NeuraLint is a toolset for verifying deep learning models using meta-modeling and graph transformations.
This toolset performs verification of deep learning models that are specified using graph transformations by the Groove toolset.
A deep learning program as input must be written using Tensorflow or Keras. First, the program is parsed to extract relevant information according to the meta-model. The model of the program is a graph that conforms to the type graph (meta-model). Then, the graph is verified by Groove as a model checker. The output graph of Groove is used to extract relevant Information for the final report.
groove-x_x_x-bin and DNN-metamodel.gps folders are the Groove toolset and type graph respectively which are needed for running NeuraLint.
Keras_graphs and TF_graphs folders are also used for intermediate calculation and required to run NeuraLint.
The tool is written in Python and it can be easily run in the command line. To use the toolset, please enter following options with running command:
$ python endToEnd.py [name of deep learning programs (.py)] [input size] [output size] [parser type] [name of the output file]
-
[name of deep learning programs (.py)]should be entered with.py -
[input size]and[output size]should be entered as table like[x1, x2, x3, ...] -
[parser type]should betforkeras -
[name of the output file]should be entered without file type
The following code is a sample TensorFlow script:
import tensorflow as tf
import click
import numpy as np
from tensorflow.keras.datasets import mnist
class Dataset:
def __init__(self, X, y, batch_size):
dset = tf.data.Dataset.from_tensor_slices((X, y))
dset = dset.map(self.preprocess_example)
dset = dset.shuffle(10000)
dset = dset.batch(batch_size)
self.dset = dset
self.iterator = self.dset.make_initializable_iterator()
self.next_batch = self.iterator.get_next()
def init(self, sess):
sess.run(self.iterator.initializer)
def preprocess_example(self, *example):
new_image = tf.image.convert_image_dtype(tf.reshape(example[0], [28, 28, 1]), tf.float32)
new_label = tf.cast(tf.one_hot(example[1], 10), tf.float32)
return new_image, new_label
def build_model(input, scope='lenet', reuse=False, training=True):
with tf.variable_scope(scope, reuse=reuse):
conv1 = tf.layers.conv2d(inputs=input,
filters=32,
kernel_size=[15, 15],
padding='valid',
activation=tf.nn.relu)
reg_conv1 = tf.layers.dropout(inputs=conv1, rate=0.35, training=training)
conv1_norm = tf.layers.batch_normalization(reg_conv1, training=training)
pool1 = tf.layers.max_pooling2d(inputs=conv1_norm, pool_size=[4, 4], strides=2)
conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[7, 7],
padding='valid', activation=tf.nn.relu)
reg_conv2 = tf.layers.dropout(inputs=conv2, rate=0.3, training=training)
conv2_norm = tf.layers.batch_normalization(reg_conv2, training=training)
pool2 = tf.layers.max_pooling2d(inputs=conv2_norm, pool_size=[4, 4], strides=2)
pool2_flat = tf.layers.flatten(pool2)
dense = tf.layers.dense(inputs=pool2_flat, units=512, activation=tf.nn.relu)
dense_reg = tf.layers.dropout(inputs=dense, rate=0.25, training=training)
dense_norm = tf.layers.batch_normalization(dense_reg, training=training)
logits = tf.layers.dense(inputs=dense_norm, units=10, activation=None)
return logits
def train(n_epochs, learning_rate, batch_size):
X_train, y_train = mnist.load_data()[0]
X_test, y_test = mnist.load_data()[1]
train_dataset = Dataset(X_train, y_train, batch_size)
test_dataset = Dataset(X_test, y_test, batch_size)
X, y = train_dataset.next_batch
y_pred = build_model(X, scope='lenet', reuse=False, training=True)
train_loss = tf.losses.softmax_cross_entropy(y, y_pred)
train_acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y, 1), tf.argmax(y_pred, 1)), tf.float32))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
optimizer = tf.train.AdamOptimizer(learning_rate)
train_op = optimizer.minimize(train_loss)
X_test, y_test = test_dataset.next_batch
