that you used LSTMs for different problems and you did not find them useful. Your article is about time series regression, but I would like to hear your opinion about time series classification. While reading the literature, I found RNNs/LSTMs to enhance a bit the accuracies in different domains, but I did not see many groundbreaking results with these networks. Do you have any experience if a windowing approach with MLP or CNN is also more useful than LSTM/RNN methods for time series classification?

  35. ranijoseph April 11, 2019 at 7:21 pm #

    thank you sir for these awesome tutorials,it have been a great help me to me…. i tried to implement CNN-lstm using keras but i am getting accuracy of only 0.5. Also accuracy not improving after few epochs….. please guide me sir

    from string import punctuation
    from os import listdir
    from numpy import array,shape
    from keras.preprocessing.text import Tokenizer
    from keras.preprocessing.sequence import pad_sequences
    from keras.models import Sequential
    from keras.layers import Dense,BatchNormalization
    from keras.layers import Flatten
    from keras.layers import Dropout, Activation
    from keras.layers import LSTM
    from keras.layers import Embedding
    from keras.layers.convolutional import Conv1D
    from keras.layers.convolutional import MaxPooling1D
    from keras.callbacks import EarlyStopping
    from History import LossHistory
    # load doc into memory
    def load_doc(filename):
    # open the file as read only
    file = open(filename, ‘r’)
    # read all text
    text = file.read()
    # close the file
    file.close()
    return text

    # turn a doc into clean tokens
    def clean_doc(doc, vocab):
    # split into tokens by white space
    tokens = doc.split()
    # remove punctuation from each token
    table = str.maketrans(”, ”, punctuation)
    tokens = [w.translate(table) for w in tokens]
    # filter out tokens not in vocab
    tokens = [w for w in tokens if w in vocab]
    tokens = ‘ ‘.join(tokens)
    return tokens

    # load all docs in a directory
    def process_docs(directory, vocab, is_trian):
    documents = list()
    # walk through all files in the folder
    for filename in listdir(directory):
    # skip any reviews in the test set
    if is_trian and filename.startswith(‘cv9’):
    continue
    if not is_trian and not filename.startswith(‘cv9’):
    continue
    # create the full path of the file to open
    path = directory + ‘/’ + filename
    # load the doc
    doc = load_doc(path)
    # clean doc
    tokens = clean_doc(doc, vocab)
    # add to list
    documents.append(tokens)
    return documents

    # load the vocabulary
    vocab_filename = ‘vocab.txt’
    vocab = load_doc(vocab_filename)
    vocab = vocab.split()
    vocab = set(vocab)

    # load all training reviews
    positive_docs = process_docs(‘txt_sentoken/pos’, vocab, True)
    negative_docs = process_docs(‘txt_sentoken/neg’, vocab, True)
    train_docs = negative_docs + positive_docs

    # create the tokenizer
    tokenizer = Tokenizer()
    # fit the tokenizer on the documents
    tokenizer.fit_on_texts(train_docs)

    # sequence encode
    encoded_docs = tokenizer.texts_to_sequences(train_docs)
    # pad sequences
    max_length = max([len(s.split()) for s in train_docs])
    Xtrain = pad_sequences(encoded_docs, maxlen=max_length, padding=’post’)
    # define training labels
    ytrain = array([0 for _ in range(900)] + [1 for _ in range(900)])
    # load all test reviews
    positive_docs = process_docs(‘txt_sentoken/pos’, vocab, False)
    negative_docs = process_docs(‘txt_sentoken/neg’, vocab, False)
    test_docs = negative_docs + positive_docs
    # sequence encode
    encoded_docs = tokenizer.texts_to_sequences(test_docs)
    # pad sequences
    Xtest = pad_sequences(encoded_docs, maxlen=max_length, padding=’post’)
    with open(“sentiment.txt”, “w”) as text_file:
    for p in test_docs: text_file.write(“%s \n” % p)

