How to Use Small Experiments to Develop a Caption Generation Model in Keras

Caption generation is a challenging artificial intelligence problem where a textual description must be generated for a photograph.

It requires both methods from computer vision to understand the content of the image and a language model from the field of natural language processing to turn the understanding of the image into words in the right order. Recently, deep learning methods have achieved state of the art results on examples of this problem.

It can be hard to develop caption generating models on your own data, primarily because the datasets and the models are so large and take days to train. An alternative approach is to explore model configurations with a small sample of the fuller dataset.

In this tutorial, you will discover how you can use a small sample of a standard photo captioning dataset to explore different deep model designs.

After completing this tutorial, you will know:

  • How to prepare data for photo captioning modeling.
  • How to design a baseline and test harness to evaluate the skill of models and control for their stochastic nature.
  • How to evaluate properties like model skill, feature extraction models, and word embeddings in order to lift model skill.

Let’s get started.

How to Use Small Experiments to Develop a Caption Generation Model in Keras

How to Use Small Experiments to Develop a Caption Generation Model in Keras
Photo by Per, some rights reserved.

Tutorial Overview

This tutorial is divided into 6 parts; they are:

  1. Data Preparation
  2. Baseline Caption Generation Model
  3. Network Size Parameters
  4. Configuring the Feature Extraction Model
  5. Word Embedding Models
  6. Analysis of Results

Python Environment

This tutorial assumes you have a Python SciPy environment installed, ideally with Python 3.

You must have Keras (2.0 or higher) installed with either the TensorFlow or Theano backend.

The tutorial also assumes you have scikit-learn, Pandas, NumPy, and Matplotlib installed.

If you need help with your environment, see this tutorial:

I recommend running the code on a system with a GPU.

You can access GPUs cheaply on Amazon Web Services. Learn how in this tutorial:

Let’s dive in.

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Data Preparation

First, we need to prepare the dataset for training the model.

We will use the Flickr8K dataset that is comprised of a little more than 8,000 photographs and their descriptions.

You can download the dataset from here:

Unzip the photographs and descriptions into your current working directory into Flicker8k_Dataset and Flickr8k_text directories respectively.

There are two parts to the data preparation, they are:

  1. Preparing the Text
  2. Preparing the Photos

Preparing the Text

The dataset contains multiple descriptions for each photograph and the text of the descriptions requires some minimal cleaning.

First, we will load the file containing all of the descriptions.

Each photo has a unique identifier. This is used in the photo filename and in the text file of descriptions. Next, we will step through the list of photo descriptions and save the first description for each photo. Below defines a function named load_descriptions() that, given the loaded document text, will return a dictionary of photo identifiers to descriptions.

Next, we need to clean the description text.

The descriptions are already tokenized and easy to work with. We will clean the text in the following ways in order to reduce the size of the vocabulary of words we will need to work with:

  • Convert all words to lowercase.
  • Remove all punctuation.
  • Remove all words that are one character or less in length (e.g. ‘a’).

Below defines the clean_descriptions() function that, given the dictionary of image identifiers to descriptions, steps through each description and cleans the text.

Finally, we save the dictionary of image identifiers and descriptions to a new file named descriptions.txt, with one image identifier and description per line.

Below defines the save_doc() function that given a dictionary containing the mapping of identifiers to descriptions and a filename, saves the mapping to file.

Putting this all together, the complete listing is provided below.

Running the example first prints the number of loaded photo descriptions (8,092) and the size of the clean vocabulary (4,484 words).

The clean descriptions are then written to ‘descriptions.txt‘. Taking a look in the file, we can see that the descriptions are ready for modeling.

Taking a look in the file, we can see that the descriptions are ready for modeling.

Preparing the Photos

We will use a pre-trained model to interpret the content of the photos.

There are many models to choose from. In this case, we will use the Oxford Visual Geometry Group or VGG model that won the ImageNet competition in 2014. Learn more about the model here:

Keras provides this pre-trained model directly. Note, the first time you use this model, Keras will download the model weights from the Internet, which are about 500 Megabytes. This may take a few minutes depending on your internet connection.

We could use this model as part of a broader image caption model. The problem is, it is a large model and running each photo through the network every time we want to test a new language model configuration (downstream) is redundant.

Instead, we can pre-compute the “photo features” using the pre-trained model and save them to file. We can then load these features later and feed them into our model as the interpretation of a given photo in the dataset. It is no different to running the photo through the full VGG model, it is just that we will have done it once in advance.

