1 files changed, 0 insertions, 98 deletions
diff --git a/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py b/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py
deleted file mode 100644
index 86a17e81f8f..00000000000
--- a/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-# Common imports
-import numpy as np
-import tensorflow as tf
-
-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 = 50
-
-input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input")
-y = tf.placeholder(tf.int64, shape=(None), name="y")
-
-
-def neuron_layer(X, n_neurons, name, activation=None):
-    with tf.name_scope(name):
-        n_inputs = int(X.get_shape()[1])
-        stddev = 2 / np.sqrt(n_inputs)
-        init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev)
-        W = tf.Variable(init, name="weights")
-        b = tf.Variable(tf.zeros([n_neurons]), name="bias")
-        Z = tf.matmul(X, W) + b
-        if activation is not None:
-            return activation(Z)
-        else:
-            return Z
-
-
-def leaky_relu(z, name=None):
-    return tf.maximum(0.01 * z, z, name=name)
-
-
-with tf.name_scope("dnn"):
-    hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu)
-    hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=tf.nn.selu)
-    logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid)
-
-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())
-
-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, feed_dict={input: X_batch, y: y_batch})
-        acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch})
-        acc_val = accuracy.eval(feed_dict={input: mnist.validation.images,
-                                           y: mnist.validation.labels})
-        print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val)
-
-        # Save summary for tensorboard
-        summary_str = accuracy_summary.eval(feed_dict={input: 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':input}, 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()