keras_integration.py

"""
This example demonstrates how to use the active learning interface with Keras.
The example uses the scikit-learn wrappers of Keras. For more info, see https://keras.io/scikit-learn-api/
"""

import keras
import numpy as np
from keras.datasets import mnist
from keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
from keras.models import Sequential
from keras.wrappers.scikit_learn import KerasClassifier
from modAL.models import ActiveLearner


# build function for the Keras' scikit-learn API
def create_keras_model():
    """
    This function compiles and returns a Keras model.
    Should be passed to KerasClassifier in the Keras scikit-learn API.
    """

    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
    model.add(Conv2D(64, (3, 3), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Dropout(0.25))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(10, activation='softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])

    return model


# create the classifier
classifier = KerasClassifier(create_keras_model)

"""
Data wrangling
1. Reading data from Keras
2. Assembling initial training data for ActiveLearner
3. Generating the pool
"""

# read training data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 28, 28, 1).astype('float32') / 255
X_test = X_test.reshape(10000, 28, 28, 1).astype('float32') / 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)

# assemble initial data
n_initial = 1000
initial_idx = np.random.choice(range(len(X_train)), size=n_initial, replace=False)
X_initial = X_train[initial_idx]
y_initial = y_train[initial_idx]

# generate the pool
# remove the initial data from the training dataset
X_pool = np.delete(X_train, initial_idx, axis=0)
y_pool = np.delete(y_train, initial_idx, axis=0)

"""
Training the ActiveLearner
"""

# initialize ActiveLearner
learner = ActiveLearner(
    estimator=classifier,
    X_training=X_initial, y_training=y_initial,
    verbose=1
)

# the active learning loop
n_queries = 10
for idx in range(n_queries):
    query_idx, query_instance = learner.query(X_pool, n_instances=100, verbose=0)
    print(query_idx)
    learner.teach(
        X=X_pool[query_idx], y=y_pool[query_idx], only_new=True,
        verbose=1
    )
    # remove queried instance from pool
    X_pool = np.delete(X_pool, query_idx, axis=0)
    y_pool = np.delete(y_pool, query_idx, axis=0)

# the final accuracy score
print(learner.score(X_test, y_test, verbose=1))