Revert "Import Tensorflow models vis ONNX conversion"

This reverts commit 0a886d74d4c9ffde41eef1f7e3c186b60b9f3726.

10 files changed, 14069 insertions, 7 deletions

diff --git a/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx b/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx
deleted file mode 100644
index 0d4bffa5b57..00000000000
--- a/application/src/test/app-packages/model-evaluation/models/onnx/softmax_func.onnx
+++ /dev/null
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt
new file mode 100644
index 00000000000..5528aa99401
--- /dev/null
+++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/saved_model.pbtxt
@@ -0,0 +1,8830 @@
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+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
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+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_2/tensor_names"
+      input: "save/RestoreV2_2/shape_and_slices"
+      attr {
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+      op: "Assign"
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+      input: "save/RestoreV2_2"
+      attr {
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+        value {
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+      attr {
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+    node {
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+      op: "Const"
+      attr {
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+    node {
+      name: "save/RestoreV2_3"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_3/tensor_names"
+      input: "save/RestoreV2_3/shape_and_slices"
+      attr {
+        key: "_output_shapes"
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+      attr {
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+    }
+    node {
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+      op: "Assign"
+      input: "dnn/hidden2/weights"
+      input: "save/RestoreV2_3"
+      attr {
+        key: "T"
+        value {
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+      }
+      attr {
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+      }
+      attr {
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+        value {
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+      }
+      attr {
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+    }
+    node {
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+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+              dim {
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+          }
+        }
+      }
+      attr {
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+        value {
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+        }
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+      attr {
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+        value {
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+            tensor_shape {
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+            string_val: "dnn/outputs/bias"
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+      }
+    }
+    node {
+      name: "save/RestoreV2_4/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
+              dim {
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+      attr {
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+            tensor_shape {
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+    }
+    node {
+      name: "save/RestoreV2_4"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_4/tensor_names"
+      input: "save/RestoreV2_4/shape_and_slices"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+      attr {
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+    node {
+      name: "save/Assign_4"
+      op: "Assign"
+      input: "dnn/outputs/bias"
+      input: "save/RestoreV2_4"
+      attr {
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+      }
+      attr {
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+        }
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+              dim {
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+        }
+      }
+      attr {
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+        value {
+          b: true
+        }
+      }
+      attr {
+        key: "validate_shape"
+        value {
+          b: true
+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_5/tensor_names"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
+              dim {
+                size: 1
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+            }
+          }
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+      }
+      attr {
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+      attr {
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+        value {
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+            tensor_shape {
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+            string_val: "dnn/outputs/weights"
+          }
+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_5/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
+              dim {
+                size: 1
+              }
+            }
+          }
+        }
+      }
+      attr {
+        key: "dtype"
+        value {
+          type: DT_STRING
+        }
+      }
+      attr {
+        key: "value"
+        value {
+          tensor {
+            dtype: DT_STRING
+            tensor_shape {
+              dim {
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+            }
+            string_val: ""
+          }
+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_5"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_5/tensor_names"
+      input: "save/RestoreV2_5/shape_and_slices"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
+              unknown_rank: true
+            }
+          }
+        }
+      }
+      attr {
+        key: "dtypes"
+        value {
+          list {
+            type: DT_FLOAT
+          }
+        }
+      }
+    }
+    node {
+      name: "save/Assign_5"
+      op: "Assign"
+      input: "dnn/outputs/weights"
+      input: "save/RestoreV2_5"
+      attr {
+        key: "T"
+        value {
+          type: DT_FLOAT
+        }
+      }
+      attr {
+        key: "_class"
+        value {
