Question

我正在编写代码以在python（Tensorflow）中对猫和猫进行分类，但是代码显示此错误：

IndexError：索引0超出了大小为0的轴0的范围

我被困在这里。任何帮助表示赞赏。

也请您帮帮我，我在这里无法弄清楚OneHotEncoder的工作方式。我不理解这里的逻辑。我在同一个上花了很多时间。

def reset_graph(seed=42):
    tf.reset_default_graph()
    tf.set_random_seed(seed)
    np.random.seed(seed)

reset_graph()
img_size = 64
num_channels = 3
img_size_flat = img_size * img_size * num_channels
img_shape = (img_size, img_size)
trainpath='C:\ProgramData\Anaconda3\train'
testpath='C:\ProgramData\Anaconda3\test'
labels = {'cats': 0, 'dogs': 1}
fc_size=32 #size of the output of final FC layer
num_steps=300
tf.logging.set_verbosity(tf.logging.INFO)

def read_images_classes(basepath,imgSize=img_size):
    image_stack = []
    label_stack = []
    for counter, l in enumerate(labels):
        path = os.path.join(basepath, l,'*g')
        for img in glob.glob(path):
            one_hot_vector = np.zeros(len(labels),dtype=np.int16)
            one_hot_vector[counter]=1
            image = cv2.imread(img)
            image_stack.append(im_resize)
            label_stack.append(labels[l])            
    return np.array(image_stack), np.array(label_stack)

X_train, y_train = read_images_classes(trainpath)
X_test, y_test = read_images_classes(testpath)b 
print('length of train image set',len(X_train))
print('X_data shape:', X_train.shape)
print('y_data shape:', y_train.shape)

fig1 = plt.figure() 
ax1 = fig1.add_subplot(2,2,1) 
img = cv2.resize(X_train[0],(64,64), interpolation=cv2.INTER_CUBIC)
ax1.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
plt.title(y_train[0])
plt.show()

Answer 1

IndexError：索引0超出了大小为0的轴0的边界

这意味着您没有要引用的索引。

由于这是一个二进制分类问题，因此您不需要预处理标签使用one_hot编码。如果标签有两个以上，则可以使用one_hot编码。

请使用Tensorflow引用猫和狗数据集的二进制分类代码

import os
import numpy as np
from keras import layers
import pandas as pd
from tensorflow.keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from tensorflow.keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras import regularizers, optimizers
from tensorflow.keras.preprocessing import image
from tensorflow.keras.preprocessing.image import ImageDataGenerator

import keras.backend as K

import keras.backend as K
K.set_image_data_format('channels_last')

from google.colab import drive
drive.mount('/content/drive')

train_dir  = '/content/drive/My Drive/Dogs_Vs_Cats/train'
test_dir = '/content/drive/My Drive/Dogs_Vs_Cats/test'

img_width, img_height = 300, 281
input_shape = img_width, img_height, 3

train_samples = 2000
test_samples = 1000
epochs = 30
batch_size = 32

train_datagen = ImageDataGenerator(
    rescale = 1. /255,
    shear_range = 0.2,
    zoom_range = 0.2,
    horizontal_flip = True)

test_datagen = ImageDataGenerator(
    rescale = 1. /255)

train_data = train_datagen.flow_from_directory(
    train_dir,
    target_size = (img_width, img_height),
    batch_size = batch_size,
    class_mode = 'binary')

test_data = test_datagen.flow_from_directory(
    test_dir,
    target_size = (img_width, img_height),
    batch_size = batch_size,
    class_mode = 'binary')


model = Sequential()

model.add(Conv2D(32, (7, 7), strides = (1, 1), input_shape = input_shape))
model.add(BatchNormalization(axis = 3))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (7, 7), strides = (1, 1)))
model.add(BatchNormalization(axis = 3))
model.add(Activation('relu'))
model.add(MaxPooling2D((2, 2)))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss = 'binary_crossentropy',
            optimizer = 'rmsprop',
            metrics = ['accuracy'])

model.build(input_shape)
model.summary() 


model.fit_generator(
        train_data,
        steps_per_epoch = train_samples//batch_size,
        epochs = epochs,
        validation_data = test_data,
        verbose = 1,
        validation_steps = test_samples//batch_size)

使用TensorFlow进行二进制图像分类

1 个答案: