From de6aa68f23f569a5ccb774c3aa96447d85fdae71 Mon Sep 17 00:00:00 2001 From: Si11ium Date: Tue, 5 Mar 2019 20:31:33 +0100 Subject: [PATCH] Visual Debugging --- code/experiment.py | 2 +- code/latent_trajectory_plot.html | 7 +++ code/methods.py | 1 + code/network.py | 7 ++- code/plot.html | 7 +++ code/visualization.py | 77 ++++++++++++++++---------------- 6 files changed, 59 insertions(+), 42 deletions(-) create mode 100644 code/latent_trajectory_plot.html create mode 100644 code/plot.html diff --git a/code/experiment.py b/code/experiment.py index 1f97d6d..1117603 100644 --- a/code/experiment.py +++ b/code/experiment.py @@ -67,7 +67,7 @@ class FixpointExperiment(Experiment): net.self_attack() i += 1 if run_id: - weights = net.get_weights() + weights = net.get_weights_flat() self.add_trajectory_segment(run_id, weights) self.count(net) diff --git a/code/latent_trajectory_plot.html b/code/latent_trajectory_plot.html new file mode 100644 index 0000000..5d6eef6 --- /dev/null +++ b/code/latent_trajectory_plot.html @@ -0,0 +1,7 @@ +
\ No newline at end of file diff --git a/code/methods.py b/code/methods.py index 86fdd84..dd2497b 100644 --- a/code/methods.py +++ b/code/methods.py @@ -173,6 +173,7 @@ class FeedForwardNetwork(_BaseNetwork): bar.update() return losses + if __name__ == '__main__': with Experiment() as exp: features, cells, layers = 2, 2, 2 diff --git a/code/network.py b/code/network.py index 31be760..8c4e53a 100644 --- a/code/network.py +++ b/code/network.py @@ -83,6 +83,9 @@ class NeuralNetwork(PrintingObject): def get_weights(self): return self.model.get_weights() + def get_weights_flat(self): + return np.hstack([weight.flatten() for weight in self.get_weights()]) + def set_weights(self, new_weights): return self.model.set_weights(new_weights) @@ -603,9 +606,9 @@ class TrainingNeuralNetworkDecorator(NeuralNetwork): if __name__ == '__main__': - if False: + if True: with FixpointExperiment() as exp: - for run_id in tqdm(range(1)): + for run_id in tqdm(range(100)): # net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear') # net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)\ net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \ diff --git a/code/plot.html b/code/plot.html new file mode 100644 index 0000000..c519f39 --- /dev/null +++ b/code/plot.html @@ -0,0 +1,7 @@ +
\ No newline at end of file diff --git a/code/visualization.py b/code/visualization.py index 35604cc..30e6571 100644 --- a/code/visualization.py +++ b/code/visualization.py @@ -22,47 +22,42 @@ def build_args(): def plot_latent_trajectories(data_dict, filename='latent_trajectory_plot'): - # TODO Fist and Last Position Markers - - def norm(val, a=0, b=0.25): - return (val - a) / (b - a) - bupu = cl.scales['9']['seq']['BuPu'] - scale = cl.interp(bupu, len(data_dict)) # Map color scale to N bins + scale = cl.interp(bupu, len(data_dict)+1) # Map color scale to N bins # Fit the mebedding space transformer = TSNE() - for trajectory in data_dict: - transformer.fit(trajectory) + for trajectory_id in data_dict: + transformer.fit(np.asarray(data_dict[trajectory_id])) # Transform data accordingly and plot it data = [] - for t_id, trajectory in enumerate(data_dict): - transformed = transformer.fit(trajectory) + for trajectory_id in data_dict: + transformed = transformer._fit(np.asarray(data_dict[trajectory_id])) line_trace = go.Scatter( x=transformed[:, 0], y=transformed[:, 1], text='Hovertext goes here'.format(), - line=dict(color=scale[t_id]), + line=dict(color=scale[trajectory_id]), # legendgroup='Position -{}'.format(pos), # name='Position -{}'.format(pos), showlegend=False, # hoverinfo='text', mode='lines') - line_start = go.Scatter(mode='markers', x=transformed[0, 0], y=transformed[0, 1], + line_start = go.Scatter(mode='markers', x=[transformed[0, 0]], y=[transformed[0, 1]], marker=dict( color='rgb(255, 0, 0)', - size=2 + size=4 ), showlegend=False ) - line_end = go.Scatter(mode='markers', x=transformed[-1, 