believe to have created a function to build a svg plot and send it over the network

neural_network/main.py

@@ -3,6 +3,7 @@ import numpy as np
 
 from neural_network.opts import activation
 from neural_network.backprop import bp
+from neural_network.plot import plot
 
 
 def init(X: np.array, hidden_size: int) -> dict:
@@ -46,4 +47,7 @@ def main(
         X_test=X_test,
         y_test=y_test,
     )
+
+    plot(model=model)
+
     return model.to_dict()

neural_network/neural_network.py

@@ -19,6 +19,7 @@ class NeuralNetwork:
     loss_history: list = field(
         default_factory=lambda: [],
     )
+    plot = None
 
     def predict(self, x: np.array) -> np.array:
         n1 = self.compute_node(x, self.w1, self.b1, self.activation_func)
@@ -51,4 +52,5 @@ class NeuralNetwork:
             "hidden_size": self.hidden_size,
             "mse": self.mse,
             "loss_history": self.loss_history,
+            "plot": self.plot,
         }

neural_network/plot.py

@@ -0,0 +1,21 @@
+import numpy as np
+import base64
+import io
+import seaborn as sns
+import matplotlib
+import matplotlib.pyplot as plt
+from neural_network.neural_network import NeuralNetwork
+
+matplotlib.use("Agg")
+
+def plot(model: NeuralNetwork) -> None:
+    _ = sns.scatterplot(
+        x=np.arange(len(model.loss_history)),
+        y=model.loss_history,
+    )
+    buf = io.BytesIO() 
+    plt.savefig(buf, format="svg")
+    plt.clf()
+    buf.seek(0)
+    plot_data = base64.b64encode(buf.getvalue()).decode("utf-8")
+    model.plot = plot_data