Category "lstm"

How to predict the stock price for the next 30 days after the LSTM model has predicted the test_set?

I've used a data-set containing closing price of a particular stock for 5 years.It has closing prices for 1231 days. The train_set consists of 987 days and the

Trying to copy an LSTM code, giving me an error "x and y must have same first dimension, but have shapes (1103,) and (275,)"

I'm trying to copy a LSTM model that I found from here: Stock Market-Predict volume with LSTM model I'm getting stuck on the last line of code. Specifically, th

ValueError: Input 0 of layer "lstm" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 1024)

I was following Transfer learning with YAMNet for environmental sound classification tutorial. Here is the link: https://www.tensorflow.org/tutorials/audio/tran

Missing required positional argument:

I tried to implement federated learning based on the LSTM approach. def create_keras_model(): model = Sequential() model.add(LSTM(32, input_shape=(3,1))

When predicting, shall we scale unseen inputs, and un-scale outputs of a model?

I am new to Machine Learning, and I followed this tutorial to implement LSTM model in Keras/Tensorflow: https://www.tensorflow.org/tutorials/structured_data/tim

How to train LSTM model with variable-length sequence input

I'm trying to train LSTM model in Keras using data of variable timestep, for example, the data looks like: <tf.RaggedTensor [[[0.0, 0.0, 0.0, 0.0, 0.0, 1.0,

Deep learning result graph is is limited to the average area

] You can check the Network Model and Result from the Photos. Result datas are stuck in the "average band" and can't forecasting the exact value. I used a 3ye

How to fix LSTM Layer Error - Image Classification

Currently I'm working on an image classification issue and created the following code based on a tutorial online - Image Classification using Keras. The code w

Keras LSTM fit underfitting

I have time series training data of about 5000 numbers. For each 100 numbers, I am trying to predict the 101st. At the end of the series, I would put in the pre

Keras: the difference between LSTM dropout and LSTM recurrent dropout

From the Keras documentation: dropout: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. recurrent_dropout: F

PyTorch Model Training: RuntimeError: cuDNN error: CUDNN_STATUS_INTERNAL_ERROR

After training a PyTorch model on a GPU for several hours, the program fails with the error RuntimeError: cuDNN error: CUDNN_STATUS_INTERNAL_ERROR Trainin

LSTM is Showing very low accuracy and large loss

I am applying LSTM on a dataset that has 53699 entries for the training set and 23014 entries for the test set. The shape of the input training set is (53699,4)

Trying to copy a LSTM model - it's not working

I'm trying to copy a LSTM model that I found from here: Stock Market-Predict volume with LSTM model I'm getting stuck on the last line of code. Specifically, th

Tensorflow LSTM/GRU reset states once per epoch and not for each new batch

I train the following model based on GRU, note that I am passing the argument stateful=True to the GRU builder. class LearningToSurpriseModel(tf.keras.Model):

How to properly initialize TensorFlow GRU-layer with noisy states?

I wish to experiement with noisy GRU states instead of resetting them to zero for each batch. I try below an implementation. My initial code was resetting initi

Tensorflow - ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type float)

Continuation from previous question: Tensorflow - TypeError: 'int' object is not iterable My training data is a list of lists each comprised of 1000 floats. For

Specifying the batch size when subclassing keras.model

I implement a model including a LSTM layer by subclassing the keras.Model. The following is the code I used. import tensorflow as tf from tensorflow import ker

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