def test_anchor_image():
# load and prepare fashion MNIST data
(x_train, y_train), (_, _) = keras.datasets.fashion_mnist.load_data()
x_train = x_train.astype('float32') / 255
x_train = np.reshape(x_train, x_train.shape + (1,))
y_train = to_categorical(y_train)
# define and train model
def model():
x_in = Input(shape=(28, 28, 1))
x = Conv2D(filters=8, kernel_size=2, padding='same', activation='relu')(x_in)
x = MaxPooling2D(pool_size=2)(x)
x = Dropout(0.3)(x)
x = Flatten()(x)
x_out = Dense(10, activation='softmax')(x)
cnn = Model(inputs=x_in, outputs=x_out)
cnn.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return cnn
cnn = model()
cnn.fit(x_train, y_train, batch_size=256, epochs=1)
# test explainer initialization
predict_fn = lambda x: cnn.predict(x)
segmentation_fn = 'slic'
segmentation_kwargs = {'n_segments': 10, 'compactness': 10, 'sigma': .5}
image_shape = (28, 28, 1)
explainer = AnchorImage(predict_fn, image_shape, segmentation_fn=segmentation_fn,
segmentation_kwargs=segmentation_kwargs)
assert explainer.predict_fn(np.zeros((1,) + image_shape)).shape == (1,)
# test sampling and segmentation functions
image = x_train[0]
segments, sample_fn = explainer.get_sample_fn(image, p_sample=.5)
raw_data, data, labels = sample_fn([], 10)
assert raw_data.shape == data.shape
assert data.shape[0] == labels.shape[0]
assert data.shape[1] == len(np.unique(segments))
# test explanation
threshold = .95
explanation = explainer.explain(image, threshold=threshold)
assert explanation['anchor'].shape == image_shape
assert explanation['precision'] >= threshold
assert len(np.unique(explanation['segments'])) == len(np.unique(segments))
from tensorflow.keras.applications.mobilenet import MobileNet
from alibi.datasets import fetch_imagenet
from alibi.explainers import AnchorImage
import dill
import alibi
# Be careful when you're using Jupyter Kernel
# Better to use Docker Container Environment
print(alibi.__version__)
print(dill.__version__)
model = MobileNet(weights='imagenet')
predict_fn = lambda x: model.predict(x)
segmentation_fn = 'slic'
kwargs = {'n_segments': 15, 'compactness': 20, 'sigma': .5}
image_shape = (224, 224, 3)
explainer = AnchorImage(predict_fn,
image_shape,
segmentation_fn=segmentation_fn,
segmentation_kwargs=kwargs,
images_background=None)
explainer.predict_fn = None # Clear explainer predict_fn as its a lambda and will be reset when loaded
with open("explainer.dill", 'wb') as f:
dill.dump(explainer, f)
image_shape,
segmentation_fn='slic',
segmentation_kwargs=kwargs,
images_background=None,
)
categories = ['Persian cat', 'volcano', 'strawberry', 'jellyfish', 'centipede']
full_data = []
full_labels = []
for category in categories:
data, labels = fetch_imagenet(
category,
nb_images=10,
target_size=image_shape[:2],
seed=0,
return_X_y=True,
)
full_data.append(data)
full_labels.append(labels)
full_data = np.concatenate(full_data)
full_labels = np.concatenate(full_labels)
data.shape
explainer.fit(fetch_imagenet)
# Clear explainer predict_fn as its a lambda and will be reset when loaded
explainer.predict_fn = None
with open("./mobilenet_explainer.dill", 'wb') as f:
dill.dump(explainer, f)