    # define test labels
    ytest = array([0 for _ in range(100)] + [1 for _ in range(100)])

    # define vocabulary size (largest integer value)
    vocab_size = len(tokenizer.word_index) + 1
    print (Xtrain)
    print(‘Build model…’)
    # print (max_length) # 1209
    # print (vocab_size) #13045
    # define model
    model = Sequential()
    model.add(Embedding(vocab_size, 100, input_length=max_length))
    model.add(Conv1D(filters=32, kernel_size=8, activation=’relu’))
    model.add(MaxPooling1D(pool_size=2))
    model.add(LSTM(100))
    model.add(BatchNormalization())
    model.add(Dropout(0.5))
    model.add(Dense(1))
    model.add(Activation(‘sigmoid’))

    model.compile(loss=’binary_crossentropy’,
    optimizer=’adam’,
    metrics=[‘accuracy’])
    early_stop = EarlyStopping(monitor=’val_loss’, patience=10)
    train_log = LossHistory()
    model.fit(array(Xtrain), array(ytrain),
    batch_size = 30,
    epochs=10,
    callbacks=[early_stop, train_log],
    validation_data=(array(Xtest), array(ytest)))
    score, acc = model.evaluate(array(Xtest), array(ytest), batch_size=30)
    model.save(‘cnn-lstm_model.h5’)
    # score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
    print(‘Test score:’, score)
    print(‘Test accuracy:’, acc)

  36. Sandipan Banerjee April 15, 2019 at 7:27 am #

    Hi Jason,

    What neural network model would you suggest for physical problems? For example: Problems related to Fluid Mechanics? Like the one in the video below shows fluid flow around a circle, and after a while it starts to produce vortices. Which NN architecture would be best suited for a problem like this where in we have say data for 50 time-steps, we train the model for 40 of them and we want to predict data for the next 10 time-steps and compare with the actual results. The data would include the velocity, vorticity and other physical parameters along the domain shown in the video in both x and y direction. I tried using ConvLSTM2D in Keras, but the results are not good.

    LInk of the video of the fluid flow problem I was talking about:
    https://www.youtube.com/watch?v=JgoHhKiQFKI

    • Jason Brownlee April 15, 2019 at 7:58 am #

      I recommend testing a suite of methods in order to discover what works best for your specific dataset.

  37. KK April 15, 2019 at 4:40 pm #

    Hi Dr. Jason,

    Excellent Post, Thanks for the sharing the same.

    I am currently working on traffic classification problem. It does seem like this approach would be ideal for the case.

    I would need your help in below points.
    1. I have implemented a traffic classification using normal CNN model (Transfer Learning using ResNet50)
    2. Time is important factor as I dont want the image to be classified like A B A B. (Ideal would be A A A A …….. B B B B B ……)
    3. Can I combine the LSTM model add the end of this model ?
    4. Will this approach make my model better in real world application?

    Thank you,
    KK

    • Jason Brownlee April 16, 2019 at 6:46 am #

      Yes, I recommend prototyping the model and evaluate its performance.

  38. Jordan Miller April 23, 2019 at 11:20 pm #

    How could one turn this into a hierarchical model? Both hierarchical in space (cnn) and time (lstm)? That way features over a larger area could be correlated with smaller features and extended temporal patterns could be correlated with shorter temporal patterns in these small and large feature spaces.

    would it necessarily be something like this:

    cnn -> lstm ->
    cnn -> lstm -> cnn -> lstm ->
    cnn -> lstm -> cnn -> lstm -> cnn -> lstm
    cnn -> lstm -> cnn -> lstm -> ^
    cnn -> lstm -> ^ ||
    || ||
    || ||
    || ||
    summary timeseires data ||
    ||
    ||
    ||
    summary summary timeseires data

    lots of small feature detection at the beginning which feed to a set of layers that convolve over larger spacial and temporal dimentions, and so on?

    • Jason Brownlee April 24, 2019 at 8:02 am #

      A Conv-LSTM hybrid model is sufficient, also a ConvLSTM model.

      Both will process time steps of spatial data.

