rename models

JordiCorbilla · Nov 24, 2019 · 53fbda0 · 53fbda0
1 parent 8946a30
commit 53fbda0
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Showing 4 changed files with 24 additions and 16 deletions.
diff --git a/odir_model_factory.py b/odir_model_factory.py
@@ -15,23 +15,23 @@
 import enum
 
 from odir_model_advanced import Advanced
-from odir_model_googlenet import GoogleNet
-from odir_model_vggnet import VggNet
+from odir_model_inception_v1 import InceptionV1
+from odir_model_vgg16 import Vgg16
 
 
 class ModelTypes(enum.Enum):
-    vgg_net = 1
-    google_net = 2
-    advanced = 3
+    vgg16 = 1
+    inception_v1 = 2
+    advanced_testing = 3
 
 
 class Factory:
 
     def __init__(self, input_shape):
         self.Makers = {
-            ModelTypes.vgg_net: VggNet(input_shape),
-            ModelTypes.google_net: GoogleNet(input_shape),
-            ModelTypes.advanced: Advanced(input_shape)
+            ModelTypes.vgg16: Vgg16(input_shape),
+            ModelTypes.inception_v1: InceptionV1(input_shape),
+            ModelTypes.advanced_testing: Advanced(input_shape)
         }
 
     def compile(self, model_type):

diff --git a/odir_model_googlenet.py → odir_model_inception_v1.py b/odir_model_googlenet.py → odir_model_inception_v1.py
@@ -19,7 +19,7 @@
 from odir_model_base import ModelBase
 
 
-class GoogleNet(ModelBase):
+class InceptionV1(ModelBase):
 
     def compile(self):
         input_img = Input(shape=self.input_shape)
@@ -40,8 +40,10 @@ def compile(self):
         dense_3 = Dense(150, activation='relu')(dense_2)
         output = Dense(8, activation='sigmoid')(dense_3)
         model = Model([input_img], output)
+
         model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
         self.show_summary(model)
-        self.plot_summary(model, 'model_googlenet.png')
+        self.plot_summary(model, 'model_inception_v1.png')
 
         return model
diff --git a/odir_model_runner.py b/odir_model_runner.py
@@ -30,8 +30,8 @@
 
 import odir
 from odir_model_factory import Factory, ModelTypes
-from odir_model_googlenet import GoogleNet
-from odir_model_vggnet import VggNet
+from odir_model_inception_v1 import GoogleNet
+from odir_model_vgg16 import VggNet
 
 os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'
 from tensorflow.keras.layers import Dense, Flatten, Conv2D, Dropout, MaxPooling2D

diff --git a/odir_model_vggnet.py → odir_model_vgg16.py b/odir_model_vggnet.py → odir_model_vgg16.py
@@ -13,36 +13,36 @@
 # limitations under the License.
 # ==============================================================================
 from tensorflow.keras import models, layers
-from tensorflow.keras.optimizers import SGD
 from odir_model_base import ModelBase
 
 
-class VggNet(ModelBase):
+class Vgg16(ModelBase):
 
     def compile(self):
         x = models.Sequential()
 
-
         # Block 1
         x.add(layers.Conv2D(input_shape=self.input_shape, filters=64,kernel_size=(3,3),padding="same", activation="relu"))
-
         x.add(layers.Conv2D(filters=64,kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
 
         # Block 2
         x.add(layers.Conv2D(128, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.Conv2D(128, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
+
         # Block 3
         x.add(layers.Conv2D(256, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.Conv2D(256, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.Conv2D(256, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
+
         # Block 4
         x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.MaxPooling2D((2, 2), strides=(2, 2)))
+
         # Block 5
         x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
         x.add(layers.Conv2D(512, kernel_size=(3,3),padding="same", activation="relu"))
@@ -53,11 +53,17 @@ def compile(self):
         x.add(layers.Dense(4096, activation='relu'))
         x.add(layers.Dense(4096, activation='relu'))
         x.add(layers.Dense(1000, activation='softmax'))
+
+        # Transfer learning, load previous weights
         x.load_weights(r'C:\temp\vgg16_weights_tf_dim_ordering_tf_kernels.h5')
 
+        # Remove last layer
         x.pop()
+
+        # Add new dense layer
         x.add(layers.Dense(8, activation='sigmoid'))
         x.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
         self.show_summary(x)
         self.plot_summary(x, 'model_vggnet.png')
         return x