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"""
nystrom_vs_deepstrom: Compute accuracy efficiency of the nystrom method vs deepstrom.
Usage:
nystrom_vs_deepstrom (--nystroem | --deepstrom) [-e numepoch -s batchsize -G gammavalue -m subsamplesize]
nystrom_vs_deepstrom -h | --help
Options:
-h --help Show this screen.
--nystroem Run the nystroem version.
--deepstrom Run the deepstrom version.
-G gammavalue --gamma-nystrom=gammavalue The gamma value used in nystrom.
-m subsamplesize --subsample-size-nystrom=subsamplesize The subsample size for nystrom.
-e numepoch --num-epoch=numepoch The number of epoch. [default: 1]
-s batchsize --batch-size=batchsize The number of example in each batch [default: 50]
"""
import tensorflow as tf
import numpy as np
import skluc.mldatasets as dataset
from sklearn.kernel_approximation import Nystroem
from sklearn.svm import SVC
from skluc.tensorflow.kernel_approximation.nystrom_approx import nystrom_layer
from skluc.tensorflow.utils import batch_generator, classification_mnist
tf.logging.set_verbosity(tf.logging.ERROR)
import docopt
def deepstrom_classif(X_train,
Y_train,
X_nystrom,
batch_size,
num_epoch,
dataset_cycling,
gamma,
data_shape,
output_dim,
output_nystrom_layer,
X_test=None,
Y_test=None):
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with tf.Graph().as_default():
x = tf.placeholder(tf.float32, shape=[None, *data_shape], name="x")
y_ = tf.placeholder(tf.float32, shape=[None, output_dim], name="labels")
x_nystrom = tf.Variable(X_nystrom, name="nystrom_subsample", trainable=False)
out_fc = nystrom_layer(x, x_nystrom, gamma, output_dim=output_nystrom_layer)
y_conv, keep_prob = classification_mnist(out_fc, output_dim=output_dim)
# # calcul de la loss
with tf.name_scope("xent"):
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv, name="xentropy"),
name="xentropy_mean")
# # calcul du gradient
with tf.name_scope("train"):
global_step = tf.Variable(0, name="global_step", trainable=False)
train_optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cross_entropy,
global_step=global_step)
# # calcul de l'accuracy
with tf.name_scope("accuracy"):
predictions = tf.argmax(y_conv, 1)
correct_prediction = tf.equal(predictions, tf.argmax(y_, 1))
accuracy_op = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()
# Create a session for running Ops on the Graph.
with tf.Session() as sess:
# Initialize all Variable objects
sess.run(init)
# actual learning
for i in range(num_epoch):
for X_batch, Y_batch in batch_generator(X_train, Y_train, batch_size, dataset_cycling):
feed_dict = {x: X_batch, y_: Y_batch, keep_prob: 0.5}
sess.run([train_optimizer, cross_entropy], feed_dict=feed_dict)
accuracy = None
if X_test is not None and Y_test is not None:
# testing or predicting may not be wanted
accuracy = sess.run([accuracy_op], feed_dict={
x: X_test, y_: Y_test, keep_prob: 1.0})
lst_output = [str(accuracy[0]), str(x_nystrom.shape[0]), str(gamma), str(batch_size), str(num_epoch)]
print(",".join(lst_output))
def nystroem_classif(X_train, Y_train, X_test, Y_test, subsample, gamma):
nys = Nystroem(kernel="rbf", gamma=gamma, n_components=len(subsample))
nys.fit(subsample)
X_train_transformed = nys.transform(X_train)
X_test_transformed = nys.transform(X_test)
clf = SVC(kernel="linear")
clf.fit(X_train_transformed, Y_train)
score = clf.score(X_test_transformed, Y_test)
lst_output = [str(score), str(len(subsample)), str(gamma)]
print(",".join(lst_output))
if __name__ == "__main__":
arguments = docopt.docopt(__doc__)
SUBSAMPLE_SIZE = int(arguments["--subsample-size-nystrom"])
gamma = float(arguments["--gamma-nystrom"])
nystroem = arguments["--nystroem"]
deepstrom = arguments["--deepstrom"]
num_epoch = int(float(arguments["--num-epoch"]))
batch_size = int(arguments["--batch-size"])
mnist = dataset.MnistDataset()
mnist.load()
mnist.normalize()
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indexes_nystrom = np.random.permutation(60000)[:SUBSAMPLE_SIZE]
if nystroem:
X_train, Y_train = mnist.train
X_test, Y_test = mnist.test
X_subsample = X_train[indexes_nystrom]
nystroem_classif(X_train=X_train,
Y_train=Y_train,
X_test=X_test,
Y_test=Y_test,
subsample=X_subsample,
gamma=gamma)
elif deepstrom:
mnist.to_one_hot()
mnist.data_astype(np.float32)
mnist.labels_astype(np.float32)
X_train, Y_train = mnist.train
X_test, Y_test = mnist.test
X_subsample = X_train[indexes_nystrom]
deepstrom_classif(X_train=X_train,
Y_train=Y_train,
X_test=X_test,
Y_test=Y_test,
gamma=gamma,
data_shape=X_train.shape[1:],
output_dim=Y_train.shape[1],
dataset_cycling=False,
num_epoch=num_epoch,
output_nystrom_layer=SUBSAMPLE_SIZE,
X_nystrom=X_subsample,
batch_size=batch_size)