def _test_outputs_gradients(model):
x, y, _ = make_data(K.int_shape(model.input), model.layers[2].units)
name = model.layers[1].name
grads_all = get_gradients(model, name, x, y, mode='outputs')
grads_last = get_gradients(model, 2, x, y, mode='outputs')
kwargs1 = dict(n_rows=None,
show_xy_ticks=[0, 0],
show_borders=True,
max_timesteps=50,
title_mode='grads')
kwargs2 = dict(n_rows=2,
show_xy_ticks=[1, 1],
show_borders=False,
max_timesteps=None)
features_1D(grads_all[0], **kwargs1)
features_1D(grads_all[:1], **kwargs1)
features_1D(grads_all, **kwargs2)
features_2D(grads_all[0], norm=(-.01, .01), show_colorbar=True, **kwargs1)
features_2D(grads_all, norm=None, reflect_half=True, **kwargs2)
features_0D(grads_last, marker='o', color=None, title_mode='grads')
features_0D(grads_last, marker='x', color='blue', ylims=(-.1, .1))
features_hist(grads_all, bins=100, xlims=(-.01, .01), title="Outs hists")
features_hist(grads_all, bins=100, n_rows=4)
print('\n') # improve separation
def _test_weights_gradients(model):
x, y, _ = make_data(K.int_shape(model.input), model.layers[2].units)
name = model.layers[1].name
with tempdir() as dirpath:
kws = dict(input_data=x, labels=y, mode='grads')
if hasattr(model.layers[1], 'backward_layer'):
kws['savepath'] = dirpath
rnn_histogram(model, name, bins=100, **kws)
rnn_heatmap(model, name, **kws)
def _test_outputs(model):
x, *_ = make_data(K.int_shape(model.input), model.layers[2].units)
outs = get_outputs(model, 1, x)
features_1D(outs[:1], show_y_zero=True)
features_1D(outs[0])
features_2D(outs)