worked on #32

views/py/python_model_template.py

@@ -7,10 +7,8 @@ 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}}')
+    def on_epoch_end(self, epoch, log, *args, **kwargs):
+        requests.get(f'http://localhost:8000/model/epoch/update?model_id={{.Model.Id}}&epoch={epoch}&accuracy={log["accuracy"]}&definition={{.DefId}}')
 
 
 DATA_DIR = "{{ .DataDir }}"
@@ -84,7 +82,7 @@ def filterDataset(path):
 
 seed = random.randint(0, 100000000)
 
-batch_size = 100
+batch_size = 64
 
 # Read all the files from the direcotry
 list_ds = tf.data.Dataset.list_files(str(f'{DATA_DIR}/*'), shuffle=False)
@@ -102,23 +100,55 @@ val_ds = list_ds.take(val_size)
 dataset = prepare_dataset(train_ds)
 dataset_validation   = prepare_dataset(val_ds)
 
+track = 0
+
+def addBlock(
+    b_size: int,
+    filter_size: int,
+    kernel_size: int = 3,
+    top: bool = True,
+    pooling_same: bool = False,
+    pool_func=layers.MaxPool2D
+):
+    global track
+    model = keras.Sequential(name=f"{track}-{b_size}-{filter_size}-{kernel_size}")
+    track += 1
+    for _ in range(b_size):
+        model.add(layers.Conv2D(
+            filter_size,
+            kernel_size,
+            padding="same"
+        ))
+        model.add(layers.ReLU())
+    if top:
+        if pooling_same:
+            model.add(pool_func(padding="same", strides=(1, 1)))
+        else:
+            model.add(pool_func())
+        model.add(layers.BatchNormalization())
+        model.add(layers.LeakyReLU())
+        model.add(layers.Dropout(0.4))
+    return model
+
 
 {{ if .LoadPrev }}
 model = tf.keras.saving.load_model('{{.LastModelRunPath}}')
 {{ else }}
-model = keras.Sequential([
-    {{- range .Layers }}
-    {{- if eq .LayerType 1}}
-    layers.Rescaling(1./255),
-    {{- else if eq .LayerType 2 }}
-    layers.Dense({{ .Shape }}, activation="sigmoid"),
-    {{- else if eq .LayerType 3}}
-    layers.Flatten(),
-    {{- else }}
-    ERROR
-    {{- end }}
-    {{- end }}
-])
+model = keras.Sequential()
+
+{{- range .Layers }}
+{{- if eq .LayerType 1}}
+model.add(layers.Rescaling(1./255))
+{{- else if eq .LayerType 2 }}
+model.add(layers.Dense({{ .Shape }}, activation="sigmoid"))
+{{- else if eq .LayerType 3}}
+model.add(layers.Flatten())
+{{- else if eq .LayerType 4}}
+model.add(addBlock(2, 128, 3, pool_func=layers.AveragePooling2D))
+{{- else }}
+ERROR
+{{- end }}
+{{- end }}
 {{ end }}
 
 model.compile(
@@ -126,7 +156,7 @@ model.compile(
     optimizer=tf.keras.optimizers.Adam(),
     metrics=['accuracy'])
 
-his = model.fit(dataset, validation_data= dataset_validation, epochs={{.EPOCH_PER_RUN}}, callbacks=[NotifyServerCallback()])
+his = model.fit(dataset, validation_data= dataset_validation, epochs={{.EPOCH_PER_RUN}}, callbacks=[NotifyServerCallback()], use_multiprocessing = True)
 
 acc = his.history["accuracy"]