Anchor explanations for fashion MNIST

import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D, Input
from tensorflow.keras.models import Model
from tensorflow.keras.utils import to_categorical
from alibi.explainers import AnchorImage

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
print('x_train shape:', x_train.shape, 'y_train shape:', y_train.shape)

x_train shape: (60000, 28, 28) y_train shape: (60000,)

idx = 0
plt.imshow(x_train[idx]);

x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
x_train = np.reshape(x_train, x_train.shape + (1,))
x_test = np.reshape(x_test, x_test.shape + (1,))
print('x_train shape:', x_train.shape, 'x_test shape:', x_test.shape)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
print('y_train shape:', y_train.shape, 'y_test shape:', y_test.shape)

x_train shape: (60000, 28, 28, 1) x_test shape: (10000, 28, 28, 1)
y_train shape: (60000, 10) y_test shape: (10000, 10)

def model():
    x_in = Input(shape=(28, 28, 1))
    x = Conv2D(filters=64, kernel_size=2, padding='same', activation='relu')(x_in)
    x = MaxPooling2D(pool_size=2)(x)
    x = Dropout(0.3)(x)
    
    x = Conv2D(filters=32, kernel_size=2, padding='same', activation='relu')(x)
    x = MaxPooling2D(pool_size=2)(x)
    x = Dropout(0.3)(x)
    
    x = Flatten()(x)
    x = Dense(256, activation='relu')(x)
    x = Dropout(0.5)(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.summary()

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 28, 28, 1)]       0         
_________________________________________________________________
conv2d (Conv2D)              (None, 28, 28, 64)        320       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 14, 14, 64)        0         
_________________________________________________________________
dropout (Dropout)            (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 14, 14, 32)        8224      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 7, 7, 32)          0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 7, 7, 32)          0         
_________________________________________________________________
flatten (Flatten)            (None, 1568)              0         
_________________________________________________________________
dense (Dense)                (None, 256)               401664    
_________________________________________________________________
dropout_2 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                2570      
=================================================================
Total params: 412,778
Trainable params: 412,778
Non-trainable params: 0
_________________________________________________________________

cnn.fit(x_train, y_train, batch_size=64, epochs=3)

Train on 60000 samples
Epoch 1/3
60000/60000 [==============================] - 29s 481us/sample - loss: 0.5932 - acc: 0.7819
Epoch 2/3
60000/60000 [==============================] - 33s 542us/sample - loss: 0.4066 - acc: 0.8506
Epoch 3/3
60000/60000 [==============================] - 32s 525us/sample - loss: 0.3624 - acc: 0.8681





<tensorflow.python.keras.callbacks.History at 0x7fae6dd5cb70>

# Evaluate the model on test set
score = cnn.evaluate(x_test, y_test, verbose=0)
print('Test accuracy: ', score[1])

Test accuracy:  0.8867

def superpixel(image, size=(4, 7)):
    segments = np.zeros([image.shape[0], image.shape[1]])
    row_idx, col_idx = np.where(segments == 0)
    for i, j in zip(row_idx, col_idx):
        segments[i, j] = int((image.shape[1]/size[1]) * (i//size[0]) + j//size[1])
    return segments

segments = superpixel(x_train[idx])
plt.imshow(segments);

predict_fn = lambda x: cnn.predict(x)

image_shape = x_train[idx].shape
explainer = AnchorImage(predict_fn, image_shape, segmentation_fn=superpixel)

i = 1
image = x_test[i]
plt.imshow(image[:,:,0]);

cnn.predict(image.reshape(1, 28, 28, 1)).argmax()

2

explanation = explainer.explain(image, threshold=.95, p_sample=.8, seed=0)

plt.imshow(explanation.anchor[:,:,0]);