deterministic_cnn.py

from __future__ import print_function
from keras.datasets import mnist
from keras.models import Sequential
from keras.utils import np_utils, generic_utils
from six.moves import range
import numpy as np
import scipy as sp
from keras import backend as K  
import random
import scipy.io
from scipy.stats import mode
# custom module for cnn model
from cnn_architecture1 import *

batch_size = 128
nb_classes = 10
#use a large number of epochs
nb_epoch = 50
# input image dimensions
img_rows, img_cols = 28, 28
input_shape = (img_rows, img_cols, 1)

acquisition_iterations = 98
num_of_queries = 10

# the data, shuffled and split between tran and test sets
(X_train_All, y_train_All), (X_test, y_test) = mnist.load_data()

X_train_All = X_train_All.reshape(X_train_All.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)

random_split = np.asarray(random.sample(range(0, X_train_All.shape[0]), X_train_All.shape[0]))

X_train_All = X_train_All[random_split]
y_train_All = y_train_All[random_split]
X_Pool = X_train_All[20000:60000]
y_Pool = y_train_All[20000:60000]

X_train_All = X_train_All[0:10000]
y_train_All = y_train_All[0:10000]

X_train, y_train = np.array([], dtype=np.int64), np.array([], dtype=np.int64)

for index in range(10):
    idx = np.array(np.where(y_train_All==index)).T
    idx = idx[0:2, 0]
    X = X_train_All[idx, :, :, :]
    y = y_train_All[idx]
    
    X_train = np.concatenate((X_train, X), axis=0 ) if X_train.size else X
    y_train = np.concatenate((y_train, y), axis=0 ) if y_train.size else y


print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')

print('Distribution of Training Classes:', np.bincount(y_train))

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_Pool = X_Pool.astype('float32')
X_train /= 255
X_Pool /= 255
X_test /= 255

Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
Y_Pool = np_utils.to_categorical(y_Pool, nb_classes)

model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch, verbose=0)

print('Evaluating Test Accuracy Without Acquisition')
score, acc = model.evaluate(X_test, Y_test, verbose=0)
all_accuracy = acc

print('Starting Active Learning in Experiment ')

# Deterministic CNN
for i in range(acquisition_iterations):
    print('ACQUISITION ITERATION ' + str(i+1) + ' of ' + str(acquisition_iterations))

    pool_subset_count = 2000
    pool_subset_random_index = np.asarray(random.sample(range(0, X_Pool.shape[0]), pool_subset_count))
    X_Pool_subset = X_Pool[pool_subset_random_index]
    y_Pool_subset = y_Pool[pool_subset_random_index]

    print('Search over Pool of Unlabeled Data')
    
    # Var ratio active learning acquisition function
    D_probs = model.predict_proba(X_Pool_subset)  
    acquired_index = np.argsort(np.max(D_probs, axis=1))[:num_of_queries]
    acquired_X = X_Pool_subset[acquired_index]
    acquired_Y = y_Pool_subset[acquired_index]	

    # Remove the acquired data from the unlabeled Pool
    X_Pool = np.delete(X_Pool, (pool_subset_random_index[acquired_index]), axis=0)
    y_Pool = np.delete(y_Pool, (pool_subset_random_index[acquired_index]), axis=0)

    print('Acquired Points added to the training set')
    X_train = np.concatenate((X_train, acquired_X), axis=0)
    y_train = np.concatenate((y_train, acquired_Y), axis=0)
    print('Train Data size: ' + str(X_train.shape))  
    print('Unlabeled Pool size: ' + str(X_Pool.shape))

    print('Train Again with the added points')

    # convert class vectors to binary class matrices
    Y_train = np_utils.to_categorical(y_train, nb_classes)

    model.fit(X_train, Y_train, batch_size=batch_size, epochs=nb_epoch, verbose=0)
  
    print('Evaluate Model Test Accuracy after training')
    score, acc = model.evaluate(X_test, Y_test, verbose=0)
    # print('Test score:', score)
    print('Test accuracy:', acc)
    all_accuracy = np.append(all_accuracy, acc)
    print()


print('Storing Accuracy Values over experiments')
np.save('test_acc.npy', all_accuracy)