前提・実現したいこと
kerasで学習させたh5モデルからcoremltoolsを使ったmlmodelへの変換
発生している問題・エラーメッセージ
Traceback (most recent call last): File "converter.py", line 8, in <module> class_labels='labels.txt') File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/coremltools/converters/keras/_keras_converter.py", line 745, in convert custom_conversion_functions=custom_conversion_functions) File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/coremltools/converters/keras/_keras_converter.py", line 543, in convertToSpec custom_objects=custom_objects) File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/coremltools/converters/keras/_keras2_converter.py", line 182, in _convert model = _keras.models.load_model(model, custom_objects = custom_objects) File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/models.py", line 233, in load_model model = model_from_config(model_config, custom_objects=custom_objects) File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/models.py", line 307, in model_from_config return layer_module.deserialize(config, custom_objects=custom_objects) File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/layers/__init__.py", line 54, in deserialize printable_module_name='layer') File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/utils/generic_utils.py", line 139, in deserialize_keras_object list(custom_objects.items()))) File "/anaconda3/envs/tf-200/lib/python3.6/site-packages/keras/models.py", line 1204, in from_config if 'class_name' not in config[0] or config[0]['class_name'] == 'Merge': KeyError: 0
python
1path = './keras_cnn.h5' 2 3import coremltools 4coreml_model = coremltools.converters.keras.convert( 5 path, 6 input_names='image', 7 image_input_names='image', 8 class_labels='labels.txt') 9coreml_model.save('predict.mlmodel')
from keras.models import Sequential from keras.layers import Conv2D, MaxPooling2D from keras.layers import Activation, Dropout, Flatten, Dense from keras.utils import np_utils import keras import numpy as np classes = ["perfect", "normal", "bad"] num_classes = len(classes) image_size = 75 def main(): X_train, X_test, y_train, y_test = np.load("./Judgment.npy") X_train = X_train.astype("float") / 256 X_test = X_test.astype("float") / 256 y_train = np_utils.to_categorical(y_train, num_classes) y_test = np_utils.to_categorical(y_test, num_classes) model = model_train(X_train, y_train) model_eval(model, X_test, y_test) def model_train(X, y): model = Sequential() model.add(Conv2D(32, (3, 3), padding='same', input_shape=X.shape[1:])) model.add(Activation('relu')) model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), padding='same')) model.add(Activation('relu')) model.add(Conv2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(512)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(3)) model.add(Activation('softmax')) opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6) model.compile( loss='categorical_crossentropy', optimizer=opt, metrics=['accuracy']) model.fit(X, y, batch_size=32, epochs=100) model.save('./keras_cnn.h5') return model def model_eval(model, X, y): scores = model.evaluate(X, y, verbose=1) print('Test Loss: ', scores[0]) print('Test Accuracy: ', scores[1]) if __name__ == "__main__": main()
試したこと
python2.7で同様の条件での実行
補足情報(FW/ツールのバージョンなど)
python 3.6
Keras 2.0.6
h5py 2.7.1
tensorflow 1.1.0
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