This is an optimization that will make training our models faster and consume less memory.

We can load the VGG model in Keras using the VGG class. We will load the model without the top; this means without the layers at the end of the network that are used to interpret the features extracted from the input and turn them into a class prediction. We are not interested in the image net classification of the photos and we will train our own interpretation of the image features.

Keras also provides tools for reshaping the loaded photo into the preferred size for the model (e.g. 3 channel 224 x 224 pixel image).

Below is a function named extract_features() that given a directory name will load each photo, prepare it for VGG and collect the predicted features from the VGG model. The image features are a 3-dimensional array with the shape (7, 7, 512).

The function returns a dictionary of image identifier to image features.

We can call this function to prepare the photo data for testing our models, then save the resulting dictionary to a file named ‘features.pkl‘.

The complete example is listed below.

Running this data preparation step may take a while depending on your hardware, perhaps one hour on the CPU with a modern workstation.

At the end of the run, you will have the extracted features stored in ‘features.pkl‘ for later use.

Baseline Caption Generation Model

In this section, we will define a baseline model for generating captions for photos and how to evaluate it so that it can be compared to variations on this baseline.

This section is divided into 5 parts:

  1. Load Data.
  2. Fit Model.
  3. Evaluate Model.
  4. Complete Example
  5. “A” versus “A” Test
  6. Generate Photo Captions

1. Load Data

We are not going to fit the model on all of the caption data, or even on a large sample of the data.

In this tutorial, we are interested in quickly testing a suite of different configurations of a caption model to see what works on this data. That means we need the evaluation of one model configuration to happen quickly. Toward this end, we will train the models on 100 photographs and captions, then evaluate them on both the training dataset and on a new test set of 100 photographs and captions.

First, we need to load a pre-defined subset of photographs. The provided dataset has separate sets for train, test, and development, which are really just different groups of photo identifiers. We will load the development set and use the first 100 identifiers for train and the second 100 (e.g. from 100 to 200) as the test set.

The function load_set() below will load a pre-defined set of identifiers, and we will call it with the ‘Flickr_8k.devImages.txt‘ filename as an argument.

Next, we need to split the set into train and test sets.

We will start by ordering the identifiers by sorting them to ensure we always split them consistently across machines and runs, then take the first 100 for train and the next 100 for test.

The train_test_split() function below will create this split given the loaded set of identifiers as input.

Now, we can load the photo descriptions using the pre-defined set of train or test identifiers.

Below is the function load_clean_descriptions() that loads the cleaned text descriptions from ‘descriptions.txt‘ for a given set of identifiers and returns a dictionary of identifier to text.

The model we will develop will generate a caption given a photo, and the caption will be generated one word at a time. The sequence of previously generated words will be provided as input. Therefore, we will need a “first word” to kick-off the generation process and a ‘last word‘ to signal the end of the caption. We will use the strings ‘startseq‘ and ‘endseq‘ for this purpose.

Next, we can load the photo features for a given dataset.

Below defines a function named load_photo_features() that loads the entire set of photo descriptions, then returns the subset of interest for a given set of photo identifiers. This is not very efficient as the loaded dictionary of all photo features is about 700 Megabytes. Nevertheless, this will get us up and running quickly.

Note, if you have a better approach, share it in the comments below.

We can pause here and test everything developed so far.

The complete code example is listed below.

Running this example first loads the 1,000 photo identifiers in the development dataset. A train and test set is selected and used to filter the set of clean photo descriptions and prepared image features.

We are nearly there.

The description text will need to be encoded to numbers before it can be presented to the model as in input or compared to the model’s predictions.

The first step in encoding the data is to create a consistent mapping from words to unique integer values. Keras provides the Tokenizer class that can learn this mapping from the loaded description data.

Below defines the create_tokenizer() that will fit a Tokenizer given the loaded photo description text.

We can now encode the text.

Each description will be split into words. The model will be provided one word and the photo and generate the next word. Then the first two words of the description will be provided to the model as input with the image to generate the next word. This is how the model will be trained.

For example, the input sequence “little girl running in field” would be split into 6 input-output pairs to train the model:

Later when the model is used to generate descriptions, the generated words will be concatenated and recursively provided as input to generate a caption for an image.

The function below named create_sequences() given the tokenizer, a single clean description, the features for a photo, and the maximum description length will prepare a set of input-output pairs for training a model. Calling this function will return X1 and X2 for the arrays of image data and input sequence data and the y value for the output word.

