fashion-mnist.py

from keras.layers import Input, Dense
from keras.models import Model
import matplotlib.pyplot as plt 
import numpy as np 
from sklearn.neighbors import LSHForest
import matplotlib.pyplot as plt
from keras.datasets import fashion_mnist


# Autoencoder model definition
input_img = Input(shape=(784,))
encoded = Dense(128, activation='relu')(input_img)
encoded = Dense(64, activation='relu')(encoded)
encoded = Dense(32, activation='relu', name='encoded')(encoded)

decoded = Dense(64, activation='relu')(encoded)
decoded = Dense(128, activation='relu')(decoded)
decoded = Dense(784, activation='sigmoid')(decoded)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

# Load fashion MNIST dataset
(x_train, _), (x_test, _) = fashion_mnist.load_data()

x_train = x_train.astype('float32') / 255.
x_test = x_test.astype('float32') / 255.
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))

# Train autoencoder on data
autoencoder.fit(x_train, x_train,
                epochs=1,
                batch_size=128,
                shuffle=True,
                validation_data=(x_test, x_test),
                callbacks=[])

# Define Enocder as new model
layer_name = 'encoded'
encoder = Model(inputs=autoencoder.input,
                                 outputs=autoencoder.get_layer(layer_name).output)

# Generate feature vectors of test dataset
x_test_encoded = encoder.predict(x_test)

x_test = np.reshape(x_test, [-1, 28, 28])

# create Local Sensitivity hashing instance for fast neighborhood search
lshf = LSHForest(random_state=42)  
lshf.fit(x_test_encoded)  

# Random index of query image from test set
random_query = np.random.randint(0, 1000)

query_features = np.expand_dims(x_test_encoded[random_query, :], axis=0)


distances, indices = lshf.kneighbors(query_features, n_neighbors=5)


plt.imshow(x_test[random_query, :, :])
plt.title('Query image')
plt.gray()
plt.show()

for i in range(1, 5):
    ax = plt.subplot(1, 4, i)
    plt.imshow(x_test[indices[0][i], :, :])
    plt.gray()
    plt.title('Distance = ' + str(distances[0][i]))
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()