setting an array element with a sequence.

Question

import tensorflow as tf
from matplotlib import pyplot as plt
import os
import numpy as np
import glob
from torch.utils import data
from PIL import Image
from torchvision import transforms


# 数据集内图片的验证
os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
dataset_path = "rps_data_sample"

labels = []
for i in os.listdir(dataset_path):
    if os.path.isdir(os.path.join(dataset_path, i)):
        labels.append(i)

NUM_EXAMPLES = 5

for label in labels:
    label_dir = os.path.join(dataset_path, label)
    example_filenames = os.listdir(label_dir)[:NUM_EXAMPLES]
    fig, axs = plt.subplots(1, 5, figsize=(10,2))
    for i in range(NUM_EXAMPLES):
        axs[i].imshow(plt.imread(os.path.join(label_dir, example_filenames[i])))
        axs[i].get_xaxis().set_visible(False)
        axs[i].get_yaxis().set_visible(False)
    fig.suptitle(f'Showing {NUM_EXAMPLES} examples for {label}')


# 创建data.Dataset子类Mydataset
class Mydataset(data.Dataset):
    def __init__(self,root):
        self.imgs_path = root
    def __getitem__(self, index):
        img_path = self.imgs_path[index]
        return img_path
    def __len__(self):
        return len(self.imgs_path)

# glob遍历数据路径
all_imgs_path = glob.glob('rps_data_sample\*\*.jpg')
for var in all_imgs_path:
    print(var)

# 建立gesture_data
gesture_dataset = Mydataset(all_imgs_path)
print(len(gesture_dataset))

# path迭代
species = ['none','paper','rock','scissors']
species_to_id = dict((c,i) for i,c in enumerate(species))
id_to_species = dict((v,k) for k,v in species_to_id.items())
all_labels = []
for img in all_imgs_path:
    for i,c in enumerate(species):
        if c in img:
            all_labels.append(i)

transform = transforms.Compose([transforms.ToTensor()])
index = np.random.permutation(len(all_imgs_path))

all_imgs_path = np.array(all_imgs_path)[index]
all_labels = np.array(all_labels)[index]

p = int(len(all_imgs_path)*0.8)
x_train_ = all_imgs_path[:p]
y_train_ = all_labels[:p]
x_test_ = all_imgs_path[p:]
y_test_ = all_imgs_path[p:]


class MyDatasetpro(data.Dataset):
    def __init__(self,img_paths ,labels, transform):
        self.imgs = img_paths
        self.labels = labels
        self.transforms = transform
    def __getitem__(self, index):
        img = self.imgs[index]
        label = self.labels[index]
        pil_img = Image.open(img)
        data = self.transforms(pil_img)
        return data, label
    def __len__(self):
        return len(self.imgs)

x_train = MyDatasetpro(x_train_, y_train_, transform)
x_test = MyDatasetpro(x_test_, y_test_, transform)
y_train = data.DataLoader(x_train, batch_size=5,shuffle=True)
y_test = data.DataLoader(x_test, batch_size=5,shuffle=True)

# tensorflow训练
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(filters=32,kernel_size=(5,5),padding='same'),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Conv2D(filters=96,kernel_size=(5,5),padding='same'),
    tf.keras.layers.BatchNormalization(),
    tf.keras.layers.Activation('relu'),
    tf.keras.layers.AveragePooling2D(pool_size=(3,3),padding='valid'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(256,activation='relu',kernel_regularizer=tf.keras.regularizers.l1()),
    tf.keras.layers.Dense(64,activation='relu',kernel_regularizer=tf.keras.regularizers.l1()),
    tf.keras.layers.Dense(5,activation='softmax',kernel_regularizer=tf.keras.regularizers.l1())
])
model.compile(optimizer='nadam',loss="sparse_categoriical_crossentropy", metrics=["sparse_categorical_accuracy"])
# 由于神经层较多，cpu一次可能跑不了，断点续训
# checkpoint_save_path = './rps_data_sample/checkpoint.ckpt'
# if os.path.exists(checkpoint_save_path+'index'):
#   print('load model')

# model.load_weights(checkpoint_save_path)
# cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
#                                                 save_weights_only=True,
#                                                 save_best_only=True)

history = model.fit(np.array(x_train),y_train,batch_size=32, epochs=1000, validation_data=(np.array(x_test),y_test),validation_freq=1)
model.summary()

file = open('/weights.txt','w')
for v in model.trainable_variables:
    file.write(str(v.name)+'\n')
    file.write(str(v.shape)+'\n')
    file.write(str(v.numpy()+'\n'))
file.close()

# 可视化
acc=history.history['sparse_categorical_accuracy']
val_acc=history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss=history.history['val_loss']

plt.subplot(1,2,1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label="Validation Accuracy")
plt.title('Training and Validation Accuracy')

结果报setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (400, 2) + inhomogeneous part.
怎么处理啊

Answer 1

以下内容由CHATGPT及阿里嘎多学长共同生成、有用望采纳：

---

您遇到的错误信息 "setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (400, 2) + inhomogeneous part." 指出您尝试用一个序列来设置数组元素，但是这个数组在两个维度之后的形状是不均匀的。这通常发生在尝试将一个形状不匹配的序列赋值给numpy数组或类似结构时。

从您提供的代码片段来看，问题可能出在以下几行：

x_train_ = all_imgs_path[:p]
y_train_ = all_labels[:p]
x_test_ = all_imgs_path[p:]
y_test_ = all_labels[p:]

这里，您将 all_imgs_path 和 all_labels 分别切片成了训练集和测试集。但是，当您创建 MyDatasetpro 类的实例时，您需要确保 img_paths 和 labels 的长度是一致的。如果 all_imgs_path 和 all_labels 的长度不一致，这将导致错误。

此外，您在创建 DataLoader 对象时，似乎将 DataLoader 直接赋值给了 y_train 和 y_test，这是不正确的。DataLoader 是一个迭代器，用于批量加载数据，您应该将其与数据集一起使用。例如：

y_train = data.DataLoader(x_train, batch_size=5, shuffle=True)
y_test = data.DataLoader(x_test, batch_size=5, shuffle=False)

请注意，y_train 和 y_test 应该是 DataLoader 对象，而不是标签数组本身。

最后，您在保存模型权重时的代码存在一些问题。file.write(str(v.numpy()+'\n')) 这一行缺少了一个括号，应该是 file.write(str(v.numpy())+'\n')。

修复这些问题后，您的代码应该能够正常运行。如果问题仍然存在，请提供更多的上下文信息，以便我能够提供更具体的帮助。