我正在使用googlenet体系结构来训练包含两个类的数据集的神经网络。我正在使用“ create_training_data（）”函数创建数据集，该函数会从相应的类文件夹中导入图像。文件夹并在运行代码并显示错误时说“在0个样本上进行训练，在0个样本上进行验证” “ UnboundLocalError：分配前已引用本地变量'batch_index'”
这是我的完整代码：
import keras
from sklearn.preprocessing import LabelBinarizer
from keras.layers.core import Layer
import keras.backend as K
import tensorflow as tf
from keras.models import Model
from keras.layers import Conv2D, MaxPool2D, \
Dropout, Dense, Input, concatenate, \
GlobalAveragePooling2D, AveragePooling2D,\
Flatten
import cv2
import numpy as np
import os
from tqdm import tqdm
from keras import backend as K
from keras.utils import np_utils
import pickle
import os
import math
from keras.optimizers import SGD
from keras.callbacks import LearningRateScheduler
from tensorflow import reset_default_graph
tf.compat.v1.reset_default_graph
num_classes = 2
DATADIR = r"C:\Users\Soogareddy\Desktop\FINAL"
CATEGORIES = ["DEFECT","NONDEFECT"]
img_array = []
training_data = []
def create_training_data():
for category in CATEGORIES: # do dogs and cats
path = os.path.join(DATADIR,category) # create path to dogs and cats
class_num = CATEGORIES.index(category) # get the classification (0 or a 1). 0=dog 1=cat 2=flower
for img in tqdm(os.listdir(path)): # iterate over each image per dogs and cats
try:
img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR) # convert to array
img_array = cv2.cvtColor(img_array,cv2.COLOR_BGR2RGB)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) # resize to normalize data size
training_data.append([new_array, class_num]) # add this to our training_data
except Exception as e: # in the interest in keeping the output clean...
pass # add this to our training_data
create_training_data()
IMG_SIZE = 224
import random
random.shuffle(training_data)
T = []
y = []
for features, label in training_data:
T.append(features)
y.append(label)
y = to_categorical(y)
T = np.array(T).reshape( -1 ,IMG_SIZE, IMG_SIZE, 3)
T = T.astype('float32')
T = T/255.0
pickle_out = open("T.pickle","wb")
pickle.dump(T, pickle_out)
pickle_out.close()
pickle_out = open("y.pickle","wb")
pickle.dump(y, pickle_out)
pickle_out.close()
pickle_in = open("T.pickle","rb")
T = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
#T = T/255.0
def inception_module(x,
filters_1x1,
filters_3x3_reduce,
filters_3x3,
filters_5x5_reduce,
filters_5x5,
filters_pool_proj,
name=None):
conv_1x1 = Conv2D(filters_1x1, (1, 1), padding='same', activation='relu', kernel_initializer=kernel_init,
bias_initializer=bias_init)(x)
conv_3x3 = Conv2D(filters_3x3_reduce, (1, 1), padding='same', activation='relu', kernel_initializer=kernel_init,
bias_initializer=bias_init)(x)
conv_3x3 = Conv2D(filters_3x3, (3, 3), padding='same', activation='relu', kernel_initializer=kernel_init,
bias_initializer=bias_init)(conv_3x3)
conv_5x5 = Conv2D(filters_5x5_reduce, (1, 1), padding='same', activation='relu', kernel_initializer=kernel_init,
bias_initializer=bias_init)(x)
conv_5x5 = Conv2D(filters_5x5, (5, 5), padding='same', activation='relu', kernel_initializer=kernel_init,
bias_initializer=bias_init)(conv_5x5)
pool_proj = MaxPool2D((3, 3), strides=(1, 1), padding='same')(x)
pool_proj = Conv2D(filters_pool_proj, (1, 1), padding='same', activation='relu', kernel_initializer=kernel_init,
bias_initializer=bias_init)(pool_proj)
output = concatenate([conv_1x1, conv_3x3, conv_5x5, pool_proj], axis=3, name=name)
return output
kernel_init = keras.initializers.glorot_uniform()
bias_init = keras.initializers.Constant(value=0.2)
input_layer = Input(shape=(224, 224, 3))
