I am exploring deep learning methods especially LSTM to predict next word. Suppose, My data set is like this: Each data point consists of 7 features (7 different words)(A-G here) of different length.
Group1 Group2............ Group 38
A B F
E C A
B E G
C D G
C F F
D G G
. . .
. . .
I used one hot encoding as an Input layer. Here is the model
main_input= Input(shape=(None,action_count),name='main_input')
lstm_out= LSTM(units=64,activation='tanh')(main_input)
lstm_out=Dropout(0.2)(lstm_out)
lstm_out=Dense(action_count)(lstm_out)
main_output=Activation('softmax')(lstm_out)
model=Model(inputs=[main_input],outputs=main_output)
print(model.summary())
Using this model. I got an accuracy of about 60%.
My question is how can I use embedding layer for my problem. Actually, I do not know much about embedding (why, when and how it works)[I only know one hot vector does not carry much information]. I am wondering if embedding can improve accuracy. If someone can provide me guidance in these regards, it will be greatly beneficial for me. (At least whether uses of embedding is logical or not for my case)
What are Embedding layers?
They are layers which converts positive integers ( maybe word counts ) into fixed size dense vectors. They learn the so called embeddings for a particular text dataset ( in NLP tasks ).
Why are they useful?
Embedding layers slowly learn the relationships between words. Hence, if you have a large enough corpus ( which probably contains all possible English words ), then vectors for words like "king" and "queen" will show some similarity in the mutidimensional space of the embedding.
How are used in Keras?
The keras.layers.Embedding has the following configurations:
keras.layers.Embedding(input_dim, output_dim, embeddings_initializer='uniform', embeddings_regularizer=None, activity_regularizer=None, embeddings_constraint=None, mask_zero=False, input_length=None)
Turns positive integers (indexes) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]
This layer can only be used as the first layer in a model.
When the input_dim is the vocabulary size + 1. Vocabulary is the corpus of all the words used in the dataset. The input_length is the length of the input sequences whereas output_dim is the dimensionality of the output vectors ( the dimensions for the vector of a particular word ).
The layer can also be used wih pretrained word embeddings like Word2Vec or GloVE.
Are they suitable for my use case?
Absolutely, yes. For sentiment analysis, if we could generate a context ( embedding ) for a particular word then we could definitely increase its efficiency.
How can I use them in my use case?
Follow the steps:
You need to tokenize the sentences. Maybe with keras.preprocessing.text.Tokenizer.
Pad the sequences to a fixed length using keras.preprocessing.sequence.pad_sequences. This will be the input_length parameter for the Embedding layer.
Initialize the model with Embedding layer as the first layer.
Hope this helps.
Related
Recently I was going through the paper : "Intriguing Properties of Contrastive Losses"(https://arxiv.org/abs/2011.02803). In the paper(section 3.2) the authors try to determine how well the SimCLR framework has allowed the ResNet50 Model to learn good quality/generalised features that exhibit hierarchical properties. To achieve this, they make use of K-means on intermediate features of the ResNet50 model (intermediate means o/p of block 2,3,4..) & quote the reason -> "If the model learns good representations then regions of similar objects should be grouped together".
Final Results :
KMeans feature visualisation
I am trying to replicate the same procedure but with a different model (like VggNet, Xception), are there any resources explaining how to perform such visualisations ?
The procedure would be as follow:
Let us assume that you want to visualize the 8th layer from VGG. This layer's output might have the shape (64, 64, 256) (I just took some random numbers, this does not correspond to actual VGG). This means that you have 4096 256-dimensional vectors (for one specific image). Now you can apply K-Means on these vectors (for example with 5 clusters) and then color your image corresponding to the clustering result. The coloring is easy, since the 64x64 feature map represents a scaled down version of your image, and thus you just color the corresponding image region for each of these vectors.
I don't know if it might be a good idea to do the K-Means clustering on the combined output of many images, theoretically doing it on many images and one a single one should both give good results (even though for many images you probably would increase the number of clusters to account for the higher variation in your feature vectors).
I will explain my problem:
I have around 50.000 samples, each of one described by a list of codes representing "events"
The number of unique codes are around 800.
The max number of codes that a sample could have is around 600.
I want to represent each sample using one-hot encoding. The representation should be, if we consider the operation of padding for those samples that has fewer codes, a 800x600 matrix.
Giving this new representation as input of a network, means to flatten each matrix to a vector of size 800x600 (460.000 values).
At the end the dataset should consist in 50.000 vectors of size 460.000 .
Now, I have two considerations:
How is it possible to handle a dataset of that size?(I tried data generator to obtain the representation on-the-fly but they are really slow).
Having a vector of size 460.000 as input for each sample, means that the complexity of my model( number of parameters to learn ) is extremely high ( around 15.000.000 in my case ) and, so, I need an huge dataset to train the model properly. Doesn't it?
Why do not you use the conventional model used in NLP?
These events can be translated as you say by embedding matrix.
Then you can represent the chains of events using LSTM (or GRU or RNN o Bilateral LSTM), the difference of using LSTM instead of a conventional network is that you use the same module repeated by N times.
