From 7f450694b085014d394213ecc0ccf7aad33c3cc3 Mon Sep 17 00:00:00 2001
From: Nikolai <osterei33@gmx.de>
Date: Tue, 28 Aug 2018 17:25:17 +0200
Subject: [PATCH] starting to implement function for plotting actmax events for
all neurons
---
plotting.py | 91 +++++++++++++++++++++++++++++++++++++++++++++++++++++
1 file changed, 91 insertions(+)
diff --git a/plotting.py b/plotting.py
index cdcde3f..10d61e1 100644
--- a/plotting.py
+++ b/plotting.py
@@ -234,6 +234,97 @@ def plot_NN_vs_var_2D_all(plotname, model, means,
save_show(plt, fig, plotname, bbox_inches='tight')
+def plot_profile_2D_all(plotname, model,
+ events,
+ nbinsx, xmin, xmax,
+ nbinsy, ymin, ymax,
+ transform_function=None,
+ varx_label=None,
+ vary_label=None,
+ zrange=None, logz=False,
+ plot_last_layer=False,
+ log_default_ymin=1e-5,
+ cmap="inferno", **kwargs):
+
+ "Similar to plot_NN_2D, but creates a grid of plots for all neurons."
+
+ # transform
+ if transform_function is not None:
+ events = transform_function(events)
+
+ acts = get_activations(model, events, print_shape_only=True)
+
+ if plot_last_layer:
+ n_neurons = [len(i[0]) for i in acts]
+ else:
+ n_neurons = [len(i[0]) for i in acts[:-1]]
+ layers = len(n_neurons)
+
+ nrows_ncols = (layers, max(n_neurons))
+ fig = plt.figure(1, figsize=nrows_ncols)
+ grid = ImageGrid(fig, 111, nrows_ncols=nrows_ncols[::-1], axes_pad=0,
+ label_mode="1",
+ aspect=False,
+ cbar_location="top",
+ cbar_mode="single",
+ cbar_pad=.2,
+ cbar_size="5%",)
+ grid_array = np.array(grid)
+ grid_array = grid_array.reshape(*nrows_ncols[::-1])
+
+ # leave out the last layer
+ global_min = min([np.min(ar_layer) for ar_layer in acts[:-1]])
+ global_max = max([np.max(ar_layer) for ar_layer in acts[:-1]])
+
+ logger.info("global_min: {}".format(global_min))
+ logger.info("global_max: {}".format(global_max))
+
+ output_min_default = 0
+ output_max_default = 1
+
+ if global_min <= 0 and logz:
+ global_min = log_default_ymin
+ logger.info("Changing global_min to {}".format(log_default_ymin))
+
+ ims = []
+ for layer in range(layers):
+ for neuron in range(len(acts[layer][0])):
+ acts_neuron = acts[layer][:,neuron]
+ ax = grid_array[neuron][layer]
+ extra_opts = {}
+ if not (plot_last_layer and layer == layers-1):
+ # for hidden layers, plot the same z-scale
+ if logz:
+ norm = matplotlib.colors.LogNorm
+ else:
+ norm = matplotlib.colors.Normalize
+ if zrange is not None:
+ extra_opts["norm"] = norm(vmin=zrange[0], vmax=zrange[1])
+ else:
+ extra_opts["norm"] = norm(vmin=global_min, vmax=global_max)
+ #im = ax.pcolormesh(varx_vals, vary_vals, acts_neuron, cmap=cmap, linewidth=0, rasterized=True, **extra_opts)
+ hist, xedges, yedges = get_profile_2D(
+ valsx, valsy, acts_neuron,
+ nbinsx, xmin, xmax,
+ nbinsy, ymin, ymax,
+ **kwargs
+ )
+ X, Y = np.meshgrid(xedges, yedges)
+ im = ax.pcolormesh(X, Y, hist, cmap="inferno", linewidth=0, rasterized=True, **extra_opts)
+ ax.set_facecolor("black")
+ if varx_label is not None:
+ ax.set_xlabel(varx_label)
+ if vary_label is not None:
+ ax.set_ylabel(vary_label)
+ ax.text(0., 0.5, "{}, {}".format(layer, neuron), transform=ax.transAxes, color="white")
+
+ cb = fig.colorbar(im, cax=grid[0].cax, orientation="horizontal")
+ cb.ax.xaxis.set_ticks_position('top')
+ cb.ax.xaxis.set_label_position('top')
+
+ save_show(plt, fig, plotname, bbox_inches='tight')
+
+
def plot_hist_2D(plotname, xedges, yedges, hist, varx_label=None, vary_label=None, log=False, zlabel="# of events"):
X, Y = np.meshgrid(xedges, yedges)
--
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