def single_layer_model(input_shape):
    model = Sequential()

    # 2D convolution layer (e.g. spatial convolution over images).
   # model.add(Conv2D(32, (3, 3), input_shape=input_shape))
    model.add(Dense(30, input_shape=input_shape, activation="relu", kernel_initializer="normal"))
    # Flattens the input. Does not affect the batch size.
    model.add(Flatten())

    # Regular densely-connected NN layer.
    model.add(Dense(10,  activation='softmax', kernel_initializer="normal"))

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