I want to get the output (that is a vector) of a LSTM layer of a network built in Python using Keras and that is trained to classify sentences (i.e. sequences). How can I do it ?
My attempt has been the following:
Is it right to use the function model.predict() ? I found this video LSTM in Keras | Understanding LSTM input and output shapes that explains that the input of an LSTM layer (that is just after the embedding layer) is a vector of size (number of sequences, number of inputs, embedding dimension) and the corresponding LSTM output has dimension(number of sequences, number of LSTM units). In the linked video, it gets the latter vector (i.e. the output vector of lstm layer) by using model.predict(encodedsequences_data) just after the LSTM layer. For example, if I train a neural network to classify between positive and negative comments like this:
import numpy as np
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import Dense
from tensorflow.keras.layers import Dropout
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
# define documents
docs = np.array(['Well done!',
'Good work',
'Great effort',
'nice work',
'Excellent!',
'Weak',
'Poor effort!',
'not good',
'poor work',
'Could have done better.'])
# define class labels
labels = np.array([1,1,1,1,1,0,0,0,0,0])
# train the tokenizer
tokenizer = Tokenizer()
# fit the tokenizer
tokenizer.fit_on_texts(docs)
# encode the sentences
encoded_docs = tokenizer.texts_to_sequences(docs)
vocab_size=len(tokenizer.word_index)+1
# pad documents to a max length of 4 words
max_length = 4
padded_docs = pad_sequences(encoded_docs, maxlen=max_length, padding='post')
embedding_dim=100
# define the model
model = Sequential()
model.add(Embedding(vocab_size, embedding_dim, input_length=max_length, name='embeddings'))
model.add(LSTM(64))
output=model.predict(padded_docs)
model.add(Dropout(0.25))
model.add(Dense(64))
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid'))
model.summary()
# compile the model
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# fit the model
model.fit(padded_docs, labels, epochs=50, verbose=2)
I should consider the input of the LSTM layer as a vector of size (10, 4, 100). While instead the output is a vector whose shape is given by:
In[10]: output.shape
Out[10]: (10, 64)
That is in practice this numpy.ndarray:
In[11]: output
Out[11]:
array([[-7.35682389e-03, -6.29833259e-04, -2.14141682e-02,
5.49282366e-03, -1.68905873e-02, -7.86065124e-03,
1.14580495e-02, 1.25549696e-02, -9.16293636e-03,
6.39621960e-03, -1.34323994e-02, -2.12187809e-03,
8.44217744e-03, 1.45898620e-02, -1.40892563e-03,
-3.41916122e-02, -1.31929619e-02, 9.33299214e-03,
1.19191762e-02, -1.00926403e-03, 1.26794688e-02,
-2.21233014e-02, -1.12434607e-02, -4.41948650e-03,
8.63359030e-03, -1.87689364e-02, 7.90755264e-03,
-9.07730684e-03, -7.35392375e-03, -8.41679424e-03,
6.20685238e-03, -3.13799526e-03, 1.42355347e-02,
3.77556833e-04, -7.31376186e-03, 4.97561414e-03,
-1.09350188e-02, -7.71270739e-03, 1.80931657e-03,
-4.15941747e-03, 1.03279343e-02, 1.07305320e-02,
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7.19825737e-03, -8.12581368e-03, 2.17414256e-02,
1.95931066e-02, -1.33228181e-02, 5.87116787e-03,
3.40227783e-02, 2.07215771e-02, -4.87452417e-05,
-2.10380089e-02],
[-5.61810751e-03, 1.05204089e-02, -1.52371302e-02,
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-8.58186861e-04, -2.87145306e-03, 1.99557561e-02,
2.37323977e-02, -1.33994315e-02, 8.58740974e-03,
2.87993196e-02, 2.31237561e-02, -9.56221425e-04,
-1.58641450e-02],
[-7.23353820e-03, 1.03754746e-02, -1.44966058e-02,
-5.24685532e-03, -7.83862360e-03, -1.28473695e-02,
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3.11383940e-02, 2.01738160e-02, -1.07578654e-03,
-1.72522105e-02],
[-1.20945638e-02, 8.68875161e-03, -2.62735188e-02,
1.12946620e-02, -1.22309905e-02, -5.58001362e-03,
1.08549614e-02, 8.95095989e-03, -7.41497055e-03,
1.16792703e-02, -2.28939448e-02, -3.50253959e-03,
9.97900032e-03, 1.66020077e-02, -7.89092761e-03,
-4.07132506e-02, -1.78568307e-02, 4.34662355e-03,
1.21228509e-02, 3.13125411e-03, 1.45842955e-02,
-2.05406677e-02, -8.31084419e-03, -4.01034905e-03,
7.12802447e-03, -1.84220411e-02, 1.13592790e-02,
-1.26814209e-02, -1.18051590e-02, -8.80334340e-03,
3.80245410e-03, -8.09166487e-03, 1.76429395e-02,
-1.92356238e-04, -5.44061745e-03, 1.36529943e-02,
-1.77526288e-02, -7.82714784e-03, 1.20329263e-03,
-4.59810719e-03, 1.16233192e-02, 1.27270408e-02,
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-2.18094457e-02, -1.00004785e-02, -1.08398555e-03,
3.66409164e-04, -2.32298975e-03, 1.85762774e-02,
-3.05683468e-03, -1.08189192e-02, -8.16119835e-03,
2.15096259e-03, -1.27328513e-02, 3.22906636e-02,
2.98772007e-02, -1.85494740e-02, -6.53657946e-04,
3.88556197e-02, 2.41267327e-02, -4.84270183e-03,
-2.17875950e-02],
[-5.25501464e-03, 8.72639474e-03, -2.05799732e-02,
6.14312338e-03, -1.72808673e-02, -1.31866131e-02,
5.83314849e-03, 6.02243049e-03, -3.84115218e-03,
1.41651463e-02, -2.30942499e-02, -5.55078313e-03,
1.04255294e-02, 2.30545755e-02, -2.18844530e-03,
-3.62902768e-02, -1.92042850e-02, 1.22342422e-03,
1.13154203e-02, -2.17898004e-03, 1.37291076e-02,
-2.04059239e-02, -8.93826876e-03, -3.92630836e-03,
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2.67816409e-02, -1.40035218e-02, 4.13230434e-03,
3.57808806e-02, 2.61333492e-02, -8.73044250e-04,
-2.66207643e-02],
[-7.28952140e-03, 3.69984098e-03, -1.50594991e-02,
-2.92313565e-03, -1.21962698e-02, -1.36810802e-02,
6.82729529e-03, 6.73223054e-03, -5.93179138e-04,
1.36305839e-02, -1.97144989e-02, -3.86482291e-03,
1.80094223e-02, 1.42702283e-02, -1.32999849e-03,
-3.40334214e-02, -1.76202524e-02, 2.30349993e-04,
1.09610325e-02, 5.54277329e-03, 7.91644678e-03,
-2.16093995e-02, -1.39994686e-02, -3.96339269e-03,
8.55575502e-03, -9.74893570e-03, 1.05637731e-02,
-4.55878396e-03, -1.34231234e-02, -5.41363843e-04,
4.89153492e-04, -6.66437577e-03, 1.07367048e-02,
3.43973073e-03, -4.18765657e-03, 6.89268531e-03,
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2.46776640e-03, -1.70147279e-03, 5.19089587e-03,
1.00052624e-03, -5.67172188e-03, -3.38913826e-03,
2.77179107e-03, -6.60816021e-03, 2.12577283e-02,
2.13659275e-02, -1.32223312e-02, 2.25704932e-03,
2.57163309e-02, 2.04398781e-02, -1.69396808e-04,
-1.73870064e-02],
[-3.72720137e-03, 1.10780252e-02, -1.58859324e-02,
6.66555809e-03, -1.44280717e-02, -8.00609123e-03,
6.45924266e-03, 8.54926067e-04, -1.88849677e-04,
8.98782630e-03, -1.54129220e-02, -1.08604110e-03,
1.07514234e-02, 1.73932631e-02, -1.32540641e-02,
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7.03095598e-03, -1.30360266e-02, 1.06415655e-02,
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1.58338025e-02, -1.86773948e-02, 7.61539035e-04,
2.71951333e-02, 1.39996661e-02, -5.14069805e-03,
-1.78940073e-02],
[-6.54364191e-03, 5.09772869e-03, -1.04034524e-02,
-3.73475719e-03, -1.19625181e-02, -1.40891932e-02,
9.06457752e-03, 3.41503625e-03, -4.86045564e-03,
1.25511140e-02, -2.17867326e-02, -7.47563411e-03,
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3.10223433e-03, 4.86978563e-03, 1.11100767e-02,