      You can then use one model for each level of detail, and use a model to combine their interpretations.

  39. Emre April 26, 2019 at 4:07 am #

    Hello Jason, I used similar model like yours. I used 10 frames per cnn-lstm. My shape is (Sample,10,90,90,1) —> (sample,time,img-shape,img-shape,channel)
    The problem is I am getting 10 results for each prediction and I don’t know why. Also these 10 results are different. Do you have any idea?
    Thanks
    Emre

    • Jason Brownlee April 26, 2019 at 8:37 am #

      Unless you use an encoder-decoder, you will get one output per input time step.

      • Emre April 26, 2019 at 11:59 pm #

        Only used one hot encoder for classes ( I have 5 different classes). And I have 50 results (not 10) per each time actually. Should I use encode-decoder for model also? Or how can i solve this problem?
        Regards,
        Emre

  40. stone May 5, 2019 at 1:31 pm #

    Have you solved the problem? I also had the same error.

  41. KK May 6, 2019 at 3:03 pm #

    Hi Sir,

    Thanks for the excellent tutorial.
    I am working on an Image classification problem where I think CNN+LSTM would be very much useful as I am feeding the image frame fetched from a video.
    In this case, how do I need to arrange my image frames?

    If it is a general image classification, I would create a folder for each class. But in this lets take I take 3 video sequences which belongs to one class. How I have to arrange the image frames in training and testing?

    Thank you,
    KK

    • Jason Brownlee May 7, 2019 at 6:09 am #

      Images must be arranged in temporal order and into sequences.

      You might want to write a custom data generator to yield each sequence or batch of sequences of images.

  42. Emre May 9, 2019 at 2:25 am #

    Hey Jason, how we can do ImageDataGenerator to the images? Becayse of the shape of the images I am getting error.

    ValueError: Input to .fit() should have rank 4. Got array with shape: (11194, 10, 90, 90, 1)

    Thanks,

    Emre

  43. Josh Ryan May 16, 2019 at 12:43 pm #

    I am attempting to build a simple version of the model described in this paper for object detection in video: https://arxiv.org/abs/1903.10172. The architecture is a convNet feature extractor which feeds it’s output to a convLSTM which feeds its output to SSD detection layers. The first step though is just to pretrain convNet+convLSTM end to end on image classification.

    The model you’ve described here seems like the proper way to combine the convNet and convLSTM, but I’m confused about input shape. The TimeDistributed layers require a 3D input, with one dimension being time. I understand how I could do this at train time, but at inference time I do not want to feed a 3D tensor to the model. I just want it to process one image at a time. I feel like I’m missing a small detail, and I’m hoping you can help. Thanks!

    P.S. The authors of the above mentioned paper have code in a tensorflow/models/research/lstm_object_detection repo, but it seems their code for this version of their work (last updated about a week ago) is incomplete and is very confusing to me.

    • Jason Brownlee May 16, 2019 at 2:25 pm #

      Hmm, I’m not familiar with the model.

      Generally, this will help in understanding input shape:
      https://machinelearningmastery.com/faq/single-faq/what-is-the-difference-between-samples-timesteps-and-features-for-lstm-input

      And, the CNN-lstms and convlstms in this post may be instructive:
      https://machinelearningmastery.com/how-to-develop-lstm-models-for-time-series-forecasting/

      Generally, a CNN-LSTM expects one feature to be an image and for a sample to be a sequence of images, e.g. something like [?, timesteps, rows, cols, channels].

      Does that help?

      • Josh Ryan May 16, 2019 at 11:07 pm #

        Thanks for the quick reply. I probably shouldn’t have mentioned that specific model, as it’s not really critical to the question. I was just trying to provide some context.

        After reading the first link you shared, it seems like what I want to do is have an input of only one timestep, but maintain the internal state over multiple timesteps with the stateful = true parameter. But in that case it doesn’t seem there’s a need for the TimeDistribted layer, except maybe to start out with the input shape that the lstm expects. Otherwise, I could probably just reshape the convNet output to have a time dimension before feeding to convLSTM. Does that sound right to you?

