找不到可以处理输入的数据适配器：<类'numpy.ndarray'>，(<类'list'>c。

下面我想把笔记本中的图片存储到名为X_data的变量中，使用的函数是 glob 然后分成训练集和测试集，再测试模型。

 import cv2
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras import datasets, layers, models
 import matplotlib.pyplot as plt
 import glob

 X_data = []
 files = glob.glob ("*.png")
 for myFile in files:
    image = cv2.imread(myFile)
    X_data.append(image)

 print('X_data shape:', np.array(X_data).shape)

import numpy
numpy.random.shuffle(X_data)
training, test = X_data[:80,:], X_data[80:,:]

model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10))
model.summary()
model.compile(optimizer='adam',
                  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  metrics=['accuracy'])
history = model.fit(training, labels_train, epochs=10, 
                                    validation_data=(test, labels_test))

但我得到的是

ValueError: Failed to find data adapter that can handle input: <class 'numpy.ndarray'>, (<class 'list'> containing values of types {"<class 'int'>"})

有什么想法吗？

0
投票

我在你的代码中看到几个问题。

两者 train 和 test 是 Lists，不 Numpy Arrays 同样的情况可能发生在 Labels 该部分代码不共享）。

该部分代码。

X_data = [] files = glob.glob ("*.png") for myFile in files: image = cv2.imread(myFile) X_data.append(image)

返回一个 List of Strings而不是 Numpy Array 的Floats和代码。

training = training.astype(float) 和 test = test.astype(float) 不会解决这个问题。

['cat.373.jpg', 'cat.843.jpg', 'cat.970.jpg', 'cat.797.jpg', 'cat.786.jpg',
'cat.1.jpg', 'cat.172.jpg', 'cat.660.jpg'...........]

您是否有任何具体的原因想要使用该函数。glob.glob 访问 Images 因为有更好的方法来实现。

其中一个最有效和最优化的方法是使用 Keras ImageDataGenerator 和 ImageDataGenerator.flow_from_directory.

例如，如果你的 Image Data Directory 有文件夹，每个文件夹对应每个类，如下截图所示。

enter image description here

代码来展示如何使用 ImageDataGenerator，以及每行代码的详细解释在下面提到。

train_datagen = ImageDataGenerator(rescale=1./255, # Normalizes every pixel value
    validation_split=0.2) # Setting Validation Data as 20% of Total Data

train_generator = train_datagen.flow_from_directory(
    Image_data_dir, # Traverses through all the Sub Folders (Category) inside this dir
    target_size=(img_height, img_width), # Sets the Image Size
    batch_size=batch_size, # Generates batches of `batch_size`
    class_mode='categorical', # Will Consider Labels as Categorical
    shuffle = True, # Shuffles the Data
    subset='training') # Considers 80% as training data

# Since we don't have separate directory for Validation Data and since we want the Total Data to be Partitioned, we should use "train_datagen"
validation_generator = train_datagen.flow_from_directory(
    Image_data_dir, # Should use the Same Dir as Training for Splitting
    target_size=(img_height, img_width), 
    batch_size=batch_size,
    class_mode='categorical',
    shuffle = True, # Shuffles the Data
    subset='validation') # Considers 20% as Validation data

# Then you can train the model using the code mentioned below
model.fit(
    train_generator,
    steps_per_epoch = train_generator.samples // batch_size,
    validation_data = validation_generator, 
    validation_steps = validation_generator.samples // batch_size,
    epochs = nb_epochs)

希望这能解决你的问题。如果你仍然面临任何错误，请分享完整的代码和错误跟踪。

祝你学习愉快