How to use TF IDF vectorizer with LSTM in Keras Python

Question

I am trying to train a Seq2Seq model using LSTM in Keras library of Python. I want to use TF IDF vector representation of sentences as input to the model and getting an error.

X = ["Good morning", "Sweet Dreams", "Stay Awake"]
Y = ["Good morning", "Sweet Dreams", "Stay Awake"]

vectorizer = TfidfVectorizer()
vectorizer.fit(X)
vectorizer.transform(X)
vectorizer.transform(Y)
tfidf_vector_X = vectorizer.transform(X).toarray() #shape - (3,6)
tfidf_vector_Y = vectorizer.transform(Y).toarray() #shape - (3,6)
tfidf_vector_X = tfidf_vector_X[:, :, None] #shape - (3,6,1) since LSTM cells expects ndims = 3
tfidf_vector_Y = tfidf_vector_Y[:, :, None] #shape - (3,6,1)

X_train, X_test, y_train, y_test = train_test_split(tfidf_vector_X, tfidf_vector_Y, test_size = 0.2, random_state = 1)
model = Sequential()
model.add(LSTM(output_dim = 6, input_shape = X_train.shape[1:], return_sequences = True, init = 'glorot_normal', inner_init = 'glorot_normal', activation = 'sigmoid'))
model.add(LSTM(output_dim = 6, input_shape = X_train.shape[1:], return_sequences = True, init = 'glorot_normal', inner_init = 'glorot_normal', activation = 'sigmoid'))
model.add(LSTM(output_dim = 6, input_shape = X_train.shape[1:], return_sequences = True, init = 'glorot_normal', inner_init = 'glorot_normal', activation = 'sigmoid'))
model.add(LSTM(output_dim = 6, input_shape = X_train.shape[1:], return_sequences = True, init = 'glorot_normal', inner_init = 'glorot_normal', activation = 'sigmoid'))
adam = optimizers.Adam(lr = 0.001, beta_1 = 0.9, beta_2 = 0.999, epsilon = None, decay = 0.0, amsgrad = False)
model.compile(loss = 'cosine_proximity', optimizer = adam, metrics = ['accuracy'])
model.fit(X_train, y_train, nb_epoch = 100)

The above code throws:

Error when checking target: expected lstm_4 to have shape (6, 6) but got array with shape (6, 1)

Could someone tell me what's wrong and how to fix it?

Answer 1

Currently, you are returning sequences of dimension 6 in your final layer. You probably want to return a sequence of dimensionality 1 to match your target sequence. I'm not 100% sure here because I'm not experienced with seq2seq models, but at least the code runs in this way. Maybe have a look at the seq2seq tutorial on the Keras blog.

Besides that, two small points: when using the Sequential API, you only need to specify an input_shape for the first layer of your model. Also, the output_dim argument of the LSTM layer is deprecated and should be replaced by the units argument:

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split

X = ["Good morning", "Sweet Dreams", "Stay Awake"]
Y = ["Good morning", "Sweet Dreams", "Stay Awake"]

vectorizer = TfidfVectorizer().fit(X)

tfidf_vector_X = vectorizer.transform(X).toarray()  #//shape - (3,6)
tfidf_vector_Y = vectorizer.transform(Y).toarray() #//shape - (3,6)
tfidf_vector_X = tfidf_vector_X[:, :, None] #//shape - (3,6,1) 
tfidf_vector_Y = tfidf_vector_Y[:, :, None] #//shape - (3,6,1)

X_train, X_test, y_train, y_test = train_test_split(tfidf_vector_X, tfidf_vector_Y, test_size = 0.2, random_state = 1)

from keras import Sequential
from keras.layers import LSTM

model = Sequential()
model.add(LSTM(units=6, input_shape = X_train.shape[1:], return_sequences = True))
model.add(LSTM(units=6, return_sequences=True))
model.add(LSTM(units=6, return_sequences=True))
model.add(LSTM(units=1, return_sequences=True, name='output'))
model.compile(loss='cosine_proximity', optimizer='sgd', metrics = ['accuracy'])

print(model.summary())

model.fit(X_train, y_train, epochs=1, verbose=1)