renaming all files - moving training to be a single file for transfer vs not transfer learning. Made the testing file test all models. Needs to be updated to only update with new models.

model_builder/image_class_builder.py

@@ -0,0 +1,98 @@
+from enum import Enum
+from time import time
+from typing import Tuple
+
+import tensorflow as tf
+from tensorflow import keras
+
+from .model_wrapper import ModelWrapper
+
+
+class ImageClassModels(Enum):
+    INCEPTION_V3 = ModelWrapper(
+        keras.applications.inception_v3.InceptionV3,
+        keras.applications.inception_v3.preprocess_input,
+        "inception_v3"
+    )
+    XCEPTION = ModelWrapper(
+        keras.applications.xception.Xception,
+        keras.applications.xception.preprocess_input,
+        "xception"
+    )
+    MOBILENET_V2 = ModelWrapper(
+        keras.applications.mobilenet_v2.MobileNetV2,
+        keras.applications.mobilenet_v2.preprocess_input,
+        "mobilenet_v2"
+    )
+
+
+class ImageClassModelBuilder(object):
+
+    def __init__(self,
+                 input_shape: Tuple[int, int, int],
+                 n_classes: int,
+                 optimizer: tf.keras.optimizers.Optimizer = keras.optimizers.Adam(
+                     learning_rate=.0001),
+                 pre_trained: bool = True,
+                 fine_tune: bool = False,
+                 base_model_type: ImageClassModels = ImageClassModels.MOBILENET_V2,
+                 dense_layer_neurons: int = 1024,
+                 dropout_rate: float = .5,
+                 l1: float = 1e-4,
+                 l2: float = 1e-4):
+        self.input_shape = input_shape
+        self.n_classes = n_classes
+        self.optimizer = optimizer
+        self.pre_trained = pre_trained
+        self.fine_tune = fine_tune
+        self.dense_layer_neurons = dense_layer_neurons
+        self.dropout_rate = dropout_rate
+        self.l1 = l1
+        self.l2 = l2
+        self.set_base_model(base_model_type)
+
+    def set_base_model(self, base_model_type: ImageClassModels):
+        self.base_model_type = base_model_type
+        self.base_model = self.base_model_type.value.model_func(
+            weights='imagenet' if self.pre_trained else None,
+            input_shape=self.input_shape,
+            include_top=False
+        )
+
+    def create_model(self):
+        if not self.fine_tune:
+            self.base_model.trainable = False
+        i = tf.keras.layers.Input([self.input_shape[0], self.input_shape[1], self.input_shape[2]], dtype=tf.float32)
+        x = tf.cast(i, tf.float32)
+        x = self.base_model_type.value.model_preprocessor(x)
+        x = self.base_model(x)
+        x = keras.layers.GlobalAveragePooling2D()(x)
+        x = keras.layers.Dense(self.dense_layer_neurons, activation='relu',
+                               kernel_regularizer=keras.regularizers.L1L2(l1=self.l1, l2=self.l2))(x)
+        x = keras.layers.Dropout(self.dropout_rate)(x)
+        output = keras.layers.Dense(self.n_classes, activation='softmax')(x)
+        self.model = keras.Model(inputs=i, outputs=output)
+        self.model.compile(
+            optimizer=self.optimizer,
+            loss=keras.losses.CategoricalCrossentropy(),
+            metrics=['accuracy', 'categorical_crossentropy']
+        )
+        self.model.summary()
+        return self.model
+
+    def get_fine_tuning(self):
+        print("self.model is found")
+        self.base_model.trainable = True
+        self.model.compile(
+            optimizer=self.optimizer,
+            loss=keras.losses.CategoricalCrossentropy(),
+            metrics=['accuracy', 'categorical_crossentropy']
+        )
+        self.model.summary()
+        return self.model
+
+    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"-{int(time())}"