forked from 626_privacy/tensorflow_privacy
123 lines
4.4 KiB
Python
123 lines
4.4 KiB
Python
# Copyright 2019, The TensorFlow Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Scratchpad for training a CNN on MNIST with DPSGD."""
|
|
|
|
from absl import logging
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
from tensorflow import estimator as tf_estimator
|
|
from tensorflow.compat.v1 import estimator as tf_compat_v1_estimator
|
|
|
|
tf.flags.DEFINE_float('learning_rate', .15, 'Learning rate for training')
|
|
tf.flags.DEFINE_integer('batch_size', 256, 'Batch size')
|
|
tf.flags.DEFINE_integer('epochs', 15, 'Number of epochs')
|
|
|
|
FLAGS = tf.flags.FLAGS
|
|
|
|
|
|
def cnn_model_fn(features, labels, mode):
|
|
"""Model function for a CNN."""
|
|
|
|
# Define CNN architecture using tf.keras.layers.
|
|
input_layer = tf.reshape(features['x'], [-1, 28, 28, 1])
|
|
y = tf.keras.layers.Conv2D(
|
|
16, 8, strides=2, padding='same', activation='relu').apply(input_layer)
|
|
y = tf.keras.layers.MaxPool2D(2, 1).apply(y)
|
|
y = tf.keras.layers.Conv2D(
|
|
32, 4, strides=2, padding='valid', activation='relu').apply(y)
|
|
y = tf.keras.layers.MaxPool2D(2, 1).apply(y)
|
|
y = tf.keras.layers.Flatten().apply(y)
|
|
y = tf.keras.layers.Dense(32, activation='relu').apply(y)
|
|
logits = tf.keras.layers.Dense(10).apply(y)
|
|
|
|
# Calculate loss as a vector and as its average across minibatch.
|
|
vector_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
|
|
labels=labels, logits=logits)
|
|
scalar_loss = tf.reduce_mean(vector_loss)
|
|
|
|
# Configure the training op (for TRAIN mode).
|
|
if mode == tf_estimator.ModeKeys.TRAIN:
|
|
optimizer = tf.compat.v1.train.GradientDescentOptimizer(FLAGS.learning_rate)
|
|
opt_loss = scalar_loss
|
|
global_step = tf.compat.v1.train.get_global_step()
|
|
train_op = optimizer.minimize(loss=opt_loss, global_step=global_step)
|
|
return tf_estimator.EstimatorSpec(
|
|
mode=mode, loss=scalar_loss, train_op=train_op)
|
|
|
|
# Add evaluation metrics (for EVAL mode).
|
|
elif mode == tf_estimator.ModeKeys.EVAL:
|
|
eval_metric_ops = {
|
|
'accuracy':
|
|
tf.metrics.accuracy(
|
|
labels=labels, predictions=tf.argmax(input=logits, axis=1))
|
|
}
|
|
return tf_estimator.EstimatorSpec(
|
|
mode=mode, loss=scalar_loss, eval_metric_ops=eval_metric_ops)
|
|
|
|
|
|
def load_mnist():
|
|
"""Loads MNIST and preprocesses to combine training and validation data."""
|
|
train, test = tf.keras.datasets.mnist.load_data()
|
|
train_data, train_labels = train
|
|
test_data, test_labels = test
|
|
|
|
train_data = np.array(train_data, dtype=np.float32) / 255
|
|
test_data = np.array(test_data, dtype=np.float32) / 255
|
|
|
|
train_labels = np.array(train_labels, dtype=np.int32)
|
|
test_labels = np.array(test_labels, dtype=np.int32)
|
|
|
|
assert train_data.min() == 0.
|
|
assert train_data.max() == 1.
|
|
assert test_data.min() == 0.
|
|
assert test_data.max() == 1.
|
|
assert train_labels.ndim == 1
|
|
assert test_labels.ndim == 1
|
|
|
|
return train_data, train_labels, test_data, test_labels
|
|
|
|
|
|
def main(unused_argv):
|
|
logging.set_verbosity(logging.INFO)
|
|
|
|
# Load training and test data.
|
|
train_data, train_labels, test_data, test_labels = load_mnist()
|
|
|
|
# Instantiate the tf.Estimator.
|
|
mnist_classifier = tf_estimator.Estimator(model_fn=cnn_model_fn)
|
|
|
|
# Create tf.Estimator input functions for the training and test data.
|
|
train_input_fn = tf_compat_v1_estimator.inputs.numpy_input_fn(
|
|
x={'x': train_data},
|
|
y=train_labels,
|
|
batch_size=FLAGS.batch_size,
|
|
num_epochs=FLAGS.epochs,
|
|
shuffle=True)
|
|
eval_input_fn = tf_compat_v1_estimator.inputs.numpy_input_fn(
|
|
x={'x': test_data}, y=test_labels, num_epochs=1, shuffle=False)
|
|
|
|
# Training loop.
|
|
steps_per_epoch = 60000 // FLAGS.batch_size
|
|
for epoch in range(1, FLAGS.epochs + 1):
|
|
# Train the model for one epoch.
|
|
mnist_classifier.train(input_fn=train_input_fn, steps=steps_per_epoch)
|
|
|
|
# Evaluate the model and print results
|
|
eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn)
|
|
test_accuracy = eval_results['accuracy']
|
|
print('Test accuracy after %d epochs is: %.3f' % (epoch, test_accuracy))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.app.run()
|