y_pred_test = build_model(X_test, reuse=True, training=False)
test_loss = tf.losses.softmax_cross_entropy(y_test, y_pred_test)
test_acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(y_test, 1),tf.argmax(y_pred_test, 1)), tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(n_epochs):
train_dataset.init(sess)
epoch_errs = []
epoch_accs = []
try:
while True:
_, err, acc = sess.run([train_op, train_loss, train_acc])
epoch_errs.append(err)
epoch_accs.append(acc)
except tf.errors.OutOfRangeError:
print(f"Epoch {epoch}:")
print(f" - Train err: {np.mean(epoch_errs)}")
print(f" - Train acc: {np.mean(epoch_accs)}")
test_dataset.init(sess)
test_errs = []
test_accs = []
try:
while True:
err, acc = sess.run([test_loss, test_acc])
test_errs.append(err)
test_accs.append(acc)
except tf.errors.OutOfRangeError:
epoch_test_err, epoch_test_acc = np.mean(test_errs), np.mean(test_accs)
print(f" - Test err: {epoch_test_err}")
print(f" - Test acc: {epoch_test_acc}")
@click.command()
@click.option('--n-epochs', type=int, default=5)
@click.option('--lr', type=float, default=3e-4)
@click.option('--batch-size', type=int, default=32)
def main(n_epochs, lr, batch_size):
train(n_epochs, lr, batch_size)
if __name__ == '__main__':
main()User can use the following command to parse above-mentioned code using NeuraLint:
$ python endToEnd.py tf_script.py [32,28,28,1] [32,10] tf result
The result of NeuraLint is
tf_script.py
Layer 1 ==> A learning layer should no longer include a bias when it is followed by batchnorm., The local window size for spatial filtering should generally increase or stay the same throughout the convolutional layers.
Layer 3 ==> Batchnorm layer should be before the dropout., Dropout layer must be placed after the pooling layer to be more effective.
Layer 6 ==> A learning layer should no longer include a bias when it is followed by batchnorm., A processing layer should receive sufficient-sized feature space to perform its spatial filtering or pooling.
Layer 8 ==> Batchnorm layer should be before the dropout., Dropout layer must be placed after the pooling layer to be more effective.
Layer 10 ==> A processing layer should receive sufficient-sized feature space to perform its spatial filtering or pooling.
Layer 12 ==> A learning layer should no longer include a bias when it is followed by batchnorm.
Layer 14 ==> Batchnorm layer should be before the dropout.
The following code is a sample Keras script:
from k.layers.convolutional import *
from k.layers import *
from k.layers.core import *
model = Sequential()
model.add(Conv1D(32,2, input_shape = (32, 1)))
model.add(Activation('relu'))
model.add(Conv1D(32,2))
model.add(Activation('relu'))
model.add(Conv1D(32,2))
model.add(Activation('relu'))
model.add(Conv1D(32,2))
model.add(Activation('relu'))
model.add(Dense(32))
model.add(Activation('sigmoid'))
model.compile(loss = 'binary_crossentropy',
optimizer = 'rmsprop',
metrics=['accuracy'])
model.fit(inputData,labelData)Again, user can use the following command to parse mentioned code using NeuraLint.
$ python endToEnd.py keras_script.py [32,32,1] [32,1] keras result
The result of NeuraLint is:
keras_script.py
Learner ==> The loss should be correctly defined and connected to the layer in accordance with its input conditions (i.e.shape and type)-post_activation
Layer 7 ==> A processing layer that operates on a N-dimensional tensors, should receive a valid input tensor with exactly N-dimensional shape(missing flatten ).
Moreover, it can be called inside a DL program as a Python library on the top of TensorFlow/Keras. The developer can import NeuraLint as a Python library and simply call it in his own code by feeding the DL model and receiving the analysis report. For example:
import neuraLint
...
report = neuraLint.check(model)
print(report)
This library is in the Python Library folder. Please note that you should place groove-x_x_x-bin and DNN-metamodel.gps folders in its root prior to using it. An example is provided in main-sample.py.