The input sequences are integer encoded and the output word is one-hot encoded to represent the probability distribution of the expected word across the whole vocabulary of possible words.

2. Fit Model

We are nearly ready to fit the model.

Parts of the model have already been discussed, but let’s re-iterate.

The model is based on the example laid out in the paper “Show and Tell: A Neural Image Caption Generator“, 2015.

The model involves three parts:

  • Photo Feature Extractor. This is a 16-layer VGG model pre-trained on the ImageNet dataset. We have pre-processed the photos with a the VGG model (without the top) and will use the extracted features predicted by this model as input.
  • Sequence Processor. This is a word embedding layer for handling the text input, followed by an LSTM layer. The LSTM output is interpreted by a Dense layer one output at a time.
  • Interpreter (for lack of a better name). Both the feature extractor and sequence processor output a fixed-length vector that is the length of a maximum sequence. These are concatenated together and processed by an LSTM and Dense layer before a final prediction is made.

A conservative number of neurons is used in the base model. Specifically, a 128 Dense layer after the feature extractor, a 50-dimensionality word embedding followed by a 256 unit LSTM and 128 neuron Dense after the sequence processor, and finally a 500 unit LSTM followed by a 500 neuron Dense at the end of the network.

The model predicts a probability distribution across the vocabulary, therefore a softmax activation function is used and a categorical cross entropy loss function is minimized while fitting the network.

The function define_model() defines the baseline model, given the size of the vocabulary and the maximum length of photo descriptions. The Keras functional API is used to define the model as it provides the flexibility needed to define a model that takes two input streams and combines them.

To get a sense for the structure of the model, specifically the shapes of the layers, see the summary listed below.

We also create a plot to visualize the structure of the network that better helps understand the two streams of input.

Plot of the Baseline Captioning Deep Learning Model

Plot of the Baseline Captioning Deep Learning Model

We will train the model using a data generator. This is strictly not required given that the captions and extracted photo features can probably fit into memory as a single dataset. Nevertheless, it is good practice for when you come to train the final model on the entire dataset.

A generator will yield a result when called. In Keras, it will yield a single batch of input-output samples that are used to estimate the error gradient and update the model weights.

The function data_generator() defines the data generator, given a dictionary of loaded photo descriptions, photo features, the tokenizer for integer encoding sequences, and the maximum sequence length in the dataset.

The generator loops forever and keeps yielding batches of input-output pairs when asked. We also have a n_step parameter that allows us to tune how many images worth of input-output pairs to generate for each batch. The average sequence has 10 words, that is 10 input-output pairs, and a good batch size might be 30 samples, which is about 2-to-3 images worth.

The model can be fit by calling fit_generator() and passing it to the data generator, along with all of the parameters needed. When fitting the model, we can also specify the number of batches to run per epoch and the number of epochs.

For these experiments, we will use 2 images per batch, 50 batches (or 100 images) per epoch, and 50 training epochs. You can experiment with different configurations in your own experiments.

3. Evaluate Model

Now that we know how to prepare the data and define a model, we must define a test harness to evaluate a given model.

We will evaluate a model by training it on the dataset, generating descriptions for all photos in the training dataset, evaluating those predictions with a cost function, and then repeating this evaluation process multiple times.

The outcome will be a distribution of skill scores for the model that we can summarize by calculating the mean and standard deviation. This is the preferred way to evaluate deep learning models. See this post:

First, we need to be able to generate a description for a photo using a trained model.

This involves passing in the start description token ‘startseq‘, generating one word, then calling the model recursively with generated words as input until the end of sequence token is reached ‘endseq‘ or the maximum description length is reached.

The function below named generate_desc() implements this behavior and generates a textual description given a trained model, and a given prepared photo as input. It calls the function word_for_id() in order to map an integer prediction back to a word.

We will generate predictions for all photos in the training dataset and in the test dataset.

The function below named evaluate_model() will evaluate a trained model against a given dataset of photo descriptions and photo features. The actual and predicted descriptions are collected and evaluated collectively using the corpus BLEU score that summarizes how close the generated text is to the expected text.

BLEU scores are used in text translation for evaluating translated text against one or more reference translations. We do in fact have access to multiple reference descriptions for each image that we could compare to, but for simplicity, we will use the first description for each photo in the dataset (e.g. the cleaned version).

