feat: more work for #23

views/py/python_model_template.py

@@ -1,5 +1,6 @@
 import tensorflow as tf
 import random
+import pandas as pd
 from tensorflow import keras
 from tensorflow.data import AUTOTUNE
 from keras import layers, losses, optimizers
@@ -7,12 +8,28 @@ from keras import layers, losses, optimizers
 DATA_DIR = "{{ .DataDir }}"
 image_size = ({{ .Size }})
 
-#based on https://www.tensorflow.org/tutorials/load_data/images
+df = pd.read_csv("{{ .RunPath }}/train.csv", dtype=str)
+keys = tf.constant(df['Id'].dropna())
+values = tf.constant(list(map(int, df['Index'].dropna())))
 
+table = tf.lookup.StaticHashTable(
+    initializer=tf.lookup.KeyValueTensorInitializer(
+        keys=keys,
+        values=values,
+    ),
+    default_value=tf.constant(-1),
+    name="Indexes"
+)
+
+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, "")
+  path = tf.strings.regex_replace(path, DATA_DIR_PREPARE, "")
   path = tf.strings.regex_replace(path, ".jpg", "")
-  return train_labels[tf.strings.to_number(path, out_type=tf.int32)]
+  path = tf.strings.regex_replace(path, ".png", "")
+  return table.lookup(tf.strings.as_string([path]))
+  #return tf.strings.as_string([path])
 
 def decode_image(img):
   # channels were reduced to 1 since image is grayscale
@@ -75,7 +92,7 @@ model = keras.Sequential([
 ])
 
 model.compile(
-    loss=losses.SparceCategoricalCrossentropy(),
+    loss=losses.SparseCategoricalCrossentropy(),
     optimizer=tf.keras.optimizers.Adam(),
     metrics=['accuracy'])