x = Conv2D(64, (7, 7), padding='same', strides=(2, 2), activation='relu', name='conv_1_7x7/2', kernel_initializer=kernel_init, bias_initializer=bias_init)(input_layer)
x = MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_1_3x3/2')(x)
x = Conv2D(64, (1, 1), padding='same', strides=(1, 1), activation='relu', name='conv_2a_3x3/1')(x)
x = Conv2D(192, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv_2b_3x3/1')(x)
x = MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_2_3x3/2')(x)
x = inception_module(x,
filters_1x1=64,
filters_3x3_reduce=96,
filters_3x3=128,
filters_5x5_reduce=16,
filters_5x5=32,
filters_pool_proj=32,
name='inception_3a')
x = inception_module(x,
filters_1x1=128,
filters_3x3_reduce=128,
filters_3x3=192,
filters_5x5_reduce=32,
filters_5x5=96,
filters_pool_proj=64,
name='inception_3b')
x = MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_3_3x3/2')(x)
x = inception_module(x,
filters_1x1=192,
filters_3x3_reduce=96,
filters_3x3=208,
filters_5x5_reduce=16,
filters_5x5=48,
filters_pool_proj=64,
name='inception_4a')
x1 = AveragePooling2D((5, 5), strides=3)(x)
x1 = Conv2D(128, (1, 1), padding='same', activation='relu')(x1)
x1 = Flatten()(x1)
x1 = Dense(1024, activation='relu')(x1)
x1 = Dropout(0.7)(x1)
x1 = Dense(1, activation='softmax', name='auxilliary_output_1')(x1)
x = inception_module(x,
filters_1x1=160,
filters_3x3_reduce=112,
filters_3x3=224,
filters_5x5_reduce=24,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4b')
x = inception_module(x,
filters_1x1=128,
filters_3x3_reduce=128,
filters_3x3=256,
filters_5x5_reduce=24,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4c')
x = inception_module(x,
filters_1x1=112,
filters_3x3_reduce=144,
filters_3x3=288,
filters_5x5_reduce=32,
filters_5x5=64,
filters_pool_proj=64,
name='inception_4d')
x2 = AveragePooling2D((5, 5), strides=3)(x)
x2 = Conv2D(128, (1, 1), padding='same', activation='relu')(x2)
x2 = Flatten()(x2)
x2 = Dense(1024, activation='relu')(x2)
x2 = Dropout(0.7)(x2)
x2 = Dense(1, activation='softmax', name='auxilliary_output_2')(x2)
x = inception_module(x,
filters_1x1=256,
filters_3x3_reduce=160,
filters_3x3=320,
filters_5x5_reduce=32,
filters_5x5=128,
filters_pool_proj=128,
name='inception_4e')
x = MaxPool2D((3, 3), padding='same', strides=(2, 2), name='max_pool_4_3x3/2')(x)
x = inception_module(x,
filters_1x1=256,
filters_3x3_reduce=160,
filters_3x3=320,
filters_5x5_reduce=32,
filters_5x5=128,
filters_pool_proj=128,
name='inception_5a')
x = inception_module(x,
filters_1x1=384,
filters_3x3_reduce=192,
filters_3x3=384,
filters_5x5_reduce=48,
filters_5x5=128,
filters_pool_proj=128,
name='inception_5b')
x = GlobalAveragePooling2D(name='avg_pool_5_3x3/1')(x)
x = Dropout(0.4)(x)
x = Dense(1, activation='softmax', name='output')(x)
model = Model(input_layer, [x, x1, x2], name='inception_v1')
model.summary()
epochs = 25
initial_lrate = 0.01
def decay(epoch, steps=100):
initial_lrate = 0.01
drop = 0.96
epochs_drop = 8
lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))
return lrate
sgd = SGD(lr=initial_lrate, momentum=0.9, nesterov=False)
lr_sc = LearningRateScheduler(decay, verbose=1)
model.compile(loss=['sparse_categorical_crossentropy', 'sparse_categorical_crossentropy', 'sparse_categorical_crossentropy'], loss_weights=[1, 0.3, 0.3], optimizer=sgd, metrics=['accuracy'])
model.fit(T,[y,y,y], validation_split=0.2, epochs=epochs, batch_size=64, callbacks=[lr_sc])
model.save('AAAAAAAAA.model')```
this is the error i am getting:
"UnboundLocalError: local variable 'batch_index' referenced before assignment"