Trying to use sigmoid as an activation function for the last dense layer of a LSTN, I get this error

ValueError: `logits` and `labels` must have the same shape, received ((None, 60, 1) vs (None,)).

The code is this

scaler = StandardScaler()
X_train_s = scaler.fit_transform(X_train) #scaled_train 
X_test_s = scaler.transform(X_test) #scaled_test     

length = 60

generator = TimeseriesGenerator(X_train_s, Y_train['TARGET_ENTRY_LONG'], length=length, batch_size=1)
validation_generator = TimeseriesGenerator(X_test_s, Y_test['TARGET_ENTRY_LONG'], length=length, batch_size=1)

# define model
model = Sequential()
model.add(LSTM(90, activation='relu', input_shape=(length, n_features), return_sequences=True, dropout = 0.3))
model.add(LSTM(30,activation='relu',return_sequences=True, dropout = 0.3))
model.add(Dense(1, activation = 'sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy')


# fit model


If I replace the last layer declaration with the following one


I get no errors, but probably also not the expected result. Any idea?


1 Answer 1


Found the cause of the trouble after several attempts, it was in the layer before the last one: it shall have no "return_sequences=True" set, that is for all the layers before if the last one is a dense layer for binary classification using sigmoid as activation function. Therefore, this layer

model.add(LSTM(30,activation='relu',return_sequences=True, dropout = 0.3))

shall be written instead as following

model.add(LSTM(30,activation='relu', dropout = 0.3))

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