我要求tensorflow在每个时期每100次迭代保存模型,以下是我的代码。但是在900次迭代之后,只保存了500次、600次、700次、800次、900次迭代的训练模型。
with tf.Session(config = tf.ConfigProto(log_device_placement = True)) as sess:
sess.run(init_op)
for i in range(args.num_epochs):
start_time = time.time()
k = 0
acc_train = 0
假设我循环使用以下代码,直到获得满意的准确性为止:
from sklearn.model_selection import train_test_split
x, y = # ... read in some data set ...
c = 3000 # iterate over some arbitrary range
for i in range(c):
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=i)
model = # .
with torch.no_grad():
for data in test_loader:
images,labels = data
images, labels = images.to(device), labels.to(device)
outputs, features = net(images)
_ , predicted = torch.max(outputs,1)
total += labels.size(0)
correct += (predicted==labels).su
用11200幅图像数据集对神经网络进行了训练,验证准确率为96%。我保存了我的模型,并将它的权重加载到同一个神经网络中。我在一个数组中选择了我的数据集的738幅图像,并试图用我的模型来预测它们的类别,并将它们与真实的标签进行比较,然后再一次计算出准确率,它是74%。这里有什么问题?我想它的准确度应该在96%左右。
prelist=[]
for i in range(len(x)):
prediction = model.predict_classes(x[i])
prelist.append(prediction)
count = 0
for i in range(len(x)
import numpy as np
from random import randint
from sklearn.preprocessing import MinMaxScaler
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Activation
from keras.layers.core import Dense
from keras.optimizers import Adam
from keras.metrics im
我正在使用pytorch RNN模型训练一个模型,并且有多个csv文件可供训练和推断。如果我训练文件#1并对文件#1进行推断,我会得到大约100%的准确预测。如果我在文件#1上进行训练,并根据文件#4或文件#2进行推断,那么准确率会下降到~80%。下面是我正在做的事情: 1. Read the file and separate the features (X) and labels (y) into two dataframes.
2. The range of my values, both features and labels, is high. So I apply scaling
有没有人用CIFAR-10从头开始训练移动网络V1?你的最大准确度是多少?在经历了110个时代之后,我被困在了70%的地方。下面是我创建模型的方法。然而,我的训练准确率在99%以上。
#create mobilenet layer
MobileNet_model = tf.keras.applications.MobileNet(include_top=False, weights=None)
# Must define the input shape in the first layer of the neural network
x = Input(shape=(32,32,3),n