ValueError：检查输入时出错：预期dense_1_input的形状为（8，）但得到的数组形状为（1，）答案

【问题标题】：ValueError: Error when checking input: expected dense_1_input to have shape (8,) but got array with shape (1,)ValueError：检查输入时出错：预期dense_1_input的形状为（8，）但得到的数组形状为（1，）
【发布时间】：2020-10-25 06:20:45
【问题描述】：

我正在尝试训练一个神经网络来反弹一个球，但我在预测球的运动时遇到了问题，得到了错误 ValueError: Error when checking input: expected dense_1_input to have shape (8,) but got array with shape (1,)
我的代码：

from keras.models import Sequential
from keras.layers import Dense
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import csv
import pygame

running=True

def main():
    # load training data
    data_path = 'x.dat'
    with open(data_path, 'r') as f:
        reader = csv.reader(f, delimiter=',')
        headers = next(reader)
        x_train = np.array(list(reader)).astype(float)
    data_path = 'y.dat'
    with open(data_path, 'r') as f:
        reader = csv.reader(f, delimiter=',')
        headers = next(reader)
        y_train = np.array(list(reader)).astype(float)
    
    # debug print statement
    print(x_train)
    
    # define the keras model
    model = Sequential()
    model.add(Dense(8, input_shape=(8,), activation='relu'))
    model.add(Dense(10, activation='relu'))
    model.add(Dense(4, activation='relu'))

    # compile the keras model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    
    # fit the keras model on the dataset
    model.fit(x_train, y_train, epochs=1, batch_size=1000)

    # pygame initialization to visualize the ball
    global running, screen

    pygame.init()
    screen = pygame.display.set_mode((200, 200))
    pygame.display.set_caption("BallPhysics")
    screen.fill((255,255,255))
    pygame.display.update()

    # ball info
    x = 25
    y = 10
    xVel = -2
    yVel = 0
    gravity = 0.1
    elasticity = 0.9999
    radius = 10
    friction = 0.999

    while running:
        screen.fill((255,255,255))
        ev = pygame.event.get()

        # draw ball
        pygame.draw.circle(screen, (255, 0, 0), (x,y), radius)
        pygame.display.update()

        # make input array
        inp = [x/200,y/200,((xVel/10)+1)/2,((yVel/10)+1)/2,gravity,elasticity,radius/50.0,friction]
        print(inp)
        out = model.predict(inp)

        # set ball position and velocity to NN output
        x = out[0][0]
        y = out[0][1]
        xVel = out[0][2]
        yVel = out[0][3]
        
        # event handling
        for event in ev:
            if event.type == pygame.QUIT:
                running = False
                pygame.display.quit()
                pygame.quit()
        
            pygame.display.flip()

main()

调试打印语句打印出来

[[0.025568 0.131659 0.755605 ... 0.414219 0.094692 0.678865]
...
[0.08742  0.08742  0.5      ... 0.250432 0.699359 0.179118]]

这有点令人困惑，因为我注意到一件事是打印出来的数组没有逗号，而我制作的数组确实有逗号。这可能与它有关，但我不知道是什么。任何帮助表示赞赏。

堆栈跟踪：

Traceback (most recent call last):
  File "/Users/grimtin10/Documents/Python Projects/BallPhysics/BallPhysics.py", line 78, in <module>
    main()
  File "/Users/grimtin10/Documents/Python Projects/BallPhysics/BallPhysics.py", line 63, in main
    out = model.predict(inp)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/engine/training.py", line 1149, in predict
    x, _, _ = self._standardize_user_data(x)
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/engine/training.py", line 751, in _standardize_user_data
    exception_prefix='input')
  File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/keras/engine/training_utils.py", line 138, in standardize_input_data
    str(data_shape))
ValueError: Error when checking input: expected dense_1_input to have shape (8,) but got array with shape (1,)

【问题讨论】：

我不知道问题出在哪里，但不是逗号。 Numpy 打印出不带逗号的数组，可能是为了节省空间。我觉得这很烦人。
啊好的，很高兴知道这不是问题
其实是要打印报表的。第一个打印语句是打印x_train，第二个是打印inp。后者只是 8 个元素的列表。似乎 model.predict 正在抱怨该输入。我不太了解这个包。这是传递给 model.predict 的正确参数吗？

标签： python tensorflow keras

【解决方案1】：

好吧，所以，我想通了。事实证明，NN 期望 批量输入， 导致单个数组无法工作。我对 tf 和 keras 不是很熟悉，所以这就是问题所在。使用np.reshape(inp,(1,8)) 修复它。

【讨论】：