fe5db3121c4313775181dea4935c764b84b8d357,keract/keract.py,,get_gradients_of_activations,#,23

Before Change

     Gets gradient a layer output for given inputs and outputs
    // grads = model.optimizer.get_gradients(model.total_loss, model.layers[layer].output)
    layer_names = [l.output.name for l in model.layers]
    grads = model.optimizer.get_gradients(model.total_loss, [l.output for l in model.layers])
    output_grad = _evaluate(model, grads, model_inputs, outputs)
    result = dict(zip(layer_names, output_grad))
    return result


def get_activations(model, model_inputs, layer_name=None):
    outputs = [layer.output for layer in model.layers if layer.name == layer_name or layer_name is None]

After Change

def get_gradients_of_activations(model, x, y, layer_name=None):
    nodes = [layer.output for layer in model.layers if layer.name == layer_name or layer_name is None]
    nodes_names = [n.name for n in nodes]
    return _get_gradients(model, x, y, nodes, nodes_names)


def _get_gradients(model, x, y, nodes, nodes_names):
    if model.optimizer is None:

Instances