0], y=transformed[-1, 1], + line_end = go.Scatter(mode='markers', x=[transformed[-1, 0]], y=[transformed[-1, 1]], marker=dict( color='rgb(0, 0, 0)', - size=2 + size=4 ), showlegend=False - ) + ) data.extend([line_trace, line_start, line_end]) layout = dict(title='{} - Latent Trajectory Movement'.format('Penis'), @@ -74,30 +69,30 @@ def plot_latent_trajectories(data_dict, filename='latent_trajectory_plot'): pass -def plot_latent_trajectories_3D(param_dict, filename='plot'): +def plot_latent_trajectories_3D(data_dict, filename='plot'): def norm(val, a=0, b=0.25): return (val - a) / (b - a) bupu = cl.scales['9']['seq']['BuPu'] - scale = cl.interp(bupu, len(param_dict.get('trajectories', []))) # Map color scale to N bins + scale = cl.interp(bupu, len(data_dict)+1) # Map color scale to N bins - max_len = max([len(trajectory) for trajectory in param_dict.get('trajectories', [])]) + max_len = max([len(trajectory) for trajectory in data_dict.values()]) # Fit the mebedding space transformer = TSNE() - for trajectory in param_dict.get('trajectories', []): - transformer.fit(trajectory) + for trajectory_id in data_dict: + transformer.fit(data_dict[trajectory_id]) # Transform data accordingly and plot it data = [] - for t_id, trajectory in enumerate(param_dict.get('trajectories', [])): - transformed = transformer.fit(trajectory) + for trajectory_id in data_dict: + transformed = transformer._fit(np.asarray(data_dict[trajectory_id])) trace = go.Scatter3d( x=transformed[:, 0], y=transformed[:, 1], - z=np.arange(max(max_len)), + z=np.arange(transformed.shape[0]), text='Hovertext goes here'.format(), - line=dict(color=scale[t_id]), + line=dict(color=scale[trajectory_id]), # legendgroup='Position -{}'.format(pos), # name='Position -{}'.format(pos), showlegend=False, @@ -106,9 +101,9 @@ def plot_latent_trajectories_3D(param_dict, filename='plot'): data.append(trace) layout = go.Layout(scene=dict(aspectratio=dict(x=2, y=2, z=1), - xaxis=dict(tickwidth=1, title='Number of Cells'), - yaxis=dict(tickwidth=1, title='Number of Layers'), - zaxis=dict(tickwidth=1, title='Position -pX')), + xaxis=dict(tickwidth=1, title='Transformed X'), + yaxis=dict(tickwidth=1, title='transformed Y'), + zaxis=dict(tickwidth=1, title='Epoch')), title='{} - Latent Trajectory Movement'.format('Penis'), width=800, height=800, margin=dict(l=0, r=0, b=0, t=0)) @@ -125,18 +120,18 @@ def plot_histogram(bars_dict_list, filename='histogram_plot'): data = [] for bar_id, bars_dict in bars_dict_list: hist = go.Histogram( - histfunc="count", - y=bars_dict.get('value', 14), - x=bars_dict.get('name', 'gimme a name'), - showlegend=False, - marker=dict( - color=ryb[bar_id] - ), - ) + histfunc="count", + y=bars_dict.get('value', 14), + x=bars_dict.get('name', 'gimme a name'), + showlegend=False, + marker=dict( + color=ryb[bar_id] + ), + ) data.append(hist) layout=dict(title='{} Histogram Plot'.format('Experiment Name Penis'), - height=400, width=400, margin=dict(l=0, r=0, t=0, b=0)) + height=400, width=400, margin=dict(l=0, r=0, t=0, b=0)) fig = go.Figure(data=data, layout=layout) pl.offline.plot(fig, auto_open=True, filename=filename) @@ -161,7 +156,7 @@ def line_plot(line_dict_list, filename='lineplot'): marker=dict(color="#444"), line=dict(width=0), fillcolor=rdylgn_background[line_id], - ) + ) trace = go.Scatter( x=line_dict['x'], @@ -183,7 +178,7 @@ def line_plot(line_dict_list, filename='lineplot'): data.extend([upper_bound, trace, lower_bound]) layout=dict(title='{} Line Plot'.format('Experiment Name Penis'), - height=800, width=800, margin=dict(l=0, r=0, t=0, b=0)) + height=800, width=800, margin=dict(l=0, r=0, t=0, b=0)) fig = go.Figure(data=data, layout=layout) pl.offline.plot(fig, auto_open=True, filename=filename) @@ -195,4 +190,8 @@ if __name__ == '__main__': in_file = args.in_file[0] out_file = args.out_file + with open(in_file, 'rb') as in_f: + experiment = dill.load(in_f) + plot_latent_trajectories_3D(experiment.data_storage) + print('aha')