# For a single-input model with 2 classes (binary classification):
    model = Sequential()
    model.add(Dense(32, activation='relu', input_dim=100))
    model.add(Dense(1, activation='sigmoid'))

    model.compile(optimizer='rmsprop', loss='binary_crossentropy',
    metrics=['accuracy'])

model = Sequential()

model.add(LSTM(32, return_sequences=True, stateful=True,
batch_input_shape=(batch_size, timesteps, data_dim)))

model.add(LSTM(32, return_sequences=True, stateful=True))
model.add(LSTM(32, stateful=True))
model.add(Dense(10, activation='softmax'))

model.compile(loss='categorical_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

model = Sequential()
model.add(Conv1D(64, 3, activation='relu', input_shape=(seq_length, 100)))
model.add(Conv1D(64, 3, activation='relu'))

model.add(MaxPooling1D(3))
model.add(Conv1D(128, 3, activation='relu'))
model.add(Conv1D(128, 3, activation='relu'))
model.add(GlobalAveragePooling1D())

model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))