Question

你好，我正在尝试使用我电脑上图像的数据制作张量流的基本示例。但是我一直都遇到此错误：“ ValueError：无法找到可以处理输入的数据适配器：，（包含类型为“”“的值）”

这是我生成数据的方式：

import numpy as np # for array operations
import matplotlib.pyplot as plt # to show image
import os # to move through directories
import cv2 # to make image operations
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()

这是我从中得到错误的张量流模型：

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

Answer 1

之后

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

您必须添加

y = np.array(y)

Tensorflow ValueError：无法找到可以处理输入的数据适配器

1 个答案: