coremltools error

transfer_learning.py

from keras.engine import Model
from keras.layers import GlobalAveragePooling2D, Dense
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.applications import InceptionV3
from keras.applications.inception_v3 import preprocess_input
from coremltools.converters.keras import convert
from keras.models import load_model

train_data_dir = "./../data/train"
validation_data_dir = "./../data/validation"
img_width, img_height = 256, 256
batch_size = 16
NB_IV3_LAYERS_TO_FREEZE = 2

train_datagen =  ImageDataGenerator(
    preprocessing_function=preprocess_input,
    rotation_range=30,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)
test_datagen = ImageDataGenerator(
    preprocessing_function=preprocess_input,
    rotation_range=30,
    width_shift_range=0.2,
    height_shift_range=0.2,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)
train_generator = train_datagen.flow_from_directory(
    train_data_dir,
  target_size=(img_width, img_height),
  batch_size=batch_size,
)
validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
  target_size=(img_width, img_height),
  batch_size=batch_size,
)


def add_new_last_layer(base_model, nb_classes):
  """Add last layer to the convnet
  Args:
    base_model: keras model excluding top
    nb_classes: # of classes
  Returns:
    new keras model with last layer
  """
  x = base_model.output
  x = GlobalAveragePooling2D()(x)
  x = Dense(1024, activation='relu')(x)
  predictions = Dense(nb_classes, activation='softmax')(x)
  model = Model(input=base_model.input, output=predictions)
  return model

def setup_to_transfer_learn(model, base_model):
  """Freeze all layers and compile the model"""
  for layer in base_model.layers:
    layer.trainable = False
  model.compile(optimizer='rmsprop',
                loss='categorical_crossentropy',
                metrics=['accuracy'])

def setup_to_finetune(model):
   """Freeze the bottom NB_IV3_LAYERS and retrain the remaining top
      layers.
   note: NB_IV3_LAYERS corresponds to the top 2 inception blocks in
         the inceptionv3 architecture
   Args:
     model: keras model
   """
   for layer in model.layers[:NB_IV3_LAYERS_TO_FREEZE]:
      layer.trainable = False
   for layer in model.layers[NB_IV3_LAYERS_TO_FREEZE:]:
      layer.trainable = True
   model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
                 loss='binary_crossentropy')

def saveToCoreml(model):
    coreml_model = convert(model,image_input_names="input_1")
    coreml_model.save('image_classifier.mlmodel')

if __name__ == '__main__':
    base_model = InceptionV3(weights='imagenet', include_top=False)
    model = add_new_last_layer(base_model,2)
    setup_to_transfer_learn(model,base_model)
    print model.summary()
    nb_epoch = 2
    model.fit(
        train_generator,
        samples_per_epoch=128,
        nb_epoch=nb_epoch,
        validation_data=validation_generator,
        nb_val_samples=64,
        class_weight='auto')
    saveToCoreml(model)