So your input really is not 460,000, but internally an event A indirectly helps you learn about an event B. That's because the LSTM has a module that repeats itself for each event in the chain.
You have an example here:
https://www.kaggle.com/ngyptr/lstm-sentiment-analysis-keras
Broadly speaking what I would do would be the following (in Keras pseudo-code):
Detect the number of total events. I generate a unique list.
unique_events = list (set ([event_0, ..., event_n]))
You can perform the translation of a sequence with:
seq_events_idx = map (unique_events.index, seq_events)
Add the necessary pad to each sequence:
sequences_pad = pad_sequences (sequences, max_seq)
Then you can directly use an embedding to carry out the transfer of the event to an associated vector of the dimension that you consider.
input_ = Input (shape = (max_seq,), dtype = 'int32')
embedding = Embedding (len(unique_events),
dimensions,
input_length = max_seq,
trainable = True) (input_)
Then you define the architecture of your LSTM (For example):
lstm = LSTM (128, input_shape = (max_seq, dimensions), dropout = 0.2, recurrent_dropout = 0.2, return_sequences = True) (embedding)
Add the dense and the result you want:
out = Dense (10, activation = 'softmax') (lstm)
I think that this type of model can help you and give better results.
I'm using a 1D CNN on temporal data. Let's say that I have two features A and B. The ratio between A and B (i.e. A/B) is important - let's call this feature C. I'm wondering if I need to explicitly calculate and include feature C, or can the CNN theoretically infer feature C from the given features A and B?
I understand that in deep learning, it's best to exclude highly-correlated features (such as feature C), but I don't understand why.
The short answer is NO. Using the standard DNN layers will not automatically capture this A/B relationship, because standard layers like Conv/Dense will only perform the matrix multiplication operations.
To simplify the discussion, let us assume that your input feature is two-dimensional, where the first dimension is A and the second is B. Applying a Conv layer to this feature simply learns a weight matrix w and bias b
y = w * [f_A, f_B] + b = w_A * f_A + w_B * f_B + b
As you can see, there is no way for this representation to mimic or even approximate the ratio operation between A and B.
You don't have to use the feature C in the same way as feature A and B. Instead, it may be a better idea to keep feature C as an individual input, because its dynamic range may be very different from those of A and B. This means that you can have a multiple-input network, where each input has its own feature extraction layers and the resulting features from both inputs can be concatenated together to predict your target.
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I'm going through this tutorial on RNNs/LSTMs and I'm having quite a hard time understanding stateful LSTMs. My questions are as follows :
1. Training batching size
In the Keras docs on RNNs, I found out that the hidden state of the sample in i-th position within the batch will be fed as input hidden state for the sample in i-th position in the next batch. Does that mean that if we want to pass the hidden state from sample to sample we have to use batches of size 1 and therefore perform online gradient descent? Is there a way to pass the hidden state within a batch of size >1 and perform gradient descent on that batch ?
2. One-Char Mapping Problems
In the tutorial's paragraph 'Stateful LSTM for a One-Char to One-Char Mapping' were given a code that uses batch_size = 1 and stateful = True to learn to predict the next letter of the alphabet given a letter of the alphabet. In the last part of the code (line 53 to the end of the complete code), the model is tested starting with a random letter ('K') and predicts 'B' then given 'B' it predicts 'C', etc. It seems to work well except for 'K'. However, I tried the following tweak to the code (last part too, I kept lines 52 and above):
# demonstrate a random starting point
letter1 = "M"
seed1 = [char_to_int[letter1]]
x = numpy.reshape(seed, (1, len(seed), 1))
x = x / float(len(alphabet))
prediction = model.predict(x, verbose=0)
index = numpy.argmax(prediction)
print(int_to_char[seed1[0]], "->", int_to_char[index])
letter2 = "E"
seed2 = [char_to_int[letter2]]
seed = seed2
print("New start: ", letter1, letter2)
for i in range(0, 5):
x = numpy.reshape(seed, (1, len(seed), 1))
x = x / float(len(alphabet))
prediction = model.predict(x, verbose=0)
index = numpy.argmax(prediction)
print(int_to_char[seed[0]], "->", int_to_char[index])
seed = [index]
model.reset_states()
and these outputs:
M -> B
New start: M E
E -> C
C -> D
D -> E
E -> F
It looks like the LSTM did not learn the alphabet but just the positions of the letters, and that regardless of the first letter we feed in, the LSTM will always predict B since it's the second letter, then C and so on.
Therefore, how does keeping the previous hidden state as initial hidden state for the current hidden state help us with the learning given that during test if we start with the letter 'K' for example, letters A to J will not have been fed in before and the initial hidden state won't be the same as during training ?
3. Training an LSTM on a book for sentence generation
I want to train my LSTM on a whole book to learn how to generate sentences and perhaps learn the authors style too, how can I naturally train my LSTM on that text (input the whole text and let the LSTM figure out the dependencies between the words) instead of having to 'artificially' create batches of sentences from that book myself to train my LSTM on? I believe I should use stateful LSTMs could help but I'm not sure how.
