我已经使用CNN和CIFAR 10数据集训练了用于图像分类的深度学习模型。它有10个课程，持续了50个纪元。训练完成后显示的准确性为96.02％，验证准确性为96.96％。但是，当我将图像传递给它进行检测和测试时，即使在对象明显的图片中也显示出错误的答案。例如：它将青蛙检测为汽车或飞机，将鹿检测为飞机或马，将鸟类检测为猫或狗。
我已经尝试用更多的神经元训练它。然后我想也许是因为我正在灰度图像上训练模型并传递彩色图像，然后在彩色图像上对其进行训练，结果也一样。
import os
from datetime import time
from random import shuffle
#import TensorBoard as TensorBoard
#import TensorBoard as TensorBoard
import cv2
import numpy as np
import os
from random import shuffle
from tqdm import tqdm
import tensorflow as tf
import matplotlib.pyplot as plt
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
TRAIN_DIR = '../input/dataset/Train'
TEST_DIR = '../input/dataset/Test'
IMG_SIZE = 32
LR = 1e-3 # 1x10^-3 or 0.003
MODEL_NAME = 'image classifier'
def create_label(image_name):
""" Create an one-hot encoded vector from image name """
word_label = image_name.split('_')[1:2]
word_label = word_label[0].split('.')[0:1]
word_label = word_label[0]
if word_label == 'cat':
return np.array([1,0,0,0,0,0,0,0,0,0])
elif word_label == 'dog':
return np.array([0,1,0,0,0,0,0,0,0,0])
elif word_label == 'automobile':
return np.array([0,0,1,0,0,0,0,0,0,0])
elif word_label == 'airplane':
return np.array([0,0,0,1,0,0,0,0,0,0])
elif word_label == 'ship':
return np.array([0,0,0,0,1,0,0,0,0,0])
elif word_label == 'frog':
return np.array([0,0,0,0,0,1,0,0,0,0])
elif word_label == 'truck':
return np.array([0,0,0,0,0,0,1,0,0,0])
elif word_label == 'bird':
return np.array([0,0,0,0,0,0,0,1,0,0])
elif word_label == 'horse':
return np.array([0,0,0,0,0,0,0,0,1,0])
elif word_label == 'deer':
return np.array([0,0,0,0,0,0,0,0,0,1])
def create_train_data():
training_data = []
for img in tqdm(os.listdir(TRAIN_DIR)):
path = os.path.join(TRAIN_DIR, img)
img_data = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
img_data = cv2.resize(img_data, (IMG_SIZE, IMG_SIZE))
training_data.append([np.array(img_data), create_label(img)])
shuffle(training_data)
np.save('train_data.npy', training_data)
return training_data
def create_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_data = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
img_data = cv2.resize(img_data, (IMG_SIZE, IMG_SIZE))
testing_data.append([np.array(img_data), img_num])
shuffle(testing_data)
np.save('test_data.npy', testing_data)
return testing_data
# If dataset is not created:
train_data = create_train_data()
test_data = create_test_data()
# If you have already created the dataset:
#train_data = np.load('train_data.npy')
#test_data = np.load('test_data.npy')
train = train_data[:50000]
test = train_data[:10000]
X_train = np.array([i[0] for i in train]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
y_train = [i[1] for i in train]
X_test = np.array([i[0] for i in test]).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
y_test = [i[1] for i in test]
# Building The Model
tf.reset_default_graph()
convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 1], name='input')
convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 128, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 256, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 128, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = conv_2d(convnet, 64, 3, activation='relu')
convnet = max_pool_2d(convnet, 3)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 10, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=LR, loss='categorical_crossentropy', name='targets')
model = tflearn.DNN(convnet, tensorboard_dir='log', tensorboard_verbose=2)
history = model.fit({'input': X_train}, {'targets': y_train}, n_epoch=50,
validation_set=({'input': X_test}, {'targets': y_test}),
snapshot_step=1250, show_metric=True, run_id=MODEL_NAME)
fig = plt.figure(figsize=(25, 12))
for num, data in enumerate(test_data[:50]):
img_num = data[1]
img_data = data[0]
y = fig.add_subplot(10, 10, 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) == 0:
str_label = 'Cat'
if np.argmax(model_out) == 1:
str_label = 'Dog'
if np.argmax(model_out) == 2:
str_label = 'Automobile'
if np.argmax(model_out) == 3:
str_label = 'Airplane'
if np.argmax(model_out) == 4:
str_label = 'Ship'
if np.argmax(model_out) == 5:
str_label = 'frog'
if np.argmax(model_out) == 6:
str_label = 'truck'
if np.argmax(model_out) == 7:
str_label = 'bird'
if np.argmax(model_out) == 8:
str_label = 'horse'
if np.argmax(model_out) == 9:
str_label = 'deer'
#if condition ends here
y.imshow(orig, cmap='gray')
plt.title(str_label)
y.axes.get_xaxis().set_visible(False)
y.axes.get_yaxis().set_visible(False) #for loop ends here
plt.show()
"""saving the model using tflearn"""
model.save('CNN.tfl')
# I am using the following code for testing purpose
global a
img_data1 = cv2.imread(a, cv2.IMREAD_COLOR)
img_data1 = cv2.resize(img_data1, (IMG_SIZE, IMG_SIZE))
data1 = img_data1.reshape(IMG_SIZE, IMG_SIZE, 3)
model_out = model.predict([data1])[0]
if np.argmax(model_out) == 0:
str_label = 'Cat'
if np.argmax(model_out) == 1:
str_label = 'Dog'
if np.argmax(model_out) == 2:
str_label = 'Automobile'
if np.argmax(model_out) == 3:
str_label = 'Airplane'
if np.argmax(model_out) == 4:
str_label = 'Ship'
if np.argmax(model_out) == 5:
str_label = 'frog'
if np.argmax(model_out) == 6:
str_label = 'truck'
if np.argmax(model_out) == 7:
str_label = 'bird'
if np.argmax(model_out) == 8:
str_label = 'horse'
if np.argmax(model_out) == 9:
str_label = 'deer'
“ a”是传递给它的图像，代码是从TK-Inter上的GUI中选取的，而a获取图像的路径并将其传递给模型。
我很生气地看到训练结束时显示的准确性如此之高，即使在可见的图像上，我在测试时通过模型得到的预测仍然如此错误。
如果有人可以帮助我解决这个问题，我将不胜感激。
谢谢!!