sequential

1 import torch 2 import torch.nn.functional as F 3 4 5 # replace following class code with an easy sequential network 6 class Net(torch.nn.Module): 7 def __init__(self, n_feature, n_hidden, n_output): 8 super(Net, self).__init__() 9 self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer 10 self.predict = torch.nn.Linear(n_hidden, n_output) # output layer 11 12 def forward(self, x): 13 x = F.relu(self.hidden(x)) # activation function for hidden layer 14 x = self.predict(x) # linear output 15 return x 16 17 net1 = Net(1, 10, 1) 18 19 # easy and fast way to build your network 20 net2 =

I'm using PostgreSQL 9.4. First, I have postgreSQL installed on a system with the only one ssd -drive. I'm trying to understand what sequential read is and end up with some issue. For instance, if we ask for an SQL-Server to give us some unindexed data, the seq-scan is likely to be happen. But What if two different clients ask for data from two different tables simultaneously? In this case, sql-server creates two different processes for each client and executes the queries concurrently. But if the queries are being executed concurrently, the head of the drive need to jump from the area the

基本卷积网络结构net.py from torch import nn class SimpleCNN(nn.Module): def __init__(self): super(SimpleCNN, self).__init__() layer1 = nn.Sequential() # 将网络模型进行添加 layer1.add_module('conv1', nn.Conv2d(3, 32, 3, 1, padding=1)) # nn.Conv layer1.add_module('relu1', nn.ReLU(True)) layer1.add_module('pool1', nn.MaxPool2d(2, 2)) self.layer1 = layer1 layer2 = nn.Sequential() layer2.add_module('conv2', nn.Conv2d(32, 64, 3, 1, padding=1)) layer2.add_module('relu2', nn.ReLU(True)) layer2.add_module('pool2', nn.MaxPool2d(2, 2)) self.layer2 = layer2 layer3 = nn.Sequential() layer3.add_module('conv3', nn.Conv2d(64,

基本的卷积神经网络 from torch import nn class SimpleCNN(nn.Module): def __init__(self): super(SimpleCNN, self).__init__() layer1 = nn.Sequential() # 将网络模型进行添加 layer1.add_module('conv1', nn.Conv2d(3, 32, 3, 1, padding=1)) # nn.Conv layer1.add_module('relu1', nn.ReLU(True)) layer1.add_module('pool1', nn.MaxPool2d(2, 2)) self.layer1 = layer1 layer2 = nn.Sequential() layer2.add_module('conv2', nn.Conv2d(32, 64, 3, 1, padding=1)) layer2.add_module('relu2', nn.ReLU(True)) layer2.add_module('pool2', nn.MaxPool2d(2, 2)) self.layer2 = layer2 layer3 = nn.Sequential() layer3.add_module('conv3', nn.Conv2d(64, 128,

I want to create and train a CNN model in Keras for classification of banknotes. Creating models works fine with simple tutorials but not with the architecture I adopt from this paper . Keras outputs: RuntimeError('You must compile your model before using it.') after fit_generator() is called. I use the tensorflow backend if that is of relevance. Model is defined in model.py : from keras.layers import ... model = Sequential() model.add(some_layer) ... #according to the paper model.add(some_layer) model.add(Dense(#output_classes, activation='softmax') #last layer model.compile(loss='categorical

I need a sequential number sequence for the rows in a table and I need to ensure that it is always sequential with no gaps on insert , when deleted I can leave the row gap, but on insert I must fill the gaps with the new rows. The reason for this is a different system must line up one for one with the row records. Yet the db can be manipulated by others in both the sql end and also via an application ; I am thinking a trigger will allow me to accomplish the something changed part - but how to actually determine if I have gaps and perform the insert of this sequence number - even if I have to

I'm trying to play a few WAV files after each other. I tried this method: for (String file : audioFiles) { new AePlayWave(file).start(); } But that plays them all at the same time. So I need a function that looks like this: public void play(Vector<String> audioFiles); The vector contains the files, for example: "test1.wav" , "test2.wav" I have been looking for over four hours, but I can't seem to find a working solution :( I also tried concatenating the WAV files to one AudioInputStream. It doesn't give any compiler errors, but the sound is totally messed up. Code: public static