You can learn more about the BLEU score here:

The NLTK Python library implements the BLEU score calculation in the corpus_bleu() function. A higher score close to 1.0 is better, a score closer to zero is worse.

Finally, all we need to do is define, fit, and evaluate the model multiple times in a loop then report the final average score.

Ideally, we would repeat the experiment 30 times or more, but this will take too long for our small test harness. Instead, will evaluate the model 3 times. It will be faster, but the mean score will have higher variance.

Below defines the model evaluation loop. At the end of the run, the distribution of BLEU scores for the train and test sets are saved to a file.

We parameterize the run as follows, allowing us to name each run and save the result to separate files.

4. Complete Example

The complete example is listed below.

Running the example first prints summary statistics for the loaded training data.

The example should take about 20 minutes on GPU hardware, a little longer on CPU hardware.

At the end of the run, a mean BLEU of 0.06 is reported on the training set and 0.04 on the test set. Results are stored in baseline1.csv.

This provides a baseline model for comparison to alternate configurations.

“A” versus “A” Test

Before we start testing variations of the model, it is important to get an idea of whether or not the test harness is stable.

That is, whether the summarizing skill of the model over 5 runs is sufficient to control for the stochastic nature of the model.

We can get an idea of this by running the experiment again in what is called an A vs A test in A/B testing land. We would expect to get an equivalent result if we ran the same experiment again; if we don’t, perhaps additional repeats would be required to control for the stochastic nature of the method and on the dataset.

Below are the results from a second run of the algorithm.

We can see that the run gets a very similar mean and standard deviation BLEU scores. Specifically, a mean BLEU of 0.03 vs 0.06 on train and 0.04 to 0.04 for test.

The harness is a little noisy, but stable enough for comparison.

Is the model any good?

Generate Photo Captions

We expect the model is under-trained and maybe even under provisioned, but can it generate any kind of readable text at all?

It is important that the baseline model have some modicum of capability so that we can relate the BLEU scores of the baseline to an idea of what kind of quality of descriptions are being generated.

Let’s train a single model and generate a few descriptions from the train and test sets as a sanity check.

Change the number of repeats to 1 and the name of the run to ‘baseline_generate‘.

Then update the evaluate_model() function to only evaluate the first 5 photos in the dataset and print the descriptions, as follows.

Re-run the example.

You should see results for the train set like the following (the specific results will vary given the stochastic nature of the algorithm):

You should see results on the test dataset as follows:

We can see that the descriptions are not perfect, some are a little rough, but generally the model is generating somewhat readable text. A good starting point for improvement.

Next, let’s look at some experiments to vary the size or capacity of different sub-models.

Network Size Parameters

In this section, we will see how gross variations to the network structure impact model skill.

We will look at the following aspects of the model size:

  1. Size of the fixed-vector output from the ‘encoders’.
  2. Size of the sequence encoder model.
  3. Size of the language model.

Let’s dive in.

Size of Fixed-Length Vector

In the baseline model, the photo feature extractor and the text sequence encoder both output a 128 element vector. These vectors are then concatenated to be processed by the language model.

The 128 element vector from each sub-model contains everything known about the input sequence and photo. We can vary the size of this vector to see if it impacts model skill

First, we can decrease the size by half from 128 elements to 64 elements.

We will name this model ‘size_sm_fixed_vec‘.

Running this experiment produces the following BLEU scores, perhaps a small gain over baseline on the test set.

We can also double the size of the fixed-length vector from 128 to 256 units.

We will name this configuration ‘size_lg_fixed_vec‘.

Running this experiment shows BLEU scores suggesting that the model is not better off.

It is possible that with more data and/or longer training, we may see a different story.

Sequence Encoder Size

We can call the sub-model that interprets the input sequence of words generated so far as the sequence encoder.

First, we can try to see if decreasing the representational capacity of the sequence encoder impacts model skill. We can reduce the number of memory units in the LSTM layer from 256 to 128.

Running this example, we can see perhaps a small bump on both train and test over baseline. This might be an artifact of the small training set size.

Going the other way, we can double the number of LSTM layers from one to two and see if that makes a dramatic difference.

Running this experiment shows a decent bump in BLEU on both train and test sets.

We can also try to increase the representational capacity of the word embedding by doubling it from 50-dimensions to 100-dimensions.

We see a large movement on the training dataset, but perhaps little movement on the test dataset.

Size of Language Model

We can refer to the model that learns from the concatenated sequence and photo feature input as the language model. It is responsible for generating words.

