What is the input dimension for a LSTM in Keras?

I'm trying to use deeplearning with LSTM in keras . I use a number of signal as input (nb_sig) that may vary during the training with a fixed number of samples (nb_sample) I would like to make parameter identification, so my output layer is the size of my parameter number (nb_param)

so I created my training set of size (nb_sig x nb_sample) and the label (nb_param x nb_sample)

my issue is I cannot find the correct dimension for the deep learning model. I tried this :

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM

nb_sample = 500
nb_sig = 100 # number that may change during the training
nb_param = 10


train = np.random.rand(nb_sig,nb_sample)
label = np.random.rand(nb_sig,nb_param)


print(train.shape,label.shape)
DLmodel = Sequential()
DLmodel.add(LSTM(units=nb_sample, return_sequences=True, input_shape =(None,nb_sample), activation='tanh'))
DLmodel.add(Dense(nb_param, activation="linear", kernel_initializer="uniform"))

DLmodel.compile(loss='mean_squared_error', optimizer='RMSprop', metrics=['accuracy', 'mse'], run_eagerly=True)
print(DLmodel.summary())

DLmodel.fit(train, label, epochs=10, batch_size=nb_sig)

but I get this error message:

Traceback (most recent call last):
  File "C:\Users\maxime\Desktop\SESAME\PycharmProjects\LargeScale_2022_09_07\di3.py", line 22, in <module>
    DLmodel.fit(train, label, epochs=10, batch_size=nb_sig)
  File "C:\Python310\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "C:\Python310\lib\site-packages\keras\engine\input_spec.py", line 232, in assert_input_compatibility
    raise ValueError(
ValueError: Exception encountered when calling layer "sequential" "                 f"(type Sequential).

Input 0 of layer "lstm" is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (100, 500)

Call arguments received by layer "sequential" "                 f"(type Sequential):
  • inputs=tf.Tensor(shape=(100, 500), dtype=float32)
  • training=True
  • mask=None

I don't understand what I'm suppose to put as input_shape for the LSTM layer and as the number of signals I use during the training will changed, this is not so clear to me.

Solution

The input to LSTM has to be in the following format:

[sample, timestep, n_features]

or in your notation

[nb_sig x nb_sample x n_features]

Therefore you need to reshape the training data to that format. Instead you have:

so I created my training set of size (nb_sig x nb_sample)

As you can see that is 2D input not 3D. You are missing the 3D dimension which in your case seems to be number of features. Simply add 1 extra dimension to the vector using numpy.expand_dims() https://numpy.org/doc/stable/reference/generated/numpy.expand_dims.html and you should be good to go :) - assuming you have univariate time-series.