lstm

问题 I have time series of P processes, each of varying length but all having 5 variables (dimensions). I am trying to predict the estimated lifetime of a test process. I am approaching this problem with a stateful LSTM in Keras. But I am not sure if my training process is correct. I divide each sequence into batches of length 30 . So each sequence is of the shape (s_i, 30, 5) , where s_i is different for each of the P sequences ( s_i = len(P_i)//30 ). I append all sequences into my training data

问题 I am trying to create a generative LSTM network in Tensorflow. I have input vectors like this: [[0 0 1 0 ... 1 0] [0 0 1 0 ... 1 0] ... [0 0 0 1 ... 0 1]] Each vector in this matrix is be one time step, or in other words, each vector should be one input to LSTM. Outputs would be the same, except they would be shifted by one time step to right (I am trying to predict next output). Then I have list of these matrices, say five of them - that is one batch. And finally I have list of those batches

问题 Im trying to understand how to use LSTM to classify a certain dataset that i have. I researched and found this example of keras and imdb : https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py However, im confused about how the data set must be processed to input. I know keras has pre-processing text methods, but im not sure which to use. The x contain n lines with texts and the y classify the text by happiness/sadness. Basically, 1.0 means 100% happy and 0.0 means totally sad.

问题 I am trying to build a small LSTM that can learn to write code (even if it's garbage code) by training it on existing Python code. I have concatenated a few thousand lines of code together in one file across several hundred files, with each file ending in <eos> to signify "end of sequence". As an example, my training file looks like: setup(name='Keras', ... ], packages=find_packages()) <eos> import pyux ... with open('api.json', 'w') as f: json.dump(sign, f) <eos> I am creating tokens from

问题 I have tried to build LSTM model with this sample of dataset (patient number, time in mill/sec., normalization of X Y and Z, kurtosis, skewness, pitch, roll and yaw, label) respectively. 1,15,-0.248010047716,0.00378335508419,-0.0152548459993,-86.3738760481,0.872322164158,-3.51314800063,0 1,31,-0.248010047716,0.00378335508419,-0.0152548459993,-86.3738760481,0.872322164158,-3.51314800063,0 1,46,-0.267422664673,0.0051143782875,-0.0191247001961,-85.7662354031,1.0928406847,-4.08015176908,0 1,62,-0

问题 I have trained the imdb_lstm.py on my PC. Now I want to test the trained network by inputting some text of my own. How do I do it? Thank you! 回答1: So what you basically need to do is as follows: Tokenize sequnces: convert the string into words (features): For example: "hello my name is georgio" to ["hello", "my", "name", "is", "georgio"]. Next, you want to remove stop words (check Google for what stop words are). This stage is optional, it may lead to faulty results but I think it worth a try

问题 I have sequences of long 1_D vectors (3000 digits) that I am trying to classify. I have previously implemented a simple CNN to classify them with relative success: def create_shallow_model(shape,repeat_length,stride): model = Sequential() model.add(Conv1D(75,repeat_length,strides=stride,padding='same', input_shape=shape, activation='relu')) model.add(MaxPooling1D(repeat_length)) model.add(Flatten()) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer=

问题 I have data that has various conversations between two people. Each sentence has some type of classification. I am attempting to use an NLP net to classify each sentence of the conversation. I tried a convolution net and get decent results (not ground breaking tho). I figured that since this a back and forth conversation, and LSTM net may produce better results, because what was previously said may have a large impact on what follows. If I follow the structure above, I would assume that I am

问题 I want to train a bi-directional LSTM in tensorflow to perform a sequence classification problem (sentiment classification). Because sequences are of variable lengths, batches are normally padded with vectors of zero. Normally, I use the sequence_length parameter in the uni-directional RNN to avoid training on the padding vectors. How can this be managed with bi-directional LSTM. Does the "sequence_length" parameter work automatically starts from an advanced position in the sequence for the