def create_model():
    embedding_layer = Embedding(input_dim=100, output_dim=300,
                                input_length=100)
    model = Sequential()
    model.add(embedding_layer)
    model.add(Dropout(0.2))
    model.add(Conv1D(filters=100, kernel_size=4, padding='same', activation='relu'))
    model.add(MaxPooling1D(pool_size=4))
    model.add(LSTM(units=100, return_sequences=True))
    model.add(GlobalMaxPooling1D())
    model.add(Dense(1, activation='sigmoid'))
    ###### multiclassification #########
    #model.add(Dense(3, activation='softmax')) #I want to replace the above line with this for multi-classification but this didnt work
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

Programs/anaconda3/lib/python3.6/site-packages/keras/engine/training.py in _standardize_input_data(data=array([1, 1, 2, 1, 0, 0, 2, 1, 1, 2, 0, 0, 1, 1,...2, 0, 2, 1, 0, 2, 0, 1, 0, 0,
       1, 2, 2, 0]), names=['dense_1'], shapes=[(None, 3)], check_batch_axis=False, exception_prefix='model target')
    128                         raise ValueError(
    129                             'Error when checking ' + exception_prefix +
    130                             ': expected ' + names[i] +
    131                             ' to have shape ' + str(shapes[i]) +
    132                             ' but got array with shape ' +
--> 133                             str(array.shape))
        array.shape = (280, 1)
    134     return arrays
    135
    136
    137 def _standardize_sample_or_class_weights(x_weight, output_names, weight_type):

ValueError: Error when checking model target: expected dense_1 to have shape (None, 3) but got array with shape (280, 1)