lstm

问题 I am trying to fit an LSTM network to a sin function. Currently, as far as I understand Keras, my code does only predict the next value. According to this link: Many to one and many to many LSTM examples in Keras it is a many to one model. However, my goal is to implement a Many-to-many model. Basically, I want to be able to predict let's say 10 values, to a given time. When I am trying to use return_sequences=True (see line model.add(..) ), which is supposed to be the solution, the following

问题 So, I am currently working on a machine learning algorithm problem pertaining to car speeds and angles, and I'm trying to improve upon some of my work. I recently got done with an XGBRegressor that yielded between 88 - 95% accuracy on my cross-validated data. However, I'm trying to improve upon it, so I've been looking into the LSTM algorithm, because my data is time-series dependent. Essentially, every link includes a steering angle, the previous times steering angle (x-1), the time before

问题 Im trying to get a LSTM working in Keras but even after the first epoch, the accuracy seems to be too high (90%) and Im worried is not training properly, I took some ideas from this post: https://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Here's my code: import numpy from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense from keras.layers import LSTM from keras.layers import Dropout from keras

问题 This is respect to CNTK brain scripts. I went through [1] to figure out whether there is an option to specify the random seed value, although I couldn't find any (Yes there is an option to set the 'random seed' parameter through the ParameterTensor() function, but if I followed that approach, I might have to explicitly initialize all the LSTM weights separately(defining separate weights for input layer gate, forget layer gate etc. ), instead of using the model sequence as below). Is there any

问题 I have a simple LSTM model based on Keras. X_train, X_test, Y_train, Y_test = train_test_split(input, labels, test_size=0.2, random_state=i*10) X_train = X_train.reshape(80,112,12) X_test = X_test.reshape(20,112,12) y_train = np.zeros((80,112),dtype='int') y_test = np.zeros((20,112),dtype='int') y_train = np.repeat(Y_train,112, axis=1) y_test = np.repeat(Y_test,112, axis=1) np.random.seed(1) # create the model model = Sequential() batch_size = 20 model.add(BatchNormalization(input_shape=(112

问题 I'm trying to build a very simple LSTM autoencoder with PyTorch. I always train it with the same data: x = torch.Tensor([[0.0], [0.1], [0.2], [0.3], [0.4]]) I have built my model following this link: inputs = Input(shape=(timesteps, input_dim)) encoded = LSTM(latent_dim)(inputs) decoded = RepeatVector(timesteps)(encoded) decoded = LSTM(input_dim, return_sequences=True)(decoded) sequence_autoencoder = Model(inputs, decoded) encoder = Model(inputs, encoded) My code is running with no errors but

问题 I'm new with Keras and I'm trying to implement a Sequence to Sequence LSTM. Particularly, I have a dataset with 9 features and I want to predict 5 continuous values. I split the training and the test set and their shape are respectively: X TRAIN (59010, 9) X TEST (25291, 9) Y TRAIN (59010, 5) Y TEST (25291, 5) The LSTM is extremely simple at the moment: model = Sequential() model.add(LSTM(100, input_shape=(9,), return_sequences=True)) model.compile(loss="mean_absolute_error", optimizer="adam"

问题 Background I would like to do mini-batch training of "stateful" LSTMs in Keras. My input training data is in a large matrix "X" whose dimensions are m x n where m = number-of-subsequences n = number-of-time-steps-per-sequence Each row of X contains a subsequence which picks up where the subsequence on the preceding row leaves off. So given a long sequence of data, Data = ( t01, t02, t03, ... ) where "tK" means the token at position K in the original data, the sequence is layed out in X like

问题 I'm new to tensorflow and I'm trying to update some code for a bidirectional LSTM from an old version of tensorflow to the newest (1.0), but I get this error: Shape must be rank 2 but is rank 3 for 'MatMul_3' (op: 'MatMul') with input shapes: [100,?,400], [400,2]. The error happens on pred_mod. _weights = { # Hidden layer weights => 2*n_hidden because of foward + backward cells 'w_emb' : tf.Variable(0.2 * tf.random_uniform([max_features,FLAGS.embedding_dim], minval=-1.0, maxval=1.0, dtype=tf

问题 I trained a LSTM classifier, using a BasicLSTMCell. How can I save my model and restore it for use in later classifications? 回答1: I was wondering this myself. As other pointed out, the usual way to save a model in TensorFlow is to use tf.train.Saver() , however I believe this saves the values of tf.Variables . I'm not exactly sure if there are tf.Variables inside the BasicLSTMCell implementation which are saved automatically when you do this, or if there is perhaps another step that need to