        Thanks again! Hopefully I’ll get my model built today.

        • Jason Brownlee May 17, 2019 at 5:54 am #

          Perhaps.

          There are many ways to frame a give problem. I’d encourage you to brainstorm a couple, then test each. It will help you learn more about the data and the models, and also find what works for your specific dataset.

  44. Atefeh May 29, 2019 at 12:25 pm #

    hello Mr Brownlee

    I want to use CNN lstm for image classsification.
    i used your CNN lstm code above but i faced to the
    “ValueError: The first layer in a Sequential model must get an input_shape or batch_input_shape argument.” error for the line “model.add(TimeDistributed(cnn,…)”
    how can i find what is the input shape of this layer?is it my input image shape or the CNN output shape(feature vectors getting from CNN)?
    would you please guide me how to fill the brackets of the LSTM model code?for example how to choose the elements of the “model.add(LSTM(…))?

    thanks a lot

  45. Atefeh June 3, 2019 at 2:31 pm #

    hello Mr Brownlee
    thank you for your guidance. i read the link ,but still my cnnLSTM does not work. and the error for timeDistributed code is accured.
    i search a lot to solve this problem and finally i have found a suggestion, “using r1.4.0 ,an API documentation for tensorflow”.
    my tensorflow version is 1.4.0. and now my question is how can i use this API, i mean should i install it instead of my tensorflow or should i copy it somewhere or something else.
    i send you my code(the input images are 28*28)
    please help me again,Mr Brownlee.

    thank you

    import numpy
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.layers import Dropout
    from keras.layers import Flatten
    from keras.layers.convolutional import Conv2D
    from keras.layers.convolutional import MaxPooling2D
    from keras.layers import LSTM
    from keras.layers import TimeDistributed
    from keras.utils import np_utils
    from keras import backend as K
    K.set_image_dim_ordering(‘th’)

    # fix random seed for reproducibility
    seed = 7
    numpy.random.seed(seed)

    # reshape to be [samples][pixels][width][height]
    X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype(‘float32’)
    X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype(‘float32′)

    # normalize inputs from 0-255 to 0-1
    X_train = X_train / 255
    X_test = X_test / 255
    # one hot encode outputs
    y_train = np_utils.to_categorical(y_train)
    y_test = np_utils.to_categorical(y_test)
    num_classes = y_test.shape[1]
    num_timesteps = 10 # length of sequence

    # define CNN model
    model = Sequential()
    model.add(TimeDistributed(Conv2D(32, (3, 3), input_shape=(1, 28,28), activation=’relu’)))
    model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
    model.add(TimeDistributed(Flatten()))
    # define LSTM model
    model.add(LSTM(num_timesteps))
    model.add(Dense(num_classes, activation=’softmax’))
    # Compile model
    model.compile(loss=’categorical_crossentropy’,optimizer=’adam’,metrics=[‘accuracy’])
    #return model

    # Fit the model
    model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)
    # Final evaluation of the model
    scores = model.evaluate(X_test, y_test, verbose=0)
    print(“CNN Error: %.2f%%” % (100-scores[1]*100))

    ———————————————-
    ValueError: The first layer in a Sequential model must get an input_shape or batch_input_shape argument.

    • Jason Brownlee June 3, 2019 at 2:36 pm #

      Perhaps try updating to the latest version of tensorflow, e.g. 1.13.

  46. Atefeh June 4, 2019 at 12:54 pm #

    thanks alot again
    but I typed the below codes in my Anaconda prompt ; and i faced to a exceptions.
    what is wrong?
    how can i update my tensorflow?

    (C:\Users\ASUS\Anaconda3) C:\Users\ASUS>python
    Python 3.6.3 |Anaconda custom (64-bit)| (default, Oct 15 2017, 03:27:45) [MSC v.1900 64 bit (AMD64)] on win32
    Type “help”, “copyright”, “credits” or “license” for more information.

