ValueError: Shapes are incompatible in LSTM model

Question

I am creating an LSTM model based on the following parameters

embed_dim = 128
lstm_out = 200
batch_size = 32

model = Sequential()
model.add(Embedding(2500, embed_dim,input_length = X.shape[1]))
model.add(Dropout(0.2))
model.add(LSTM(lstm_out))
model.add(Dense(2,activation='sigmoid'))
model.compile(loss = 'categorical_crossentropy', optimizer='adam',metrics = ['accuracy'])
print(model.summary())

Xtrain, Xtest, ytrain, ytest = train_test_split(X, train['target'], test_size = 0.2, shuffle=True)
print(Xtrain.shape, ytrain.shape)
print(Xtest.shape, ytest.shape)

model.fit(Xtrain, ytrain, batch_size =batch_size, epochs = 1,  verbose = 5)

but I am receiving the following error

ValueError: Shapes (32, 1) and (32, 2) are incompatible

Can you help me with this error?

Answer 1

Your y_train is coming from a single column of a Pandas dataframe, which is a single column. This is suitable if your classification problem is a binary classification 0/1 problem. Then you only need a single neuron in the output layer.

model = Sequential()
model.add(Embedding(2500, embed_dim,input_length = X.shape[1]))
model.add(Dropout(0.2))
model.add(LSTM(lstm_out))
# Only one neuron in the output layer
model.add(Dense(1,activation='sigmoid'))