fix 'Unsupported' object has no attribute 'info'

examples/build_test_model.py

@@ -4,26 +4,32 @@
 from tensorflow.keras.optimizers import Adam
 from utils.BASEDIR import BASEDIR
 
+
+def one_hot(idx):
+  x = [0 for _ in range(3)]
+  x[idx] = 1
+  return x
+
+
 samples = 10000
-x_train = (np.random.rand(samples, 1) * 10)
-# y_train = x_train.take(axis=1, indices=1) * 2
-y_train = ((x_train * 1.5) + 2.5) / 2
+x_train = (np.random.rand(samples, 3) * 10)
+y_train = np.array([one_hot(np.argmax(sample)) for sample in x_train])
 
 model = Sequential()
-model.add(Dense(256, activation='relu', input_shape=x_train.shape[1:]))
+model.add(Dense(32, activation='relu', input_shape=x_train.shape[1:]))
+model.add(Dropout(0.2))
 model.add(BatchNormalization())
-model.add(Dense(128, activation='relu'))
-# model.add(BatchNormalization())
-model.add(Dense(64, activation='relu'))
-# model.add(BatchNormalization())
-model.add(Dense(1, activation='linear'))
 
-model.compile(optimizer=Adam(lr=0.001, amsgrad=True), loss='mse')
-model.fit(x_train, y_train, batch_size=64, epochs=10, verbose=1, validation_split=0.2)
+model.add(Dense(16, activation='relu'))
+model.add(Dropout(0.2))
+model.add(BatchNormalization())
+
+model.add(Dense(3, activation='softmax'))
+
+model.compile(optimizer=Adam(lr=0.003, amsgrad=True), loss='categorical_crossentropy')
+model.fit(x_train, y_train, batch_size=64, epochs=20, verbose=1, validation_split=0.2)
 
 model.save('{}/examples/batch_norm.h5'.format(BASEDIR))
-print(model.predict([[4.5]]))
+print(model.predict([[4.5, 4.5, 9]]).tolist())
 print('Saved!')
-print(model.layers[0].get_weights()[0].shape)
-print(model.layers[1].get_weights()[0].shape)
 # exit()

examples/load.py

@@ -3,7 +3,7 @@
 from utils.BASEDIR import BASEDIR
 
 model = load_model('{}/examples/batch_norm.h5'.format(BASEDIR))
-print(model.predict([[0.5]]))
+print(model.predict([[[4.5, 4.5]]]).tolist())
 
 
 # exit()

konverter/utils/konverter_support.py

@@ -59,26 +59,26 @@ def attr_map(self, classes, attr):
   def get_model_info(self, model):
     name = getattr(model, '_keras_api_names_v1')[0]
     model_class = self.get_class_from_name(name, 'models')
+    model_class.info = BaseModelInfo()
     if not model_class:
       model_class = Models.Unsupported()
       model_class.name = name
-      return model_class
+    else:
+      model_class.info.supported = True
+      model_class.info.input_shape = model.input_shape
 
-    model_class.info = BaseModelInfo()
-    model_class.info.input_shape = model.input_shape
-    model_class.info.supported = True
     return model_class
 
   def get_layer_info(self, layer):
     name = getattr(layer, '_keras_api_names_v1')
     if not len(name):
       name = getattr(layer, '_keras_api_names')
     layer_class = self.get_class_from_name(name[0], 'layers')  # assume only one name
+    layer_class.info = BaseLayerInfo()
     if not layer_class:
       layer_class = Layers.Unsupported()  # add activation below to raise exception with
       layer_class.name = name
 
-    layer_class.info = BaseLayerInfo()
     layer_class.info.is_ignored = layer_class.name in self.ignored_layers
 
     is_linear = False

misc/old/konverter/run.py

@@ -5,6 +5,6 @@
 
 os.chdir(BASEDIR)
 
-model = load_model('examples/batch_norm.h5')
+model = load_model('examples/latest_maybe_good.h5')
 kon = Konverter()
-kon.konvert(model, 'examples/batch_norm.py', 2, verbose=True)
+kon.konvert(model, 'examples/latest_maybe_good.py', 2, verbose=True)