    (C:\Users\ASUS\Anaconda3) C:\Users\ASUS>pip install
    You must give at least one requirement to install (see “pip help install”)
    You are using pip version 9.0.1, however version 19.1.1 is available.
    You should consider upgrading via the ‘python -m pip install –upgrade pip’ command.

    (C:\Users\ASUS\Anaconda3) C:\Users\ASUS>pip install -U –no-deps keras tensorflow theano scikit-learn
    Exception:
    Traceback (most recent call last):
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\basecommand.py”, line 215, in main
    status = self.run(options, args)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\commands\install.py”, line 335, in run
    wb.build(autobuilding=True)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\wheel.py”, line 749, in build
    self.requirement_set.prepare_files(self.finder)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\req\req_set.py”, line 380, in prepare_files
    ignore_dependencies=self.ignore_dependencies))
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\req\req_set.py”, line 487, in _prepare_file
    req_to_install, finder)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\req\req_set.py”, line 428, in _check_skip_installed
    req_to_install, upgrade_allowed)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\index.py”, line 465, in find_requirement
    all_candidates = self.find_all_candidates(req.name)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\index.py”, line 423, in find_all_candidates
    for page in self._get_pages(url_locations, project_name):
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\index.py”, line 568, in _get_pages
    page = self._get_page(location)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\index.py”, line 683, in _get_page
    return HTMLPage.get_page(link, session=self.session)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\index.py”, line 811, in get_page
    inst = cls(resp.content, resp.url, resp.headers)
    File “C:\Users\ASUS\Anaconda3\lib\site-packages\pip\index.py”, line 731, in __init__
    namespaceHTMLElements=False,
    TypeError: parse() got an unexpected keyword argument ‘transport_encoding’
    You are using pip version 9.0.1, however version 19.1.1 is available.
    You should consider upgrading via the ‘python -m pip install –upgrade pip’ command.

  47. Atefeh June 4, 2019 at 3:08 pm #

    i succeed to update my tensorflow.
    Now in my Anaconda /lib/site-package i have 2 tensorflow folders:
    tensorflow 1.4.0 and tensorflow 1.10.0
    should i delete tensorflow 1.4.0?

    • Jason Brownlee June 5, 2019 at 8:32 am #

      Perhaps. Also, perhaps try an update to tensorflow 1.13, the latest version.

  48. Atefeh June 5, 2019 at 1:49 pm #

    thank you
    Mr.Brownlee
    I’m so sorry for asking a lot,
    here is the only reference i could trust on it.

    After updating, my Anaconda prompt does not work.
    i mean it doesn’t stay on the desktop.

    these 4 line accure and then it would be closed

    C:\Users\ASUS>python C:\Users\ASUS\Anaconda3\etc\keras\load_config.py 1>temp.txt

    C:\Users\ASUS>set /p KERAS_BACKEND= 0del temp.txt

    C:\Users\ASUS>python -c “import keras” 1>nul 2>&1

    • Jason Brownlee June 5, 2019 at 2:37 pm #

      Perhaps run the code as-is without redirecting the output?

  49. Atefeh June 6, 2019 at 2:07 am #

    Hello Mr.Brownlee
    I unstalled the Anaconda which was in my system completely ,and again from beginning i have installed Anaconda that all of its libraries are in higher version.e.g tensorflow is 1.13.0 and so on.
    Now the problem is that when i run my previous codes i faced to a error below:

    import numpy as np
    import scipy
    import scipy.misc

    n_images =16416 #Example value
    image_names = [“traincharactor2/image_{0}.bmp”.format(k) for k in range(n_images)]

    training_set = []
    for img in image_names:
    training_set += [scipy.misc.imread(name=img)]

    X_train = np.array(training_set).reshape(16416, 28,28);

    C:\Users\ASUS\Anaconda3\lib\site-packages\ipykernel_launcher.py:10: DeprecationWarning: imread is deprecated!
    imread is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.
    Use imageio.imread instead.
    # Remove the CWD from sys.path while we load stuff.

