Initial commit

2026-04-06 15:59:23 +02:00
commit 60e9dfa5ad
83 changed files with 2167 additions and 0 deletions
@@ -0,0 +1,103 @@
+import threading
+import tkinter as tk
+
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+from matplotlib.figure import Figure
+
+from neural_net.epoch import Epoch
+from neural_net.trainer import NeuralNetTrainer
+from ui.app_state import AppState
+from ui.front_page.plots.gradients import GradientsPlot
+from ui.front_page.plots.layer_weights import LayerWeightsPlot
+from ui.front_page.plots.loss import LossPlot
+from ui.front_page.sections.training_information import EpochInformation
+
+
+class TrainingPage(tk.Frame):
+    def __init__(self, parent, app_state: AppState, on_training_finished=None):
+        super().__init__(parent)
+        self.app_state = app_state
+        self.on_training_finished = on_training_finished
+        self.trainer: NeuralNetTrainer = None
+        # trainer = NeuralNetTrainer(self.app_state.neural_net, self.app_state.model_data, learning_rate, nr_epochs)
+        # self.app_state.trainers.append(trainer)
+        # self.trainer = trainer
+        self.create_ui()
+
+    def start(self, learning_rate, nr_epochs, batch_size, callback=None):
+        if self.trainer is not None:
+            self.trainer.stop()
+        self.trainer = NeuralNetTrainer(self.app_state.neural_net, self.app_state.model_data,
+                                        learning_rate=learning_rate, nr_epochs=nr_epochs, batch_size=batch_size,
+                                        on_epoch_callback=self.update_training_data,
+                                        on_finished_callback=self.on_training_finished)
+        self.trainer.on_epoch_callback = self.update_training_data
+        self.thread = threading.Thread(target=self.trainer.start)
+        self.thread.start()
+        # self.trainer.start(on_epoch_finished=self.update_training_data)
+        if callback is not None:
+            callback()
+
+    def update_training_data(self, training_run, data: Epoch):
+        print(f"Updating training data {data.epoch}")
+        if self.trainer.is_running:
+            self.training_information_container.update_training_data(training_run, data)
+
+            self.loss_plot.update_training_data(training_run, data)
+            if data.epoch % 5 == 0:
+                self.gradients_plot.update_training_data(training_run, data)
+                self.layer0_weights_plot.update_training_data(training_run, data)
+                self.layer1_weights_plot.update_training_data(training_run, data)
+
+    def create_ui(self):
+        # Training center
+        self.training_information_container = EpochInformation(self, self.app_state.neural_net, self.trainer)
+        self.training_information_container.pack(side=tk.TOP, fill=tk.X, expand=False, pady=10, padx=10, ipady=10,
+                                                 ipadx=10)
+
+        actions_frame = tk.Frame(self)
+        actions_frame.pack(side=tk.TOP, fill=tk.X, expand=False, pady=10, padx=10, ipady=10, ipadx=10)
+        btn_text = "Pause"
+        self.btn_toggle_pause = tk.Button(actions_frame, text=btn_text, command=self.toggle_state)
+        self.btn_toggle_pause.pack(side=tk.LEFT)
+        btn_stop = tk.Button(actions_frame, text="Stop", command=self.trainer.stop)
+        btn_stop.pack(side=tk.LEFT)
+
+        # Plot tabs
+        plot_tab_control = tk.Notebook(self)
+        plot_tab_control.pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True, pady=0, padx=0, ipady=0, ipadx=0)
+
+        self.loss_plot = LossPlot(plot_tab_control, self.app_state.neural_net)
+        plot_tab_control.add(self.loss_plot, text="Loss Function")
+
+        self.gradients_plot = GradientsPlot(plot_tab_control, self.app_state.neural_net)
+        plot_tab_control.add(self.gradients_plot, text="Gradients")
+
+        self.layer0_weights_plot = LayerWeightsPlot(plot_tab_control, self.app_state.neural_net,
+                                                          self.app_state.neural_net.layers[0],
+                                                          11, 11)
+        plot_tab_control.add(self.layer0_weights_plot, text="Weights layer 0")
+
+        self.layer1_weights_plot = LayerWeightsPlot(plot_tab_control, self.app_state.neural_net,
+                                                          self.app_state.neural_net.layers[1],
+                                                          2, 5)
+        plot_tab_control.add(self.layer1_weights_plot, text="Weights layer 1")
+
+    def toggle_state(self):
+        self.trainer.toggle_state()
+        if self.trainer.training_paused:
+            self.btn_toggle_pause.config(text="Resume")
+        else:
+            self.btn_toggle_pause.config(text="Pause")
+
+    @staticmethod
+    def create_plot_figure(tab):
+        figure = Figure()
+
+        # Create a matplotlib canvas to display the plot
+        canvas = FigureCanvasTkAgg(figure, tab)
+        canvas.draw()
+        canvas.get_tk_widget().pack(fill=tk.BOTH, expand=True)
+
+        return figure
+