Having a stateful LSTM in Keras means that a Keras variable will be used to store and update the state, and in fact you could check the value of the state vector(s) at any time (that is, until you call reset_states()). A non-stateful model, on the other hand, will use an initial zero state every time it processes a batch, so it is as if you always called reset_states() after train_on_batch, test_on_batch and predict_on_batch. The explanation about the state being reused for the next batch on stateful models is just about that difference with non-stateful; of course the state will always flow within each sequence in the batch and you do not need to have batches of size 1 for that to happen. I see two scenarios where stateful models are useful:
You want to train on split sequences of data because these are very long and it would not be practical to train on their whole length.
On prediction time, you want to retrieve the output for each time point in the sequence, not just at the end (either because you want to feed it back into the network or because your application needs it). I personally do that in the models that I export for later integration (which are "copies" of the training model with batch size of 1).
I agree that the example of an RNN for the alphabet does not really seem very useful in practice; it will only work when you start with the letter A. If you want to learn to reproduce the alphabet starting at any letter, you would need to train the network with that kind of examples (subsequences or rotations of the alphabet). But I think a regular feed-forward network could learn to predict the next letter of the alphabet training on pairs like (A, B), (B, C), etc. I think the example is meant for demonstrative purposes more than anything else.
You may have probably already read it, but the popular post The Unreasonable Effectiveness of Recurrent Neural Networks shows some interesting results along the lines of what you want to do (although it does not really dive into implementation specifics). I don't have personal experience training RNN with textual data, but there is a number of approaches you can research. You can build character-based models (like the ones in the post), where your input and receive one character at a time. A more advanced approach is to do some preprocessing on the texts and transform them into sequences of numbers; Keras includes some text preprocessing functions to do that. Having one single number as feature space is probably not going to work all that well, so you could simply turn each word into a vector with one-hot encoding or, more interestingly, have the network learn the best vector representation for each for, which is what they call en embedding. You can go even further with the preprocessing and look into something like NLTK, specially if you want to remove stop words, punctuation and things like that. Finally, if you have sequences of different sizes (e.g. you are using full texts instead of excerpts of a fixed size, which may or may not be important for you) you will need to be a bit more careful and use masking and/or sample weighting. Depending on the exact problem, you can set up the training accordingly. If you want to learn to generate similar text, the "Y" would be the similar to the "X" (one-hot encoded), only shifted by one (or more) positions (in this case you may need to use return_sequences=True and TimeDistributed layers). If you want to determine the autor, your output could be a softmax Dense layer.
Hope that helps.
In the below code, they use autoencoder as supervised clustering or classification because they have data labels.
http://amunategui.github.io/anomaly-detection-h2o/
But, can I use autoencoder to cluster data if I did not have its labels.?
Regards
The deep-learning autoencoder is always unsupervised learning. The "supervised" part of the article you link to is to evaluate how well it did.
The following example (taken from ch.7 of my book, Practical Machine Learning with H2O, where I try all the H2O unsupervised algorithms on the same data set - please excuse the plug) takes 563 features, and tries to encode them into just two hidden nodes.
m <- h2o.deeplearning(
2:564, training_frame = tfidf,
hidden = c(2), auto-encoder = T, activation = "Tanh"
)
f <- h2o.deepfeatures(m, tfidf, layer = 1)
The second command there extracts the hidden node weights. f is a data frame, with two numeric columns, and one row for every row in the tfidf source data. I chose just two hidden nodes so that I could plot the clusters:
Results will change on each run. You can (maybe) get better results with stacked auto-encoders, or using more hidden nodes (but then you cannot plot them). Here I felt the results were limited by the data.
BTW, I made the above plot with this code:
d <- as.matrix(f[1:30,]) #Just first 30, to avoid over-cluttering
labels <- as.vector(tfidf[1:30, 1])
plot(d, pch = 17) #Triangle
text(d, labels, pos = 3) #pos=3 means above
(P.S. The original data came from Brandon Rose's excellent article on using NLTK. )
In some aspects encoding data and clustering data share some overlapping theory. As a result, you can use Autoencoders to cluster(encode) data.
A simple example to visualize is if you have a set of training data that you suspect has two primary classes. Such as voter history data for republicans and democrats. If you take an Autoencoder and encode it to two dimensions then plot it on a scatter plot, this clustering becomes more clear. Below is a sample result from one of my models. You can see a noticeable split between the two classes as well as a bit of expected overlap.
The code can be found here
This method does not require only two binary classes, you could also train on as many different classes as you wish. Two polarized classes is just easier to visualize.
This method is not limited to two output dimensions, that was just for plotting convenience. In fact, you may find it difficult to meaningfully map certain, large dimension spaces to such a small space.
In cases where the encoded (clustered) layer is larger in dimension it is not as clear to "visualize" feature clusters. This is where it gets a bit more difficult, as you'll have to use some form of supervised learning to map the encoded(clustered) features to your training labels.
A couple ways to determine what class features belong to is to pump the data into knn-clustering algorithm. Or, what I prefer to do is to take the encoded vectors and pass them to a standard back-error propagation neural network. Note that depending on your data you may find that just pumping the data straight into your back-propagation neural network is sufficient.