What exactly are `steps` in the Convolution1D layer?

Let’s say I wanted to train a 1D Convnet. I use 100K examples for training, where each training example is 1000 samples from a 2 channel audio file. I assume for this example that nb_samples=1000 and input_dim=2.

What would steps represent in this case? My input tensor to model.fit is size (n_examples, nb_samples, input_dim)

[fchollet]

Convolution1D is usually applied to temporal data, like yours (assuming that you “samples” are in the temporal space, not the frequency space). In Keras, temporal data is understood as a tensor of shape (nb_samples, steps, input_dim). The meaning of “sample” here is different from the one your are using, it simply means example / data point.

nb_samples = examples, steps = time dimension, input_dim = features at each time step.

[pjadzinsky]

Not sure I understand your question. The docs says “Input shape: 3D tensor with shape: (nb_samples, steps, input_dim).” Isn’t steps what you are calling nb_samples? why do you rename it to something else?