erlenda_pong_parallel.py 文件源码

def get_dense_model():
    """Make keras model"""

    learning_rate=1e-4
    inp = Input(shape=(80*80,))
    h = Dense(200, activation='relu')(inp)
    out = Dense(1, activation='sigmoid')(h)
    model = Model(inp, out)
    optim = RMSprop(learning_rate)
    model.compile(optim, 'binary_crossentropy')
    try:
        model.load_weights('mod_weights_binary.h5')
        print('weights loaded')
    except:
        pass
    return model