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1.84700415e-02, -1.15374802e-02, -1.12538924e-03,
2.62181889e-02, 1.91304646e-02, -2.57981522e-03,
-1.63868871e-02],
[ 8.44749855e-04, -1.66689996e-02, -8.96400423e-04,
6.72107562e-03, -2.39076628e-03, 2.60190992e-03,
9.26916581e-03, 8.82431399e-03, -7.25202635e-03,
-1.06594963e-02, -5.55762183e-03, -1.29527331e-03,
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5.84689900e-03, 9.08445287e-03, -1.40777905e-03,
-1.06005892e-02, -3.84641928e-03, 2.70416541e-03,
4.00838861e-03, 2.82439473e-03, 2.29762960e-03,
-1.00108888e-03, -4.37536306e-04, -5.20851184e-03,
4.79862979e-03, -4.48886072e-03, 8.50347336e-03,
-1.42790927e-02, -1.26732215e-02, 4.63177776e-03,
-1.20126840e-03, 5.99153014e-03, 1.22683365e-02,
3.37655889e-04, -1.39692798e-02, 3.31070763e-03,
-5.39805554e-03, 2.78319512e-02, -6.16331352e-03,
5.77836717e-03, -1.19619851e-03, -1.58095714e-02,
3.77729093e-03, 1.85370538e-03, 6.35961816e-03,
3.49745946e-03, -1.27604958e-02, -9.54155251e-03,
1.23564843e-02, -7.57912593e-03, -3.30073433e-03,
-7.29874987e-03, -6.47724420e-03, -1.41964471e-02,
1.36867436e-02, 5.99695602e-03, 9.53863724e-04,
5.32696443e-03]], dtype=float32)
Does this (multidimensional) vector represent the output of the LSTM layer and does each inner vector can be chosen as vector representation of the corresponding sentence (i.e. vector1 -> sentence1 and so on) ?
Based on the youtube video that I linked, it seems right and also I find other lessons that seem to do the same (such as in this blog LSTM: Understanding Output Types). But I am not really convinced. My doubt is why does the function model.predict() give the output of the LSTM layer ? Python help tells that this function:
Generates output predictions for the input samples.
Or also in 3.6. scikit-learn: machine learning in Python:
In supervised estimators:
model.predict(): given a trained model, predict the label of a new set of data. This method accepts one argument, the new dataX_new(e.g.model.predict(X_new)), and returns the learned label for each object in the array.
So, given these explanations, why should model.predict() give the LSTM output vector ? What does model.predict() exactly do ?
To sum up, I would like to know if the procedure that I reported using model.predict() is right to get the output of the LSTM layer in Keras and why. Furthermore, in case it is not, if you can suggest the right procedure.
Thanks a lot in advance.
----EDIT----: an other approach is using the explanation in https://keras.io/getting_started/faq/#how-can-i-obtain-the-output-of-an-intermediate-layer-feature-extraction
intermediate_layer_model = keras.Model(inputs=model.input,
outputs=model.get_layer(layer_name).output)
intermediate_output = intermediate_layer_model(data)
with layer_name = the name of my lstm layer and data = padded_docs. I think that this procedure is correct for sure since it is in Keras documentation. And I also think that it is similar to the first approach with model.predict(padded_docs). In the same Keras linked page, there is the difference between using just y=model(x) and y=model.predict(x) and it says that they both mean "run the model on x and retrieve the output y."
So, in conclusion, maybe they both run the input data (in this case our sentences) through the model or through the model untill the layer of interest and retrieve the output processed data by the model that has parameters (weights) that are updated after the training ? Does this mean to get the output of a layer (that in this case is a LSTM layer)?
