artificial-intelligence

问题 self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1) self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.lstm = nn.LSTMCell(32 * 3 * 3, 256) Can somebody provide the formula of the calculation lstm input (32 * 3 * 3). Where is this 3 comes from. 来源： https://stackoverflow.com/questions/62724776/torch-lstmcell-method

问题 self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1) self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.lstm = nn.LSTMCell(32 * 3 * 3, 256) Can somebody provide the formula of the calculation lstm input (32 * 3 * 3). Where is this 3 comes from. 来源： https://stackoverflow.com/questions/62724776/torch-lstmcell-method

问题 self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1) self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1) self.lstm = nn.LSTMCell(32 * 3 * 3, 256) Can somebody provide the formula of the calculation lstm input (32 * 3 * 3). Where is this 3 comes from. 来源： https://stackoverflow.com/questions/62724776/torch-lstmcell-method

问题 I have several questions about labeling for clustermap in seaborn . First is it possible to extract the the distance values for the hierarchical clustering, and plot the value on the tree structure visualization (maybe only the first three levels). Here is my example code for creating a clustermap plot: import pandas as pd import numpy as np import seaborn as sns get_ipython().magic(u'matplotlib inline') m = np.random.rand(50, 50) df = pd.DataFrame(m, columns=range(4123, 4173), index=range

问题 I need to build a software that does audio recognition from a small audio sample (A) inside other audio samples (B), and output how many times A appears inside the audio from B (if there is a match). What I have: A database with hundreds of audios Input: New audio Expected Output: A boolean if the input matches a sample from the database, and how many times appeared the input inside the matched audio (from the db). Any code, open source project, guides, books, videos, tutorial, etc... is

问题 I need to build a software that does audio recognition from a small audio sample (A) inside other audio samples (B), and output how many times A appears inside the audio from B (if there is a match). What I have: A database with hundreds of audios Input: New audio Expected Output: A boolean if the input matches a sample from the database, and how many times appeared the input inside the matched audio (from the db). Any code, open source project, guides, books, videos, tutorial, etc... is

问题 I do understand that a complete algorithm is one where if there is a solution, the algorithm is able to find it and that optimal algorithm is one where it manages to find a least cost solution. But is an optimal algorithm, a complete algorithm? Can please briefly explain? Thanks. 回答1: Yes, by definition. Finding the optimal solution entails proving optimality. This can be done by finding all solutions or by proving that no solution can have better cost than the one found already. In either

问题 I do understand that a complete algorithm is one where if there is a solution, the algorithm is able to find it and that optimal algorithm is one where it manages to find a least cost solution. But is an optimal algorithm, a complete algorithm? Can please briefly explain? Thanks. 回答1: Yes, by definition. Finding the optimal solution entails proving optimality. This can be done by finding all solutions or by proving that no solution can have better cost than the one found already. In either

问题 Argh! I keep getting the following error when attempting to compute with my neural network: > net.compute <- compute(net, matrix.train2) Error in neurons[[i]] %*% weights[[i]] : non-conformable arguments I can't figure out what the problem is. Below I'll provide you with an example data and formatting from my matrices and then I'll show you the code I'm attempting to run. matrix.train1 is used for training the network > matrix.train1 (Intercept) survived pclass sexmale age sibsp parch fare

问题 I'm having a huge block trying to understand "trees" while making a Tic-Tac-Toe bot. I understand the concept, but I can't figure out to implement them. Can someone show me an example of how a tree should be generated for such a case? Or a good tutorial on generating trees? I guess the hard part is generating partial trees. I know how to implement generating a whole tree, but not parts of it. 回答1: Imagine that at any point in a tic-tac-toe board, every single possible move is a branch. The