    I added ” import imageio”
    but the below error accure

    import numpy as np
    import scipy
    import scipy.misc
    import imageio

    n_images =16416 #Example value
    image_names = [“traincharactor2/image_{0}.bmp”.format(k) for k in range(n_images)]

    training_set = []
    for img in image_names:
    training_set += [imageio.imread(‘name=img’)]

    X_train = np.array(training_set).reshape(16416, 28,28);

    FileNotFoundError: No such file: ‘C:\Users\ASUS\name=img’

    i triead many syntax for ” training_set += [imageio.imread(‘name=img’)]”
    but nothing work

    • Jason Brownlee June 6, 2019 at 6:37 am #

      I think you need to change the name of the file that you’re loading.

  50. Atefeh June 8, 2019 at 1:23 am #

    Hello Mr.Brownlee

    I could solve the problem for readin g images.
    I start to run the LSTM_CNN for image classification.
    like befor,i faced to this error

    ValueError: Dimension must be 5 but is 4 for ‘time_distributed_4/conv2d_5/transpose’ (op: ‘Transpose’) with input shapes: [?,10,28,28,1], [4].

    here is my code(images are 28*28)

    import numpy
    #from keras.datasets import mnist
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.layers import Dropout
    from keras.layers import Flatten
    from keras.layers.convolutional import Conv2D
    from keras.layers.convolutional import MaxPooling2D
    from keras.layers import LSTM
    from keras.layers import TimeDistributed
    from keras.utils import np_utils
    from keras import backend as K
    K.set_image_dim_ordering(‘th’)

    # fix random seed for reproducibility
    seed = 7
    numpy.random.seed(seed)

    # reshape to be [samples][pixels][width][height]
    X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype(‘float32’)
    X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype(‘float32′)

    # normalize inputs from 0-255 to 0-1
    X_train = X_train / 255
    X_test = X_test / 255
    # one hot encode outputs
    y_train = np_utils.to_categorical(y_train)
    y_test = np_utils.to_categorical(y_test)
    num_classes = y_test.shape[1]
    num_timesteps = 10 # length of sequence

    # define CNN model
    cnn = Sequential()
    cnn.add(Conv2D(32, (5, 5), input_shape=(1, 28,28), activation=’relu’))
    cnn.add(MaxPooling2D(pool_size=(2, 2)))
    cnn.add(Flatten())
    # define LSTM model
    model = Sequential()
    model.add(TimeDistributed(cnn, input_shape=(None, num_timesteps, 28, 28,1)))
    model.add(LSTM(num_timesteps))
    model.add(Dense(num_classes, activation=’softmax’))
    model.compile(loss=’categorical_crossentropy’,optimizer=’adam’,metrics=[‘accuracy’])

    # Fit the model
    model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)
    # Final evaluation of the model
    scores = model.evaluate(X_test, y_test, verbose=0)
    print(“CNN Error: %.2f%%” % (100-scores[1]*100))

    i tries alot of way for changing the input_shape, but nothing work.
    would you please help me to say what is wrong in the code?!

    i really need this code for my study

    thanks alot

  51. Atefeh June 15, 2019 at 1:23 pm #

    Hello Mr Brownlee
    I couldn’t solve my problem yet.
    now I want to save the features extracted by a CNN as a vector.
    how can i do it?
    i want first save cnn features and then feed them to a LSTM.
    ———
    another question is about my database,
    for implementing a CNN_LSTM , my training dataset is a folder that the images for all the classes are arrange respectively.the test folder is so as training one.
    is it ok for your suggested code above?

  52. Rohit Halder June 18, 2019 at 4:31 am #

    i wrapped everything with a timedistributed()
    still i am having the error
    “input tensor must have rank 4”
    what shall i do ?

  53. BHAVI June 27, 2019 at 8:58 pm #

    Can we classify IRIS dataset with CNN or RNN?

  54. Jorge Baez July 9, 2019 at 4:10 pm #

    Hello Jason,

    I am taking my first steps with lstm and I am facing a strange situation while trying to fit my model. Could you please check my description of the task and correct me if you see any problem? (I am using R, but the code is almost the same 🙂 )

    # Context of the problem / data -generators- used
    To give you some context, I have dataset of single channel, 2D arrays. A sequence of these 2D arrays should be good enough to create a classifier. The shape of one of these arrays is (180,360,1), and for each day, I have 24 of them.
    In this scenario, as far as I can understand, I am trying to build a many-to-one lstm model.

    I created a data generator that returns an array of dimensions (batch_size,time_steps,180,360,1).

    # Results of test models:
    (1)
    If I use a “simple” model, without using a LSTM model, but “considering all timesteps together”:

    batch_size <- 1 # a batch is just one day
    time_steps <- 24 # all the 2D arrays of the day are sent

    model %
    layer_flatten(input_shape = c(time_steps, 180, 360, 1)) %>%
    layer_dense(units = 32, activation = “relu”) %>%
    layer_dense(units = 16, activation = “relu”) %>%
    layer_dense(units = 1, activation = “sigmoid”)

    model %>% compile(
    optimizer = “rmsprop”,
    loss = “binary_crossentropy”,
    metrics = c(“accuracy”)
    )

    I get a val_acc of ~ 0.76 and a val_loss of ~ 0.56

    (2)
    If use a CNN + LSTM, using batches of 3 days, and sending for each day 48 time steps (looking 2 days back)

    batch_size <- 3 # the batch is of three days
    time_steps <- 48 # all the arrays of a day and the day before

    model %
    time_distributed(
    layer_conv_2d(filters = 32, kernel_size = c(5,5), activation = “relu”,
    kernel_initializer = ‘he_uniform’),
    input_shape = list(24*lookback_d, 180, 360,1)
    ) %>%
    time_distributed(
    layer_max_pooling_2d(pool_size = c(3,3))
    ) %>%
    time_distributed(
    layer_conv_2d(filters = 32, kernel_size = c(5,5), activation = “relu”,
    kernel_initializer = ‘he_uniform’)
    ) %>%
    time_distributed(
    layer_flatten()
    ) %>%
    layer_gru(units = 32, dropout = 0.1, recurrent_dropout = 0.5) %>%
    layer_dense(units = 16, activation = “relu”) %>%
    layer_dense(units = 1, activation = “sigmoid”)

    model %>% compile(
    optimizer = “rmsprop”,
    loss = “binary_crossentropy”,
    metrics = c(“accuracy”)
    )

    I get the same val_acc of ~ 0.75 and a val_loss of ~ 0.55

    # Questions:
    Could it be possible that these results are almost the same because further tuning is needed in the CNN+LSTM model? Or could it be because the “basic model” created is just very difficult to improve?

    Since increasing the amount of days sent on each batch, or the amount of time_steps sent for each day, generates a ResourceExhaustedError (OOM when allocating tensor with shape …. -This occurs in the first conv_2d layer-), do you think I should modify the shape of the data sent to the model with the data generator?

    Thank you for your help!

    • Jason Brownlee July 10, 2019 at 8:02 am #

      Sorry, i don’t have the capacity to debug your code/problem.

      Perhaps try posting to stackoverflow?

  55. Firthous August 7, 2019 at 12:05 pm #

    can we use CNN with LSTM for signal data for prediction

  56. SOUMYA KANTA DAS September 4, 2019 at 4:56 pm #

    Can it be implemented for Temporal segmentation of time-series of satellite imagery?

    • Jason Brownlee September 5, 2019 at 6:48 am #

      Sorry, Id on’t know what that problem is exactly?

  57. Suraj September 5, 2019 at 11:17 pm #

    Great post Jason. Can we use ConvLSTM to extract human emotions from a series of frames consisting faces instead of face from single frame ? I am working on a practice project to extract human emotions from the video of my kids, the regular CNN spits out human emotions on each frame but I have seen the emotion to peak out at the last frame in a series of frames.

    • Jason Brownlee September 6, 2019 at 5:02 am #

      Perhaps try it and see?

