我是深度学习和Keras的新手，我想要完成一个任务：使用50个周期在训练数据上训练模型。
我编写了以下代码：
import pandas as pdfrom tensorflow.python.keras import Sequentialfrom tensorflow.python.keras.layers import Densefrom sklearn.model_selection import train_test_splitconcrete_data = pd.read_csv('https://cocl.us/concrete_data')n_cols = concrete_data.shape[1]model = Sequential()model.add(Dense(units=10, activation='relu', input_shape=(n_cols,)))model.compile(loss='mean_squared_error', optimizer='adam')x = concrete_data.Cementy = concrete_data.drop('Cement', axis=1)xTrain, xTest, yTrain, yTest = train_test_split(x, y, test_size = 0.3)但是当我这样拟合我的模型时：
model.fit(xTrain, yTrain, validation_data=(xTrain, yTrain), epochs=50)我遇到了以下错误：
Epoch 1/50---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-83-489dd99522b4> in <module>()----> 1 model.fit(xTrain, yTrain, validation_data=(xTrain, yTrain), epochs=50)10 frames/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs) 966 except Exception as e: # pylint:disable=broad-except 967 if hasattr(e, "ag_error_metadata"):--> 968 raise e.ag_error_metadata.to_exception(e) 969 else: 970 raiseValueError: in user code: /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:503 train_function * outputs = self.distribute_strategy.run( /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:951 run ** return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2290 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2649 _call_for_each_replica return fn(*args, **kwargs) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:464 train_step ** y_pred = self(x, training=True) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:885 __call__ self.name) /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/input_spec.py:216 assert_input_compatibility ' but received input with shape ' + str(shape)) ValueError: Input 0 of layer sequential_2 is incompatible with the layer: expected axis -1 of input shape to have value 9 but received input with shape [None, 1]我的具体数据是：
这是x和y的形状（以*分隔）：
我真的不知道问题出在哪里。
回答：
我认为你需要像下面这样更改输入形状：
input_shape=(n_cols,) =>> input_shape=(n_cols-1,)一开始，你的数据包含了特征和目标数据，所以形状包括了这两者。你需要从中减去1来指定输入形状。
另一个问题是你需要交换x和y的数据。我认为你想用数据集的其余部分来预测Cement。所以Cement信息应该存储在y中，而数据集的其余部分应该在x中。
此外，你还需要更改这部分代码。
model.fit(xTrain, yTrain, validation_data=(xTrain, yTrain), epochs=50)在训练和验证时使用相同的数据是没有意义的。你可以指定验证比例，让Keras自动为你完成。
