Initial commit

ui/front_page/plots/gradients.py

@@ -0,0 +1,41 @@
+from abc import ABC
+
+from matplotlib.figure import Figure
+
+from neural_net.epoch import Epoch
+from neural_net.neural_net import NeuralNet
+from ui.components.plot_figure import PlotFrame
+from ui.plotters.plotter import Plotter
+
+class GradientsPlot(PlotFrame):
+    def __init__(self, parent, neural_net: NeuralNet):
+        super().__init__(parent)
+        self.plotter = GradientsPlotter(self.figure, neural_net)
+
+class GradientsPlotter(Plotter, ABC):
+    def __init__(self, figure: Figure, neural_net: NeuralNet):
+        super().__init__(figure)
+        self.neural_net = neural_net
+        self.axes = figure.subplots(1, 2)
+
+    def reset_plot(self):
+        self.axes[0].clear()
+        self.axes[0].set_xlabel('Neuron Index')
+        self.axes[0].set_ylabel('Input Index')
+        self.axes[1].clear()
+        self.axes[1].set_xlabel('Output Neuron Index')
+        self.axes[1].set_ylabel('Hidden Neuron Index')
+
+    def plot(self, data: Epoch):
+        gradients_layer1 = data.layer_dl_gradients[1][-1]
+        self.axes[0].imshow(gradients_layer1, cmap='coolwarm', aspect='auto')
+
+        gradients_layer2 = data.layer_dl_gradients[0][-1]
+        self.axes[1].imshow(gradients_layer2, cmap='coolwarm', aspect='auto')
+
+    def plot_gradients_histogram(self, current_epoch: Epoch):
+        gradients_layer1 = current_epoch.layer_dl_gradients[1][-1]
+        self.axes[0].hist(gradients_layer1.flatten(), bins=50, color='blue', alpha=0.7)
+
+        gradients_layer2 = current_epoch.layer_dl_gradients[0][-1]
+        self.axes[1].hist(gradients_layer2.flatten(), bins=50, color='green', alpha=0.7)

ui/front_page/plots/layer_weights.py

@@ -0,0 +1,39 @@
+from ui.components.plot_figure import PlotFrame
+import math
+from abc import ABC
+
+from matplotlib.figure import Figure
+
+from neural_net.activation_layers.activation_layer import ActivationLayer
+from neural_net.neural_net import NeuralNet
+from ui.plotters.plotter import Plotter
+from utils.matplotlib.utils import mpl_matshow
+
+class LayerWeightsPlot(PlotFrame):
+    def __init__(self, parent, neural_net: NeuralNet, layer: ActivationLayer, rows, cols):
+        super().__init__(parent)
+        self.plotter = LayerWeightsPlotter(self.figure, neural_net, layer, rows, cols)
+
+class LayerWeightsPlotter(Plotter, ABC):
+    def __init__(self, figure: Figure, neural_net: NeuralNet, layer: ActivationLayer, rows, columns):
+        super().__init__(figure)
+        self.neural_net = neural_net
+        self.layer = layer
+        self.axes = figure.subplots(nrows=rows, ncols=columns, squeeze=True,
+                                    gridspec_kw={'wspace': 0.05, 'hspace': 0.05})
+
+    def reset_plot(self):
+        for axes in self.axes:
+            for ax in axes:
+                ax.clear()
+
+    def plot(self, data):
+        weights = self.layer.weights.T
+        n_neurons = weights.shape[0]
+        n_pixels = weights.shape[1]
+        for i in range(n_neurons):
+            row = i // self.axes.shape[1]
+            col = i % self.axes.shape[1]
+            mpl_matshow(self.axes[row, col], weights[i], int(math.sqrt(n_pixels)))
+
+

ui/front_page/plots/loss.py

@@ -0,0 +1,40 @@
+from neural_net.trainer import NeuralNetTrainer
+from ui.components.plot_figure import PlotFrame
+
+from abc import ABC
+
+from matplotlib.figure import Figure
+
+from neural_net.neural_net import NeuralNet
+from ui.plotters.plotter import Plotter
+
+class LossPlot(PlotFrame):
+    def __init__(self, parent, neural_net: NeuralNet, trainer: NeuralNetTrainer):
+        super().__init__(parent)
+        self.plotter = LossPlotter(self.figure, neural_net, trainer)
+
+class LossPlotter(Plotter, ABC):
+    def __init__(self, figure: Figure, neural_net: NeuralNet, trainer: NeuralNetTrainer):
+        super().__init__(figure)
+        self.neural_net = neural_net
+        self.trainer = trainer
+        self.axes = figure.add_subplot()
+
+    def reset_plot(self):
+        self.axes.clear()
+        self.axes.set_title('Loss')
+        self.axes.set_ylabel("Loss")
+        self.axes.set_xlabel("Epoch")
+
+    def plot(self, data):
+        losses = []
+        for epoch in self.trainer.epoch_history:
+            if epoch.finished:
+                losses.append(epoch.loss)
+
+        self.axes.plot(losses, marker='o', label=f"Loss")
+        for idx, loss in enumerate(losses):
+            self.axes.annotate(f"{loss:.4f}", xy=(idx, loss), rotation=45)
+
+        self.axes.legend()
+        self.axes.grid(True)

ui/front_page/plots/predictions.py

@@ -0,0 +1,37 @@
+from ui.components.plot_figure import PlotFrame
+from abc import ABC
+
+from matplotlib.figure import Figure
+
+from ui.plotters.plotter import Plotter
+
+class PredictionsPlot(PlotFrame):
+    def __init__(self, parent):
+        super().__init__(parent, height=32)
+        self.plotter = PredictionsPlotter(self.figure)
+
+class PredictionsPlotter(Plotter, ABC):
+    def __init__(self, figure: Figure):
+        super().__init__(figure)
+        self.axes = figure.add_subplot()
+        self.clean_axes()
+
+    def plot(self, data):
+        self.axes.imshow(data, cmap='coolwarm', aspect='auto')
+        for idx in range(10):
+            self.axes.annotate(f"{idx}", xy=(idx - 0.2, 0.2))
+        self.clean_axes()
+
+    def clean_axes(self):
+        # Remove axis ticks, labels, and spines
+        self.axes.set_xticks([])  # Remove x-ticks
+        self.axes.set_yticks([])  # Remove y-ticks
+        self.axes.spines['top'].set_visible(False)
+        self.axes.spines['bottom'].set_visible(False)
+        self.axes.spines['left'].set_visible(False)
+        self.axes.spines['right'].set_visible(False)
+        self.axes.set_facecolor((0, 0, 0))
+
+    def reset_plot(self):
+        self.axes.clear()
+