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我正在创建一个神经网络来玩井字游戏。我正在使用tflearn来处理神经网络。这是我正在使用的训练数据TFlearn错误输入形状作为输入
[[[1, 1, 1, 0, -1, -1, -1, 0, 0], 6], [[1, 1, 1, 0, -1, -1, -1, 0, 0], 3], [[1, 1, 1, 0, -1, -1, -1, 0, 0], 5],
[[1, 1, 1, 0, -1, -1, -1, 0, 0], 2], [[1, 1, 1, 0, -1, -1, -1, 0, 0], 7], [[1, 1, 1, 0, -1, -1, -1, 0, 0], 1],
[[0, 0, 1, -1, 1, 0, 1, -1, -1], 4], [[0, 0, 1, -1, 1, 0, 1, -1, -1], 3], [[0, 0, 1, -1, 1, 0, 1, -1, -1], 8],
[[0, 0, 1, -1, 1, 0, 1, -1, -1], 5], [[0, 0, 1, -1, 1, 0, 1, -1, -1], 9], [[0, 0, 1, -1, 1, 0, 1, -1, -1], 7],
[[0, -1, 1, 0, 1, 0, 1, -1, -1], 9], [[0, -1, 1, 0, 1, 0, 1, -1, -1], 3], [[0, -1, 1, 0, 1, 0, 1, -1, -1], 2],
[[0, -1, 1, 0, 1, 0, 1, -1, -1], 5], [[0, -1, 1, 0, 1, 0, 1, -1, -1], 8], [[0, -1, 1, 0, 1, 0, 1, -1, -1], 7],
[[1, -1, -1, 0, 1, 0, -1, 0, 1], 2], [[1, -1, -1, 0, 1, 0, -1, 0, 1], 1], [[1, -1, -1, 0, 1, 0, -1, 0, 1], 3],
[[1, -1, -1, 0, 1, 0, -1, 0, 1], 5], [[1, -1, -1, 0, 1, 0, -1, 0, 1], 7], [[1, -1, -1, 0, 1, 0, -1, 0, 1], 9],
[[-1, 1, -1, 0, 1, 0, -1, 1, 0], 1], [[-1, 1, -1, 0, 1, 0, -1, 1, 0], 5], [[-1, 1, -1, 0, 1, 0, -1, 1, 0], 3],
[[-1, 1, -1, 0, 1, 0, -1, 1, 0], 2], [[-1, 1, -1, 0, 1, 0, -1, 1, 0], 7], [[-1, 1, -1, 0, 1, 0, -1, 1, 0], 8]]
它包含当前棋盘状态9个数字的列表以及棋子1号码的位置。我把这个板子放在数据和标签上。当我喂数据到nerual网络我得到这个错误
ValueError: Cannot feed value of shape (30, 9) for Tensor u'input/X:0', which has shape '(?, 30, 9)'
这是我用来创建和训练模型
def create_model():
network = input_data(shape=(None, 30, 9), name='input')
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 256, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 128, activation='relu')
network = dropout(network, 0.8)
network = fully_connected(network, 9, activation='linear')
network = regression(network, optimizer='adam', learning_rate=0.01, loss='mean_square', name='targets')
model = tflearn.DNN(network, tensorboard_dir='log')
return model
def train_model():
training_data = [[[1, 1, 1, 0, -1, -1, -1, 0, 0], 6], [[1, 1, 1, 0, -1, -1, -1, 0, 0], 3], [[1, 1, 1, 0, -1, -1, -1, 0, 0], 5],
[[1, 1, 1, 0, -1, -1, -1, 0, 0], 2], [[1, 1, 1, 0, -1, -1, -1, 0, 0], 7], [[1, 1, 1, 0, -1, -1, -1, 0, 0], 1],
[[0, 0, 1, -1, 1, 0, 1, -1, -1], 4], [[0, 0, 1, -1, 1, 0, 1, -1, -1], 3], [[0, 0, 1, -1, 1, 0, 1, -1, -1], 8],
[[0, 0, 1, -1, 1, 0, 1, -1, -1], 5], [[0, 0, 1, -1, 1, 0, 1, -1, -1], 9], [[0, 0, 1, -1, 1, 0, 1, -1, -1], 7],
[[0, -1, 1, 0, 1, 0, 1, -1, -1], 9], [[0, -1, 1, 0, 1, 0, 1, -1, -1], 3], [[0, -1, 1, 0, 1, 0, 1, -1, -1], 2],
[[0, -1, 1, 0, 1, 0, 1, -1, -1], 5], [[0, -1, 1, 0, 1, 0, 1, -1, -1], 8], [[0, -1, 1, 0, 1, 0, 1, -1, -1], 7],
[[1, -1, -1, 0, 1, 0, -1, 0, 1], 2], [[1, -1, -1, 0, 1, 0, -1, 0, 1], 1], [[1, -1, -1, 0, 1, 0, -1, 0, 1], 3],
[[1, -1, -1, 0, 1, 0, -1, 0, 1], 5], [[1, -1, -1, 0, 1, 0, -1, 0, 1], 7], [[1, -1, -1, 0, 1, 0, -1, 0, 1], 9],
[[-1, 1, -1, 0, 1, 0, -1, 1, 0], 1], [[-1, 1, -1, 0, 1, 0, -1, 1, 0], 5], [[-1, 1, -1, 0, 1, 0, -1, 1, 0], 3],
[[-1, 1, -1, 0, 1, 0, -1, 1, 0], 2], [[-1, 1, -1, 0, 1, 0, -1, 1, 0], 7], [[-1, 1, -1, 0, 1, 0, -1, 1, 0], 8]]
x = []
y = []
for i in training_data:
x.append(i[0])
y.append(i[1])
model = create_model()
model.fit({'input': x}, {'targets': y}, n_epoch=10, snapshot_step=500, show_metric=True, run_id='openai_learning')
当我改变,我得到了一个不同的饲料错误:ValueError:不能馈送形状(1,)的张量u'targets/Y:0',其形状'( ?,9)' – Loanb222
您正在收到新的错误,因为您也有与目标相同的问题。您已经解决了以前的错误。为了解决这个新问题,我建议你在model.fit之前包含 y = np.reshape(y,( - 1,9)) – satyajith