feat: started working on #23

views/models/edit.html

@@ -63,7 +63,11 @@
             {{range .List}}
                 <tr>
                     <td>
-                        {{.FilePath}}
+                        {{ if eq .FilePath "id://" }}
+                            Managed
+                        {{ else }}
+                            {{.FilePath}}
+                        {{ end }}
                     </td>
                     <td>
                         {{ if (eq .Mode 2) }}
@@ -73,8 +77,8 @@
                         {{ end }}
                     </td>
                     <td class="text-center">
-                        {{ if startsWith .FilePath "file://" }}
-                            <img src="/savedData/{{ $.ModelId }}/data/{{ if (eq .Mode 2) }}testing{{ else }}training{{ end }}/{{ $.Name }}/{{ replace .FilePath "file://" "" 1 }}" height="30px" width="30px" style="object-fit: contain;" />
+                        {{ if startsWith .FilePath "id://" }}
+                            <img src="/savedData/{{ $.ModelId }}/data/{{ .Id }}.png" height="30px" width="30px" style="object-fit: contain;" />
                         {{ else }}
                             TODO
                             img {{ .FilePath }}

views/py/python_model_template.py

@@ -1,33 +1,64 @@
 import tensorflow as tf
 import random
 from tensorflow import keras
+from tensorflow.data import AUTOTUNE
 from keras import layers, losses, optimizers
 
+DATA_DIR = "{{ .DataDir }}"
+image_size = ({{ .Size }})
+
+#based on https://www.tensorflow.org/tutorials/load_data/images
+
+def pathToLabel(path):
+  path = tf.strings.regex_replace(path, DATA_DIR, "")
+  path = tf.strings.regex_replace(path, ".jpg", "")
+  return train_labels[tf.strings.to_number(path, out_type=tf.int32)]
+
+def decode_image(img):
+  # channels were reduced to 1 since image is grayscale
+  # TODO chnage channel number based if grayscale
+  img = tf.io.decode_png(img, channels=1)
+
+  return tf.image.resize(img, image_size)
+
+def process_path(path):
+  label = pathToLabel(path)
+
+  img = tf.io.read_file(path)
+  img = decode_image(img)
+
+  return img, label
+
+def configure_for_performance(ds: tf.data.Dataset) -> tf.data.Dataset:
+  #ds = ds.cache()
+  ds = ds.shuffle(buffer_size= 1000)
+  ds = ds.batch(batch_size)
+  ds = ds.prefetch(AUTOTUNE)
+  return ds
+
+def prepare_dataset(ds: tf.data.Dataset) -> tf.data.Dataset:
+  ds = ds.map(process_path, num_parallel_calls=AUTOTUNE)
+  ds = configure_for_performance(ds)
+  return ds
+
 seed = random.randint(0, 100000000)
 
 batch_size = 100
 
-dataset = keras.utils.image_dataset_from_directory(
-  "{{ .DataDir }}",
-  color_mode="rgb",
-  validation_split=0.2,
-  label_mode='categorical',
-  seed=seed,
-  shuffle=True,
-  subset="training",
-  image_size=({{ .Size }}),
-  batch_size=batch_size)
+# Read all the files from the direcotry
+list_ds = tf.data.Dataset.list_files(str(f'{DATA_DIR}/*'), shuffle=False)
 
-dataset_validation = keras.utils.image_dataset_from_directory(
-  "{{ .DataDir }}",
-  color_mode="rgb",
-  validation_split=0.2,
-  label_mode='categorical',
-  seed=seed,
-  shuffle=True,
-  subset="validation",
-  image_size=({{ .Size }}),
-  batch_size=batch_size)
+image_count = len(list_ds)
+
+list_ds = list_ds.shuffle(image_count, seed=seed)
+
+val_size = int(image_count * 0.3)
+
+train_ds = list_ds.skip(val_size)
+val_ds = list_ds.take(val_size)
+
+dataset = prepare_dataset(train_ds)
+dataset_validation   = prepare_dataset(val_ds)
 
 model = keras.Sequential([
     {{- range .Layers }}
@@ -44,7 +75,7 @@ model = keras.Sequential([
 ])
 
 model.compile(
-    loss=losses.CategoricalCrossentropy(),
+    loss=losses.SparceCategoricalCrossentropy(),
     optimizer=tf.keras.optimizers.Adam(),
     metrics=['accuracy'])