First, we can look at the impact on model skill by cutting the LSTM and dense layers from 500 to 256 neurons.

We can see that this has a small positive effect on BLEU for both training and test datasets, again, likely related to the small size of the datasets.

We can also look at the impact of doubling the capacity of the language model by adding a second LSTM layer of the same size.

Again, we see minor movements in BLEU, perhaps an artifact of noise and dataset size. The improvement on the test

The improvement on the test dataset may be a good sign. This might be a change worth exploring.

Tuning model size on a much smaller dataset is challenging.

Configuring the Feature Extraction Model

The use of the pre-trained VGG16 model provides some additional points of configuration.

The baseline model removed the top from the VGG model, including a global max pooling layer, which then feeds into an encoding of the features to a 128 element vector.

In this section, we will look at the following modifications to the baseline model:

  1. Using a global average pooling layer after the VGG model.
  2. Not using any global pooling.

Global Average Pooling

We can replace the GlobalMaxPooling2D layer with a GlobalAveragePooling2D to achieve average pooling.

Global average pooling was developed to reduce overfitting for image classification problems, but may offer some benefit in interpreting the features extracted from the image.

For more on global average pooling, see the paper:

The updated define_model() function and experiment name are listed below.

The results suggest a dramatic improvement on the training dataset, which may be a sign of overfitting. We also see a small lift on test skill. This might be a change worth exploring.

We also see a small lift on test skill. This might be a change worth exploring.

No Pooling

We can remove the GlobalMaxPooling2D and flatten the 3D photo feature and feed it directly into a Dense layer.

I would not expect this to be a good model design, but it is worth testing this assumption.

Surprisingly, we see a small lift on training data and a large lift on test data. This is surprising (to me) and may be worth further investigation.

We can try repeating this experiment and provide more capacity for interpreting the extracted photo features. A new Dense layer with 500 neurons is added after the Flatten layer.

This results in a less impressive change and perhaps worse BLEU results on the test dataset.

Word Embedding Models

A key part of the model is the sequence learning model that must interpret the sequence of words generated so far for a photo.

At the input to this sub-model is a word embedding and a good way to improve a word embedding over learning it from scratch as part of the model (as in the baseline model) is to use pre-trained word embeddings.

In this section, we will explore the impact of using a pre-trained word embedding on the model. Specifically:

  1. Training a Word2Vec Model
  2. Training a Word2Vec Model + Fine Tuning

Trained word2vec Embedding

An efficient learning algorithm for pre-training a word embedding from a corpus of text is the word2vec algorithm.

You can learn more about the word2vec algorithm here:

We can use this algorithm to train a new standalone set of word vectors using the cleaned photo descriptions in the dataset.

The Gensim library provides access to an implementation of the algorithm that we can use to pre-train the embedding.

First, we must load the clean photo descriptions for the training dataset, as before.

Next, we can fit the word2vec model on all of the clean descriptions. We should note that this includes more descriptions than the 50 used in the training dataset. A fairer model for these experiments should only be trained on those descriptions in the training dataset.

Once fit, we can save the words and word vectors to an ASCII file, perhaps for later inspection or visualization.

The word embedding is saved to the file ‘custom_embedding.txt‘.

Now, we can load the embedding into memory, retrieve only the word vectors for the words in our vocabulary, then save them to a new file.

The complete example is listed below.

Running this example creates a new dictionary mapping of word-to-word vectors stored in the file ‘word2vec_embedding.pkl‘.

Next, we can load this embedding and use the word vectors as the fixed weights in an Embedding layer.

Below provides the load_embedding() function that loads the custom word2vec embedding and returns the new Embedding layer for use in the model.

We can use it in our model by calling the function directly from our define_model() function.

We can see some lift on the training dataset, perhaps no real notable change on the test dataset.

Trained word2vec Embedding with Fine Tuning

We can repeat the previous experiment and allow the model to tune the word vectors while fitting the model.

The updated load_embedding() function that permits the embedding layer to be fine-tuned is listed below.

Again, we do not see much difference in using these pre-trained word embedding vectors over the baseline model.

Analysis of Results

We have performed a few experiments on a very small sample (1.6%) from the Flickr8k training dataset of 8,000 photos.

It is possible that the sample is too small, that the models were not trained for long enough, and that 3 repeats of each model results in too much variance. These aspects can also be tested by evaluated by designing experiments such as:

  1. Does model skill scale with the size of the dataset?
  2. Do more epochs result in better skill?
  3. Do more repeats result in a skill with less variance?

