From 03050adb2763123af9bbc6e3ba1b7a7b9795591a Mon Sep 17 00:00:00 2001
From: Nicolas Papernot <papernot@google.com>
Date: Tue, 26 Mar 2019 14:16:06 -0700
Subject: [PATCH] adding scratch for walkthrough blog post

PiperOrigin-RevId: 240425310
---
 tutorials/walkthrough/mnist_scratch.py | 134 +++++++++++++++++++++++++
 1 file changed, 134 insertions(+)
 create mode 100644 tutorials/walkthrough/mnist_scratch.py

diff --git a/tutorials/walkthrough/mnist_scratch.py b/tutorials/walkthrough/mnist_scratch.py
new file mode 100644
index 0000000..aa74ea6
--- /dev/null
+++ b/tutorials/walkthrough/mnist_scratch.py
@@ -0,0 +1,134 @@
+# 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 __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+import tensorflow as tf
+
+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.train.GradientDescentOptimizer(FLAGS.learning_rate)
+    opt_loss = scalar_loss
+    global_step = tf.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):
+  tf.logging.set_verbosity(tf.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.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.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()