LSTM with TFIDF as input, dimension error

Question

I am trying to run LSTM with TFIDF as input, but getting an error. I have TFIDF with each entry of 11915 dimensions

Code is as follows:

## Creating model
model=Sequential()
model.add(Bidirectional(LSTM(100, input_shape=(1, 11915),return_sequences=True)))
model.add(Dropout(0.3))
model.add(Dense(1,activation='sigmoid'))
model.build(input_shape=(1, 11915))
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
print(model.summary())

Error is as follows Input 0 of layer bidirectional_27 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: [1, 11915]

I am new to this area, any help will be highly appreciated. It will be really nice if someone writes a dummy code for running Bidirectional LSTM on such an input

My input is tfidf of 10229*11915. I want to do fake news detection using LSTM on TFIDF as input

Marco Cerliani Marco Cerliani · Accepted Answer · 2020-06-23T12:31:10

this is a complete working example

# create fake data
n_sample = 10229
X = np.random.uniform(0,1, (n_sample,11915))
y = np.random.randint(0,2, n_sample)

# expand X to 3D
X = X.reshape(X.shape[0],1,X.shape[-1])

model=Sequential()
model.add(Bidirectional(LSTM(100, return_sequences=False), input_shape=(1, 11915)))
model.add(Dropout(0.3))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
print(model.summary())

model.fit(X,y, epochs=3, batch_size=256)

the error occurs because probably u didn't manage your data correctly. take care also to define the first layer correctly and return_sequences=False because your output is 2D

LSTM with TFIDF as input, dimension error

2 Answers