CODEWITHSUNDEEP
tensorflowkerascoreml
Axel
Axel

Reputation: 483

CoreMLtools and Keras ValueError: need more than 1 value to unpack

I'm fine-tuning the Inception V3 model with Keras, in order to convert it with coremltools into a .mlmodel file.

However, when converting the model coremltools throws an error saying the following when the converter reaches the last layer of the model:

coremltools/models/neural_network.py", line 2501, in set_pre_processing_parameters
channels, height, width = array_shape

ValueError: need more than 1 value to unpack

I used the code from the Keras documentation on applications found here: https://keras.io/applications/#fine-tune-inceptionv3-on-a-new-set-of-classes

And added a piece of code loading my dataset from the VGG example found here: https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html

My final script looks like this, using TesorFlow as backend:

LOAD THE DATA 
from keras.preprocessing.image import ImageDataGenerator

img_width, img_height = 299, 299

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 358
nb_validation_samples = 21
epochs = 1
batch_size = 15

train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical')
TRAIN THE MODEL 
base_model = InceptionV3(weights='imagenet', include_top=False)

x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(7, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)

for layer in base_model.layers:
    layer.trainable = False

model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

for i, layer in enumerate(base_model.layers):
   print(i, layer.name)

for layer in model.layers[:249]:
   layer.trainable = False
for layer in model.layers[249:]:
   layer.trainable = True

from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

model.save('finetuned_inception.h5')

Upvotes: 2

Views: 442

Answers (1)

TMob
TMob

Reputation: 1278

I'm writing here in response to @SwimBikeRun's request (as I need a bit more space) I was converting YOLO to Keras and then Keras to CoreML. For conversion I was using this script https://github.com/qqwweee/keras-yolo3/blob/master/convert.py

In the conversion-process the model was eventually created like that:

input_layer = Input(shape=(None, None, 3))
...
model = Model(inputs=input_layer, outputs=[all_layers[i] for i in out_index])

And those "None"-inputs was what made CoreML conversion fail. For CoreML the input-size to your model must be known. So I changed it to this:

 input_layer = Input(shape=(416, 416, 3)

Your input-size will probably vary.

For your original question: Maybe check your base_model.input size for the same problem.

Upvotes: 2

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