Python 输出标签粘贴到vgg16中的相同值
我试图在svhn数据集上运行用于数字检测的VGG16模型 然而,预测值总是相同的。 我试着给图像加上-Python 输出标签粘贴到vgg16中的相同值,python,tensorflow,keras,conv-neural-network,imagenet,Python,Tensorflow,Keras,Conv Neural Network,Imagenet,我试图在svhn数据集上运行用于数字检测的VGG16模型 然而,预测值总是相同的。 我试着给图像加上- 从0-255缩放到0-1 从每个图像中减去平均值 除以标准差 下面是我运行它的方式: 初始化VGG16: vgg = tf.keras._impl.keras.applications.vgg16.VGG16 (include_top=True, weights=None, input_tensor=None, input_shape=(64,64,3),
vgg = tf.keras._impl.keras.applications.vgg16.VGG16
(include_top=True,
weights=None,
input_tensor=None,
input_shape=(64,64,3),
pooling='max',
classes=10)
vgg.compile(loss=tf.keras.losses.categorical_crossentropy,
optimizer=tf.keras.optimizers.SGD(lr=1e-4, momentum=0.9, nesterov=True),metrics=['mae', 'acc'])
#Here train_data shape is (None,64,64,3) and labels_data shape is (None,10) and are one hot like labels
vgg.fit(train_data, labels_data, epochs=5, batch_size=96)
列车数据的读取和预处理如下:
train_data = sio.loadmat('datasets/housing/train_32x32.mat')
以下是我用来预处理列车运行数据的两个函数:
import numpy as np
import cv2
import tensorflow as tf
import os
import scipy
from skimage import data, io, filters
import scipy.io as sio
from utils import *
import h5py
from tensorflow.python.keras._impl.keras import backend as K
from tensorflow.python.keras._impl.keras.applications.imagenet_utils import _obtain_input_shape
from tensorflow.python.keras._impl.keras.applications.imagenet_utils import decode_predictions # pylint: disable=unused-import
from tensorflow.python.keras._impl.keras.applications.imagenet_utils import preprocess_input # pylint: disable=unused-import
from tensorflow.python.keras._impl.keras.engine.topology import get_source_inputs
from tensorflow.python.keras._impl.keras.layers import Conv2D
from tensorflow.python.keras._impl.keras.layers import Dense
from tensorflow.python.keras._impl.keras.layers import Flatten
from tensorflow.python.keras._impl.keras.layers import GlobalAveragePooling2D
from tensorflow.python.keras._impl.keras.layers import GlobalMaxPooling2D
from tensorflow.python.keras._impl.keras.layers import Input
from tensorflow.python.keras._impl.keras.layers import MaxPooling2D
from tensorflow.python.keras._impl.keras.models import Model
from tensorflow.python.keras._impl.keras.utils import layer_utils
from tensorflow.python.keras._impl.keras.utils.data_utils import get_file
from tensorflow.python.keras._impl.keras.engine import Layer
def reshape_mat_vgg (QUANTITY,matfilepath='datasets/housing/train_32x32.mat', type="non-quantized", size=(64,64)):
data = read_mat_file (matfilepath)
train = data['X'][:,:,:,0:QUANTITY]
train = np.swapaxes(np.swapaxes(np.swapaxes(train,2,3),1,2),0,1)
labels = data['y'][0:QUANTITY]
labels_data = []; labels_data = np.array(labels_data)
train_data = np.zeros((QUANTITY,size[0],size[1],3))
print "Reorganizing Data..."
for i in range(QUANTITY):
image_i = np.copy(train[i,:,:,:])
image_i = preprocess_small_vgg16 (image_i, new_size=size, type=type)
train_data[i,:,:,:] = image_i
label_i = np.zeros((10)); label_i[labels[i]-1] = 1.0; label_i = label_i.reshape(1,10)
if i == 0:
labels_data = np.vstack(( label_i ))
else:
labels_data = np.vstack(( labels_data, label_i ))
if i % 1000 == 0:
print i*100/(QUANTITY-1),"percent done..."
print "100 percent done..."
return train_data, labels_data
def preprocess_small_vgg16 (image, new_size=(64,64), type="non-quantized"):
img = np.copy (image)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
#Whitening
imgnorm = img * 255.0/np.max(img)
#Normalisation
gray = imgnorm
borderless = gray[:,5:gray.shape[1]-5,:] # centering
final_img = (cv2.resize (borderless, new_size))
final_img = final_img/np.max(final_img) #scaling 0-1
stddev = np.std(final_img); mn = np.mean (final_img)
final_img = (final_img - mn) / stddev #standardizing
return final_img
输出:
Epoch 1/10
5000/5000 [==============================] - 1346s - loss: 3.2877 -
mean_absolute_error: 0.0029 - acc: 0.1850
Epoch 2/10
在多个时代运行没有帮助。我试了5个时代
当我检查输出或预测时,它显示所有输入的相同结果,例如(使用np.argmax(pred,axis=-1)转换):
请在我的模型上标出这个问题 我认为您的大部分问题是,5个时代远远不够VGG权重收敛。在VGG原创论文中,作者说他们训练了370K迭代(74个时代)以获得最好的结果,所以你可以考虑给它更多的时间来收敛。 我已经将您的数据集输入到,您可以看到经过数千次迭代后,总损失是如何减少的
当然,您可以微调学习速率衰减,以防止在高原上花费时间 哈哈哇,要多少天才能完成?即使是我的笔记本电脑电池也不能支持那么多…@user3206070这是几天的时间,但我认为如果你手动衰减学习速度,你可能会在4~5小时内达到相同的结果。你使用sgd optimizer,训练集大小是多少?@user3206070训练集大小是50k,批量大小是256。嘿,出于好奇,您知道tf用于卷积的内核内容是什么吗?
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