1 files changed, 0 insertions, 95 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
deleted file mode 100644
index bc6ea13ebc1..00000000000
--- a/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# 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()
-
-