检查输入时出错：预期输入具有3维，但数组的形状为（50，1）

Question

这真杀了我。为什么说输入数组是（50,1）却是（50,629,160）？最初我以为Conv1D层给我带来了麻烦，但看来它在输入层中，对吧？

    # keras.__version__ = 2.2.4
    # X_train.shape = (50,629,160), X_train[0].shape = (629,160)
    # y_train.shape = (50,)

    dim = X_train[0].shape

    input1 = Input(shape=dim,name='input_1')
    conv1 = Conv1D(filters=32, kernel_size=2, strides=1, activation='relu', name='conv1', input_shape=dim)(input1)
    maxpool1 = MaxPool1D(pool_size=2, name='maxpool1')(conv1)
    conv2 = Conv1D(filters=64, kernel_size=2, strides=1, activation='relu', name='conv2')(maxpool1)
    maxpool2 = MaxPool1D(pool_size=2, name='maxpool2')(conv2)
    conv3 = Conv1D(filters=128, kernel_size=2, strides=1, activation='relu', name='conv3')(maxpool2)
    maxpool3 = MaxPool1D(pool_size=2, name='maxpool3')(conv3)
    flat1 = Flatten(name='flat1')(maxpool3)
    dense1 = Dense(256, activation='relu', name='dense1')(flat1)
    dense2 = Dense(128, activation='relu', name='dense2')(dense1)
    output1 = Dense(1, activation='sigmoid', name='output')(dense2)

    model = Model(inputs=input1,outputs=output1)
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    model.fit(x=X_train,y=y_train, epochs=30, validation_split=0.1, batch_size=32)

和错误代码：

Error when checking input: expected input_1 to have 3 dimensions, but got array with shape (50, 1)