Tensors

What is a Tensor?

Tensors in Keras

Internally, Keras represents the weights of a neural network with tensors. Tensors are basically TensorFlow’s version of a Numpy array with a few differences that make them better suited to deep learning. One of the most important is that tensors are compatible with GPU and TPU) accelerators. TPUs, in fact, are designed specifically for tensor computations.

A model’s weights are kept in its weights attribute as a list of tensors. Get the weights of the model you defined above. (If you want, you could display the weights with something like: print("Weights\n{}\n\nBias\n{}".format(w, b))).

w, b = model.weights

Do you see how there’s one weight for each input (and a bias)? Notice though that there doesn’t seem to be any pattern to the values the weights have. Before the model is trained, the weights are set to random numbers (and the bias to 0.0). A neural network learns by finding better values for its weights.

(By the way, Keras represents weights as tensors, but also uses tensors to represent data. When you set the input_shape argument, you are telling Keras the dimensions of the array it should expect for each example in the training data. Setting input_shape=[3] would create a network accepting vectors of length 3, like [0.2, 0.4, 0.6].)