你好，我正在尝试使用电脑上的图像数据来创建Tensorflow的基本mnist示例。但是我一直遇到这个错误：“ValueError: Failed to find data adapter that can handle input: , ( containing values of types {“”})”

这是我生成数据的方式：

import numpy as np # 用于数组操作
import matplotlib.pyplot as plt # 用于显示图像
import os # 用于遍历目录
import cv2 # 用于图像操作
import random
import pickle
DATADIR=r"C:\Users\...\mnist_png\training"
DIGITS = ["0","1","2","3","4","5","6","7","8","9"]
training_data = []
for digit in DIGITS:
    path = os.path.join(DATADIR, digit)
    class_num = DIGITS.index(digit)
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
        training_data.append([img_array, class_num])
random.shuffle(training_data)
X = []
y = []
for features, label in training_data:
    X.append(features)
    y.append(label)
X = np.array(X).reshape(-1, 28, 28, 1)
pickle_out = open("X.pickle", "wb")
pickle.dump(X, pickle_out)
pickle_out.close()
pickle_out = open("y.pickle", "wb")
pickle.dump(y, pickle_out)
pickle_out.close()

这是导致错误的Tensorflow模型：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle
X = pickle.load(open("X.pickle", "rb"))
y = pickle.load(open("y.pickle", "rb"))
X=X/255.0
model = Sequential()
model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(64))
model.add(Dense(10))
model.add(Activation('sigmoid'))
model.compile(
    loss="sparse_categorical_crossentropy",
    optimizer="adam",
    metrics=['accuracy']
    )
model.fit(X, y, batch_size=32, validation_split=0.1)

请帮助我

回答：

在

for features, label in training_data:
    X.append(features)
    y.append(label)

之后，你需要添加

y = np.array(y)