dipanjanS

# load and pre-process cat image
IMAGE_PATH = './cat2.jpg'
img = tf.keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(299, 299))
img = tf.keras.preprocessing.image.img_to_array(img)

# view the image
plt.imshow(img/255.)

dipanjanS

# load dependencies
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tf_explain.core.activations import ExtractActivations
from tensorflow.keras.applications.xception import decode_predictions

%matplotlib inline

# load Xception pre-trained CNN model

dipanjanS

# focus on 14th layer of the CNN model
print(model.layers[14].input)
Out [49]: <tf.Tensor 'block4_conv3_2/Relu:0' shape=(?, 28, 28, 512) dtype=float32>

# make model predictions
e = shap.GradientExplainer((model.layers[14].input, model.layers[-1].output), 
                            map2layer(preprocess_input(X.copy()), 14))
shap_values, indexes = e.shap_values(map2layer(to_predict, 14), ranked_outputs=2)
index_names = np.vectorize(lambda x: class_names[str(x)][1])(indexes)

dipanjanS

# utility function to pass inputs to specific model layers
def map2layer(x, layer):
    feed_dict = dict(zip([model.layers[0].input], [preprocess_input(x.copy())]))
    return K.get_session().run(model.layers[layer].input, feed_dict)
    
# focus on the 7th layer of CNN model
print(model.layers[7].input)
Out [46]: <tf.Tensor 'block2_pool_2/MaxPool:0' shape=(?, 56, 56, 128) dtype=float32>

# make model predictions

dipanjanS

# get imagenet id to label name mappings
url = "https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json"
fname = shap.datasets.cache(url)
with open(fname) as f:
    class_names = json.load(f)
    
# make model predictions
predictions = model.predict(preprocess_input(to_predict.copy()))

# get prediction labels

dipanjanS

# load sample images
X, y = shap.datasets.imagenet50()

# load sample cat image for test
IMAGE_PATH = './cat2.jpg'
img = keras.preprocessing.image.load_img(IMAGE_PATH, target_size=(224, 224))
img = keras.preprocessing.image.img_to_array(img)

# select 3 other sample images for test
import matplotlib.pyplot as plt

dipanjanS

model = VGG16(weights='imagenet', include_top=True)
model.summary()

# output
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_3 (InputLayer)         (None, 224, 224, 3)       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      

dipanjanS

import keras
from keras.applications.vgg16 import VGG16
from keras.applications.vgg16 import preprocess_input, decode_predictions
from matplotlib.colors import LinearSegmentedColormap
import numpy as np
import shap
import keras.backend as K
import json

shap.initjs()

dipanjanS

hourly_avg_errors_sorted_df = (not_found_df
                                   .groupBy(F.hour('time')
                                             .alias('hour'))
                                   .count()
                                   .sort('hour'))
hourly_avg_errors_sorted_pd_df = hourly_avg_errors_sorted_df.toPandas()

c = sns.catplot(x='hour', y='count', 
                data=hourly_avg_errors_sorted_pd_df, 
                kind='bar', height=5, aspect=1.5)

dipanjanS

(errors_by_date_sorted_df
    .sort("count", ascending=False)
    .show(3))