Keras model not learning after training - python-3.x

Question

I am training a keras model for a sentence classification task. The problem is although it is giving an accuracy of 94%, it is not learning anything. When I give a new sentence (not present in the dataset), it gives the same probability for it (in the model.prediction step). I can't figure out why is this happening.
Here is my model
model = Sequential()
model.add(Embedding(max_words, 30, input_length=max_len))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Bidirectional(LSTM(32)))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='sigmoid'))
model.summary()
Here max_words = 2000 and max_len=300
Here is the model summary
Model: "sequential_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
embedding_3 (Embedding) (None, 300, 30) 60000
_________________________________________________________________
batch_normalization_5 (Batch (None, 300, 30) 120
_________________________________________________________________
activation_5 (Activation) (None, 300, 30) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 300, 30) 0
_________________________________________________________________
bidirectional_3 (Bidirection (None, 64) 16128
_________________________________________________________________
batch_normalization_6 (Batch (None, 64) 256
_________________________________________________________________
activation_6 (Activation) (None, 64) 0
_________________________________________________________________
dropout_4 (Dropout) (None, 64) 0
_________________________________________________________________
dense_3 (Dense) (None, 2) 130
=================================================================
Total params: 76,634
Trainable params: 76,446
Non-trainable params: 188
And here is the code, the size of my dataset is 20k, with 10% in testing.
model.compile(loss='sparse_categorical_crossentropy', metrics=['accuracy'], optimizer = 'adam')
history = model.fit(sequences_matrix, Y_train, batch_size=256, epochs=50, validation_split=0.1)

Answer 1

Try changing activation function of the last layer from sigmoid to softmax. It doesn't quite match the loss you are using (categorical cross-entropy). If you use sigmoid, then you only need one unit and should use binary cross-entropy loss.