      • Suraj September 6, 2019 at 11:04 am #

        Thanks Jason for replying. I am starter with respect to machine learning programming, it took me a lot of effort to figure out a CNN was not helping me to achieve my goal. A general algorithm of how to do it will be of immense help to keep me focused in solving my mvp in my project.

  58. Jonathan Taylar September 23, 2019 at 4:42 pm #

    Can I use 2D-CNN for time series prediction? Your response will be valuable to my research

  59. SRK September 30, 2019 at 3:33 pm #

    Thanks for the great tutorial.
    Please tell me how to use 2D CNN for spatio temporal time series prediction. Is there any tutorial for it?

    • Jason Brownlee October 1, 2019 at 6:48 am #

      Thanks for the suggestion, I hope to cover it in the future.

  60. decoder October 2, 2019 at 3:56 pm #

    I am designing a spatio temporal multivariate 2D CNN LSTM

    13974 sequences and 100 timestamps of 6 locations and 5 variables(features)

    train input shape : (13974, 100, 6, 5)
    train output shape : (13974, 1, 6, 5)
    test input shape : (3494, 100, 6, 5)
    test output shape : (3494, 1, 6, 5)

    model = Sequential()
    model.add(TimeDistributed(Conv2D(32, (3, 3),
    padding=’same’),
    input_shape=(100, 6, 5,1)))
    model.add(TimeDistributed(Activation(‘relu’)))
    model.add(TimeDistributed(Conv2D(32, (3, 3))))
    model.add(TimeDistributed(Activation(‘relu’)))
    model.add(TimeDistributed(MaxPooling2D(pool_size=(2, 2))))
    model.add(TimeDistributed(Dropout(0.25)))

    model.add(TimeDistributed(Flatten()))
    model.add(TimeDistributed(Dense(512)))

    model.add(TimeDistributed(Dense(35, name=”first_dense_flow” )))

    model.add(LSTM(20, return_sequences=True, name=”lstm_layer_flow”));
    model.add(TimeDistributed(Dense(101), name=” time_distr_dense_one_ flow “))

    model.add(GlobalAveragePooling1D(name=”global_avg_flow”))
    model.compile(loss=’mae’, optimizer=’adam’, metrics=[‘accuracy’]) model.fit(train_input,train_output,epochs=50,batch_size=60)

    is my model able to predict the output (13974, 1, 6, 5)

    please correct my .

  61. areesha October 4, 2019 at 9:22 pm #

    hi,
    I followed your post. built CNN+LSTM model, it is compiling fine.
    But their is a issue in fit_generator on training

    error is:

    Expected 5 dimension for the input but got only four

    i have provided images through Image datagenerator having input of dimension 64x64x3 with a batch size of 4

    therefore, input fit_generator is taking is: (4,64,64,3)

    • Jason Brownlee October 6, 2019 at 8:10 am #

      It suggests a mismatch between the data and the model. You can change one or the other.

  62. George October 16, 2019 at 1:48 am #

    Hi Jason: Big fan here. Question: you showed a CNN-LSTM architecture here that could be used for analyzing video data. But how do we pre-process the video data for input into this architecture? I have multiple clips of 2 second videos of human motion with the labels being 0 or 1, I have extracted some of the frames but don’t know how to input them into this NN architecture…

    • George October 16, 2019 at 2:39 am #

      From reading the comments above, it seems like quite a few people are interested in CNN-LSTM for video data! But It is still unclear how that would work… specifically how to structure the project, and how to preprocess the raw data for input… maybe this could be a future tutorial? Thanks Jason 🙂

      • Jason Brownlee October 16, 2019 at 8:11 am #

        I give a mock video example in the LSTM book.

        It is an array of images, each image is a timestep.

    • Jason Brownlee October 16, 2019 at 8:10 am #

      Thanks George.

      Good question. I don’t have a worked example, sorry. Trim frames down to a min set (a few per second?) then perhaps use pixel scaling on the image data, just like image classification?

Leave a Reply