feat: closes #39

views/py/python_model_template.py

@@ -4,6 +4,14 @@ import pandas as pd
 from tensorflow import keras
 from tensorflow.data import AUTOTUNE
 from keras import layers, losses, optimizers
+import requests
+
+class NotifyServerCallback(tf.keras.callbacks.Callback):
+    def on_epoch_begin(self, epoch, *args, **kwargs):
+        if (epoch % 5) == 0:
+            # TODO change this
+            requests.get(f'http://localhost:8000/model/epoch/update?model_id={{.Model.Id}}&epoch={epoch}&definition={{.DefId}}')
+
 
 DATA_DIR = "{{ .DataDir }}"
 image_size = ({{ .Size }})
@@ -26,11 +34,15 @@ DATA_DIR_PREPARE = DATA_DIR + "/"
 #based on https://www.tensorflow.org/tutorials/load_data/images
 def pathToLabel(path):
   path = tf.strings.regex_replace(path, DATA_DIR_PREPARE, "")
+  {{ if eq .Model.Format "png" }}
+  path = tf.strings.regex_replace(path, ".png", "")
+  {{ else if eq .Model.Format "jpeg" }}
   path = tf.strings.regex_replace(path, ".jpg", "")
   path = tf.strings.regex_replace(path, ".jpeg", "")
-  path = tf.strings.regex_replace(path, ".png", "")
+  {{ else }}
+  ERROR
+  {{ end }}
   return table.lookup(tf.strings.as_string([path]))
-  #return tf.strings.as_string([path])
 
 def decode_image(img):
   {{ if eq .Model.Format "png" }}
@@ -100,7 +112,7 @@ model.compile(
     optimizer=tf.keras.optimizers.Adam(),
     metrics=['accuracy'])
 
-his = model.fit(dataset, validation_data= dataset_validation, epochs=50)
+his = model.fit(dataset, validation_data= dataset_validation, epochs=50, callbacks=[NotifyServerCallback()])
 
 acc = his.history["accuracy"]