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Diffstat (limited to 'model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py')
-rw-r--r-- | model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py | 95 |
1 files changed, 95 insertions, 0 deletions
diff --git a/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py b/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py new file mode 100644 index 00000000000..bc6ea13ebc1 --- /dev/null +++ b/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py @@ -0,0 +1,95 @@ +# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root. + +import tensorflow as tf + +from functools import partial +from tensorflow.examples.tutorials.mnist import input_data +from datetime import datetime + +now = datetime.utcnow().strftime("%Y%m%d%H%M%S") +root_logdir = "tf_logs" +logdir = "{}/run-{}/".format(root_logdir, now) + +mnist = input_data.read_data_sets("/tmp/data/") +X_train = mnist.train.images +X_test = mnist.test.images +y_train = mnist.train.labels.astype("int") +y_test = mnist.test.labels.astype("int") + +n_inputs = 28*28 # MNIST +n_hidden1 = 300 +n_hidden2 = 100 +n_hidden3 = 40 +n_outputs = 10 + +learning_rate = 0.01 +n_epochs = 20 +batch_size = 200 +batch_norm_momentum = 0.9 + +X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X") +y = tf.placeholder(tf.int64, shape=(None), name="y") +training = tf.placeholder_with_default(False, shape=(), name='training') + +def leaky_relu(z, name=None): + return tf.maximum(0.01 * z, z, name=name) + +with tf.name_scope("dnn"): + he_init = tf.contrib.layers.variance_scaling_initializer() + + batch_norm_layer = partial(tf.layers.batch_normalization, training=training, momentum=batch_norm_momentum) + dense_layer = partial(tf.layers.dense, kernel_initializer=he_init) + + hidden1 = dense_layer(X, n_hidden1, name="hidden1", activation=leaky_relu) + bn1 = tf.nn.elu(batch_norm_layer(hidden1)) + hidden2 = dense_layer(bn1, n_hidden2, name="hidden2", activation=tf.nn.elu) + bn2 = tf.nn.elu(batch_norm_layer(hidden2)) + logits_before_bn = dense_layer(bn2, n_outputs, name="outputs", activation=tf.nn.selu) + logits = batch_norm_layer(logits_before_bn) + +with tf.name_scope("loss"): + xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits) + loss = tf.reduce_mean(xentropy, name="loss") + +with tf.name_scope("train"): + optimizer = tf.train.GradientDescentOptimizer(learning_rate) + training_op = optimizer.minimize(loss) + +with tf.name_scope("eval"): + correct = tf.nn.in_top_k(logits, y, 1) + accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) + +init = tf.global_variables_initializer() +accuracy_summary = tf.summary.scalar('Accuracy', accuracy) +file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph()) +extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) + +with tf.Session() as sess: + init.run() + for epoch in range(n_epochs): + for iteration in range(mnist.train.num_examples // batch_size): + X_batch, y_batch = mnist.train.next_batch(batch_size) + sess.run([training_op, extra_update_ops], + feed_dict={training: True, X: X_batch, y: y_batch}) + + accuracy_val = accuracy.eval(feed_dict={X: mnist.test.images, + y: mnist.test.labels}) + print(epoch, "Test accuracy:", accuracy_val) + + # Save summary for tensorboard + summary_str = accuracy_summary.eval(feed_dict={X: mnist.validation.images, + y: mnist.validation.labels}) + file_writer.add_summary(summary_str, epoch) + + export_path = "saved" + print('Exporting trained model to ', export_path) + builder = tf.saved_model.builder.SavedModelBuilder(export_path) + signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':X}, outputs = {'y':logits}) + builder.add_meta_graph_and_variables(sess, + [tf.saved_model.tag_constants.SERVING], + signature_def_map={'serving_default':signature}) + builder.save(as_text=True) + +file_writer.close() + + |