creating model builder for faster model tweaking and iteration. Increased threads for training to better feed the GPU images from the image generator.

model_builders/image_class_builder.py

@@ -0,0 +1,76 @@
+from enum import Enum
+from typing import Tuple
+
+import tensorflow as tf
+from tensorflow import keras
+
+from .modelwrapper import ModelWrapper
+
+
+class ImageClassModels(Enum):
+    INCEPTION_V3 = ModelWrapper(
+        keras.applications.InceptionV3,
+        keras.applications.inception_v3.preprocess_input
+    )
+    XCEPTION = ModelWrapper(
+        keras.applications.xception.Xception,
+        keras.applications.inception_v3.preprocess_input
+    )
+    MOBILENET_V2 = ModelWrapper(
+        keras.applications.mobilenet_v2.MobileNetV2,
+        keras.applications.mobilenet_v2.preprocess_input
+    )
+
+
+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: int = 0,
+                 base_model: ImageClassModels = ImageClassModels.MOBILENET_V2):
+        self.input_shape = input_shape
+        self.n_classes = n_classes
+        self.optimizer = optimizer
+        self.pre_trained = pre_trained
+        self.fine_tune = fine_tune
+        self.base_model = base_model
+
+    def set_base_model(self, base_model: ImageClassModels):
+        self.base_model = base_model
+
+    def create_model(self):
+
+        base_model = self.base_model.value.model_func(
+            weights='imagenet' if self.pre_trained else None,
+            include_top=False
+        )
+        if self.pre_trained:
+            if self.fine_tune > 0:
+                for layer in base_model.layers[:-self.fine_tune]:
+                    layer.trainable = False
+            else:
+                for layer in base_model.layers:
+                    layer.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.value.model_preprocessor(x)
+        x = base_model(x)
+        x = keras.layers.GlobalAveragePooling2D()(x)
+        x = keras.layers.Dense(1024, activation='relu', kernel_regularizer=keras.regularizers.L1L2(l1=1e-5, l2=1e-5))(x)
+        x = keras.layers.Dropout(0.25)(x)
+        output = keras.layers.Dense(self.n_classes, activation='softmax')(x)
+
+        model = keras.Model(inputs=i, outputs=output)
+        model.compile(optimizer=self.optimizer,
+                      loss=keras.losses.CategoricalCrossentropy(),
+                      metrics=[
+                          'accuracy',
+                          # 'mse'
+                      ])
+        model.summary()
+        return model