clean up imports. fix naming, force CPU to fill the cache faster with images using 20 workers.

4_train_keras_model.py

@@ -54,7 +54,7 @@ def get_gen(path, dataset_type: DatasetType = DatasetType.TRAIN):
 
 def train_model(model_builder, train_gen, val_gen):
     model = model_builder.create_model()
-    model_name = "rot-shift-" + model_builder.get_name()
+    model_name = model_builder.get_name()
     print(model)
     print(f"NOW TRAINING: {model_name}")
     checkpoint = keras.callbacks.ModelCheckpoint(
@@ -83,11 +83,11 @@ def train_model(model_builder, train_gen, val_gen):
     history = model.fit(
         train_gen,
         validation_data=val_gen,
-        epochs=500,
+        epochs=8,
         batch_size=batch_size,
         shuffle=True,
         verbose=True,
-        workers=12,
+        workers=20,
         callbacks=[checkpoint, early, tensorboard],
         max_queue_size=1000
     )
@@ -134,7 +134,7 @@ if __name__ == "__main__":
             fine_tune=True,
             base_model_type=ImageClassModels.MOBILENET_V2,
             dense_layer_neurons=1024,
-            dropout_rate=.33,
+            dropout_rate=.5,
         )
     ]
     for mb in model_builders:

5_test_models.py

@@ -15,15 +15,13 @@ ImageFile.LOAD_TRUNCATED_IMAGES = True
 accuracies = []
 losses = []
 filenames = []
-test_idg = ImageDataGenerator(
-)
 
 input_shape = (224, 224, 3)
 batch_size = 32
 
-test_gen = test_idg.flow_from_directory(
+test_gen = ImageDataGenerator().flow_from_directory(
-    # './data/test',
+    './data/test',
-    './single_image_test_set',
+    # './single_image_test_set',
     target_size=(input_shape[0], input_shape[1]),
     batch_size=batch_size,
     shuffle=False
@@ -34,17 +32,15 @@ for file  in glob("./models/keras/*"):
     print(file)
     model = load_model(file)
 
-
+    predictions = model.predict(test_gen, verbose=True, workers=12)
-
-    predictions = model.predict(test_gen, verbose=True, workers=12, steps=len(test_gen))
 
     print(predictions)
     print(type(predictions))
     print(predictions.shape)
+
     # Process the predictions
     predictions = np.argmax(predictions,
                             axis=1)
-    # test_gen.reset()
     label_index = {v: k for k, v in test_gen.class_indices.items()}
     predictions = [label_index[p] for p in predictions]
     reals = [label_index[p] for p in test_gen.classes]
@@ -69,24 +65,12 @@ for file  in glob("./models/keras/*"):
     print("Confusion Matrix", conf_mat)
 
     accuracies.append(acc)
-    # df_cm = pd.DataFrame(conf_mat, index=[i for i in list(set(reals))],
-    #                      columns=[i for i in list(set(reals))])
-    # print("made dataframe")
-    # plt.figure(figsize=(10, 7))
-    # print("made plot")
-    # # sn.heatmap(df_cm, annot=True)
-    # print("showing plot")
-    # plt.show()
-
-
-    # with open("labels.txt", "w") as f:
-    #     for label in label_index.values():
-    #         f.write(label + "\n")
 
 overall_df = pd.DataFrame(list(zip(filenames, accuracies)),
                columns =['model', 'acc']).sort_values('acc')
 
 print(overall_df)
-overall_df.to_csv("all_model_output.csv")
+overall_df.plot.bar(y="acc", rot=90)
-overall_df.plot.bar(x="model", y="acc", rot=0)
+plt.tight_layout()
 plt.show()
+overall_df.to_csv("all_model_output.csv")

model_builder/image_class_builder.py

@@ -1,7 +1,8 @@
+import random
 from enum import Enum
-from time import time
 from typing import Tuple
 
+import numpy as np
 import tensorflow as tf
 from tensorflow import keras
 
@@ -94,5 +95,5 @@ class ImageClassModelBuilder(object):
     def get_name(self):
         return f"{'pt-' if self.pre_trained else ''}{'ft-' if self.fine_tune else ''}" \
                f"{self.base_model_type.value.name}-d{self.dense_layer_neurons}-do{self.dropout_rate}" \
-               f"{'-l1' + str(self.l1) if self.l1 > 0 else ''}{'-l2' + str(self.l2) if self.l2 > 0 else ''}" \
+               f"{'-l1' + np.format_float_scientific(self.l1) if self.l1 > 0 else ''}{'-l2' + np.format_float_scientific(self.l2) if self.l2 > 0 else ''}" \
-               f"-{int(time())}"
+               f"-{random.randint(1111, 9999)}"