Keras顺序模型输入形状

我想训练一个基于numpy数组的神经网络，该数组有4个条目作为X数据，另一个数组有一个条目作为y数据。

X_train = [x1，x2，x3，x4]
y_train = [y1]

我认为这很简单，但是我无法使输入形状起作用。我还发现很少有关输入形状工作方式的信息：您是否仅需要指定X数据？y数据呢？

我已经尝试设置input_dim = 4，因为这是要做的第一件事，但出现以下错误： Error when checking input: expected dense_1_input to have shape (4,) but got array with shape (1,)

然后，我尝试设置input_dim =（4，1），因为我认为y数据导致了该问题。但是我又收到一条错误消息：Error when checking input: expected dense_1_input to have 3 dimensions, but got array with shape (4, 1)

这是代码：

# importing the packages
import gym
import numpy as np
from collections import deque

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
from keras.wrappers.scikit_learn import KerasRegressor

from joblib import Parallel

# creating the environment
env = gym.make('CartPole-v1')

#defining global variables
lr=0.0001
decay=0.001
batch_size=None

# creating a deep learning model with keras
def model():
    model = Sequential()

    model.add(Dense(64, input_dim=4, activation='relu'))
    model.add(Dense(32, activation='relu'))
    model.add(Dense(16, activation='relu'))

    model.add(Dense(1, activation='sigmoid'))

    model.compile(Adam(lr=lr, decay=decay), loss='mse')
    model.summary()
    return model

# running the game
for i_episodes in range(200):
    env.reset()
    for i in range(100):
        env.render()
        action = env.action_space.sample()
        observation, reward, done, info = env.step(action)

        # observation = ndarray float64
        # reward = float
        # done = bool
        # action = int
        # info = empty

        observation = np.asarray(observation)
        reward = np.asarray(reward)
        action = np.asarray(action)

        # print(observation.dtype, reward.dtype, action.dtype)
        # print(observation.shape, action.shape)

        estimator = KerasRegressor(build_fn=model, epochs=30, batch_size=3, verbose=1)
        estimator.fit(observation, action)

        if done:
            break
env.close()

如果有人可以解释输入形状的工作原理，将不胜感激。