started working on runner

views/py/python_model_template.py

@@ -82,7 +82,7 @@ def prepare_dataset(ds: tf.data.Dataset, size: int) -> tf.data.Dataset:
 
 def filterDataset(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" }}
@@ -90,7 +90,7 @@ def filterDataset(path):
   {{ else }}
   ERROR
   {{ end }}
-  
+
   return tf.reshape(table.lookup(tf.strings.as_string([path])), []) != -1
 
 seed = random.randint(0, 100000000)
@@ -135,9 +135,9 @@ def addBlock(
         model.add(layers.ReLU())
     if top:
         if pooling_same:
-            model.add(pool_func(padding="same", strides=(1, 1)))
+            model.add(pool_func(pool_size=(2,2), padding="same", strides=(1, 1)))
         else:
-            model.add(pool_func())
+            model.add(pool_func(pool_size=(2,2)))
         model.add(layers.BatchNormalization())
         model.add(layers.LeakyReLU())
         model.add(layers.Dropout(0.4))
@@ -172,7 +172,7 @@ model.compile(
 
 his = model.fit(dataset, validation_data= dataset_validation, epochs={{.EPOCH_PER_RUN}}, callbacks=[
                 NotifyServerCallback(),
-                tf.keras.callbacks.EarlyStopping("loss", mode="min", patience=5)], use_multiprocessing = True)
+                tf.keras.callbacks.EarlyStopping("loss", mode="min", patience=5)])
 
 acc = his.history["accuracy"]