Nevertheless, we have some ideas on how we might configure a model for the fuller dataset.

Below is a summary of the mean results from the experiments performed in this tutorial.

It is helpful to review a graph of the results. If we had more repeats, a box and whisker plot for each distribution of scores might be a good visualization. Here we use a simple bar graph. Remember, that larger BLEU scores are better.

Results on the training dataset:

Bar Chart of Experiment vs Model Skill on the Training Dataset

Bar Chart of Experiment vs Model Skill on the Training Dataset

Results on the test dataset:

Bar Chart of Experiment vs Model Skill on the Test Dataset

Bar Chart of Experiment vs Model Skill on the Test Dataset

From just looking at the mean results on the test dataset, we can suggest:

  • Perhaps pooling is not required after the photo feature extractor (fe_flat at 0.135231).
  • Perhaps average pooling offers an advantage over max pooling after the photo feature extractor (fe_avg_pool at 0.060847).
  • Perhaps a smaller sized fixed-length vector after the sub-models is a good idea (size_sm_fixed_vec at 0.063148).
  • Perhaps adding more layers to the language model offers some benefit (size_lg_lang_model at 0.067658).
  • Perhaps adding more layers to the sequence model offers some benefit (size_lg_seq_model at 0.09697).

I would also recommend exploring combinations of these suggestions.

We can also review the distribution of results.

Below is some code to load the saved results from each experiment and create a box-and-whisker plot of results on the train and test sets for review.

Distribution of results on the training dataset.

Box and Whisker Plot of Experiment vs Model Skill on the Training Dataset

Box and Whisker Plot of Experiment vs Model Skill on the Training Dataset

Distribution of results on the test dataset.

Box and Whisker Plot of Experiment vs Model Skill on the Test Dataset

Box and Whisker Plot of Experiment vs Model Skill on the Test Dataset

A review of these distributions suggests:

  • The spread on the flat results is large; perhaps going with average pooling might be safer.
  • The spread on the larger language model is large and skewed in the wrong/risky direction.
  • The spread on the larger sequence model is large and skewed in the right direction.
  • There may be some benefit in a smaller fixed-length vector size.

I would expect increasing repeats to 5, 10, or 30 would tighten up these distributions somewhat.

Further Reading

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

Papers

Related Captioning Projects

Other

API

Summary

In this tutorial, you discovered how you can use a small sample of the photo captioning dataset to explore different model designs.

Specifically, you learned:

  • How to prepare data for photo captioning modeling.
  • How to design a baseline and test harness to evaluate the skill of models and control for their stochastic nature.
  • How to evaluate properties like model skill, feature extraction model, and word embeddings in order to lift model skill.

What experiments can you think up?
What else have you tried?
What are the best results you can get on the train and test dataset?

Let me know in the comments below.


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8 Responses to How to Use Small Experiments to Develop a Caption Generation Model in Keras

  1. Emil November 24, 2017 at 6:39 am #

    Hats off, another ace tutorial!

    I’m curious how the TimeDistributed layer impacts the data before the concatenation. Is it possible to skip it? Also, is there a reason you are using VGG instead of the InceptionResNetV2 class other than memory/compute constraints.

    Thanks!

    • Jason Brownlee November 24, 2017 at 9:52 am #

      I chose VGG because it is smaller and simpler. You can use anything you wish.

      You can skip the TimeDistributed as Dense can support time steps now I believe. I like it in there as it reminds me what is going on (e.g. outputting time steps).

  2. Alex November 24, 2017 at 6:16 pm #

    Ho jason, why don t you reset the LSTM states between the inputs related to different images? Ad they are not related to the same sequences.

    • Jason Brownlee November 25, 2017 at 10:14 am #

      For speed of training.

      It’s a great suggestion though, try it and see if it lifts skill! Let me know how you go.

      • Alex November 28, 2017 at 1:39 am #

        Thanks! In order to try this way, should I set stateful=True (avoiding the LSTM to reset itself automatically) and manually run model.reset_states() before training a single batch? (each batch is related to the sequence of a single image).

  3. Emil December 13, 2017 at 12:10 am #

    What’s the logic of the +1 when you are creating the vocab len: “vocab_size = len(tokenizer.word_index) + 1”? Is it to leave remove for the 0?

    Thanks

    • Jason Brownlee December 13, 2017 at 5:39 am #

      Good question, to make space for 0 – words in the vocab start at 1.

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