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值错误:用序列设置数组元素。尝试用CNN对图像进行分类

conv-neural-network tensorflow numpy python-3.x python

Waqar Danish · 技术社区 · 7 年前

我正在试着运行这个代码利用 卷积神经网络(CNN) . 这段代码可以在网上查到,但它对图像中是否有狗或猫进行了分类。它 工作正常 具有分类但是 当我更改培训和测试目录时 到X射线 我得到一个错误

值错误:用序列设置数组元素。

我试着看类似的问题,但不明白如何解决这个问题。

谢谢

    import cv2
    import os
    import numpy as np
    from random import shuffle
    from tqdm import tqdm

    '''Setting up the env'''

TRAIN_DIR = 'C:\\Users\\waqar\\Desktop\\Temp\\chest_xray\\train'
TEST_DIR = 'C:\\Users\\waqar\\Desktop\\Temp\\chest_xray\\test'
IMG_SIZE = 50
LR = 1e-3

'''Setting up the model which will help with tensorflow models'''
MODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '6conv-basic')

'''Labelling the dataset'''


def label_img(img):
    word_label = img.split('.')[-3]

    if word_label == 'cat':
        return [1, 0]
    elif word_label == 'dog':
        return [0, 1]


'''Creating the training data'''
def create_train_data():
    training_data = []
    for img in tqdm(os.listdir(TRAIN_DIR)):
        label = label_img(img)
        path = os.path.join(TRAIN_DIR, img)
        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
        training_data.append([np.array(img), np.array(label)])
    shuffle(training_data)
    np.save('train_data.npy', training_data)
    return training_data


'''Processing the given test data'''
def process_test_data():
    testing_data = []
    for img in tqdm(os.listdir(TEST_DIR)):
        path = os.path.join(TEST_DIR, img)
        img_num = img.split('.')[0]
        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))
        testing_data.append([np.array(img), img_num])
    shuffle(testing_data)
    np.save('test_data.npy', testing_data)
    return testing_data


'''Running the training and the testing in the dataset for our model'''
train_data = create_train_data()
test_data = process_test_data()

train_data = np.load('train_data.npy')
test_data = np.load('test_data.npy')

'''Creating the neural network using tensorflow'''
import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression

import tensorflow as tf

tf.reset_default_graph()
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')

convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 128, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 64, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = conv_2d(convnet, 32, 5, activation='relu')
convnet = max_pool_2d(convnet, 5)

convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)

convnet = fully_connected(convnet, 2, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR,
                     loss='categorical_crossentropy', name='targets')

model = tflearn.DNN(convnet, tensorboard_dir='log')


train = train_data[:-500]
test = train_data[-500:]

'''Setting up the features and lables'''


X = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
Y = [i[1] for i in train]
test_x = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
test_y = [i[1] for i in test]

'''Fitting the data into our model'''

model.fit({'input': X}, {'targets': Y}, n_epoch=5,
          validation_set=({'input': test_x}, {'targets': test_y}),
          snapshot_step=500, show_metric=True, run_id=MODEL_NAME)
model.save(MODEL_NAME)

'''Testing the data'''
import matplotlib.pyplot as plt

# if you need to create the data:
test_data = process_test_data()
# if you already have some saved:
test_data = np.load('test_data.npy')

fig = plt.figure()

for num, data in enumerate(test_data[:20]):
    # cat: [1, 0]
    # dog: [0, 1]

    img_num = data[1]
    img_data = data[0]

    y = fig.add_subplot(4, 5, num + 1)
    orig = img_data
    data = img_data.reshape(IMG_SIZE, IMG_SIZE, 1)


    model_out = model.predict([data])[0]

    if np.argmax(model_out) == 1:
        str_label = 'Dog'
    else:
        str_label = 'Cat'

    y.imshow(orig, cmap='gray')
    plt.title(str_label)
    y.axes.get_xaxis().set_visible(False)
    y.axes.get_yaxis().set_visible(False)
plt.show()**

显示的完整错误为:

C:\Users\waqar\AppData\Local\Programs\Python\Python36\python.exe C:/Users/waqar/.PyCharm2018.2/config/scratches/scratch.py
100%|ââââââââââ| 5200/5200 [01:00<00:00, 85.55it/s]
Traceback (most recent call last):
  File "C:/Users/waqar/.PyCharm2018.2/config/scratches/scratch.py", line 87, in <module>
    train_data = create_train_data()
  File "C:/Users/waqar/.PyCharm2018.2/config/scratches/scratch.py", line 63, in create_train_data
    np.save('train_data.npy', training_data)
  File "C:\Users\waqar\AppData\Local\Programs\Python\Python36\lib\site-packages\numpy\lib\npyio.py", line 509, in save
    arr = np.asanyarray(arr)
  File "C:\Users\waqar\AppData\Local\Programs\Python\Python36\lib\site-packages\numpy\core\numeric.py", line 544, in asanyarray
    return array(a, dtype=long, copy=False, order=order, subok=True)
ValueError: setting an array element with a sequence.

Process finished with exit code 1

定义标签(img): word_label=img.split('.')[-3]

    if word_label == 'cat':
        return [1, 0]
    elif word_label == 'dog':
        return [0, 1]
    else :                    #add this line
        return [0, 0]

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