+          list {
+            s: "loc:@dnn/outputs/weights"
+          }
+        }
+      }
+      attr {
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+        value {
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+              dim {
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+              dim {
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+            }
+          }
+        }
+      }
+      attr {
+        key: "use_locking"
+        value {
+          b: true
+        }
+      }
+      attr {
+        key: "validate_shape"
+        value {
+          b: true
+        }
+      }
+    }
+    node {
+      name: "save/restore_shard"
+      op: "NoOp"
+      input: "^save/Assign"
+      input: "^save/Assign_1"
+      input: "^save/Assign_2"
+      input: "^save/Assign_3"
+      input: "^save/Assign_4"
+      input: "^save/Assign_5"
+    }
+    node {
+      name: "save/restore_all"
+      op: "NoOp"
+      input: "^save/restore_shard"
+    }
+    versions {
+      producer: 24
+    }
+  }
+  saver_def {
+    filename_tensor_name: "save/Const:0"
+    save_tensor_name: "save/Identity:0"
+    restore_op_name: "save/restore_all"
+    max_to_keep: 5
+    sharded: true
+    keep_checkpoint_every_n_hours: 10000.0
+    version: V2
+  }
+  collection_def {
+    key: "summaries"
+    value {
+      node_list {
+        value: "Accuracy:0"
+      }
+    }
+  }
+  collection_def {
+    key: "train_op"
+    value {
+      node_list {
+        value: "train/GradientDescent"
+      }
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+  }
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+      }
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+  }
+  signature_def {
+    key: "serving_default"
+    value {
+      inputs {
+        key: "x"
+        value {
+          name: "input:0"
+          dtype: DT_FLOAT
+          tensor_shape {
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+            }
+            dim {
+              size: 784
+            }
+          }
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+      }
+      outputs {
+        key: "y"
+        value {
+          name: "dnn/outputs/add:0"
+          dtype: DT_FLOAT
+          tensor_shape {
+            dim {
+              size: -1
+            }
+            dim {
+              size: 10
+            }
+          }
+        }
+      }
+      method_name: "tensorflow/serving/predict"
+    }
+  }
+}
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001 b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001
new file mode 100644
index 00000000000..ed4af6c0f8c
--- /dev/null
+++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.index b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.index
new file mode 100644
index 00000000000..c877b02b42a
--- /dev/null
+++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/saved/variables/variables.index
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/simple_mnist.py b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/simple_mnist.py
new file mode 100644
index 00000000000..7494e93fa71
--- /dev/null
+++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist/simple_mnist.py
@@ -0,0 +1,100 @@
+# 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)
+
+def leaky_relu_with_small_constant(z, name=None):
+    return tf.maximum(tf.constant(0.01, shape=[1]) * 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=leaky_relu_with_small_constant)
+    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()
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py
new file mode 100644
index 00000000000..3f4f794d2ac
--- /dev/null
+++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py
@@ -0,0 +1,93 @@
+# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+
+"""A very simple MNIST classifier.
+
+See extensive documentation at
+https://www.tensorflow.org/get_started/mnist/beginners
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+
+from tensorflow.examples.tutorials.mnist import input_data
+
+import tensorflow as tf
+
+FLAGS = None
+
+
+def main(_):
+  # Import data
+  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
+
+  # Create the model
+  x = tf.placeholder(tf.float32, [None, 784])
+
+  with tf.name_scope("layer"):
+    W = tf.Variable(tf.zeros([784, 10]))
+    b = tf.Variable(tf.zeros([10]))
+    y = tf.matmul(x, W) + b
+
+
+  # Define loss and optimizer
+  y_ = tf.placeholder(tf.float32, [None, 10])
+
+  # The raw formulation of cross-entropy,
+  #
+  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
+  #                                 reduction_indices=[1]))
+  #
+  # can be numerically unstable.
+  #
+  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
+  # outputs of 'y', and then average across the batch.
+  cross_entropy = tf.reduce_mean(
+      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
+  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
+
+  sess = tf.InteractiveSession()
+  tf.global_variables_initializer().run()
+  # Train
+  for _ in range(1000):
+    batch_xs, batch_ys = mnist.train.next_batch(100)
+    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
+
+  # Test trained model
+  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
+  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+  print(sess.run(accuracy, feed_dict={x: mnist.test.images,
+                                      y_: mnist.test.labels}))
+
+  # Save the model
+  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':y})
+  builder.add_meta_graph_and_variables(sess,
+                                       [tf.saved_model.tag_constants.SERVING],
+                                       signature_def_map={'serving_default':signature})
+  builder.save(as_text=True)
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
+                      help='Directory for storing input data')
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt
new file mode 100644
index 00000000000..05b0e4e0f29
--- /dev/null
+++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt
@@ -0,0 +1,5039 @@
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+      op: "StringJoin"
+      input: "save/Const"
+      input: "save/StringJoin/inputs_1"
+      attr {
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+      op: "ShardedFilename"
+      input: "save/StringJoin"
+      input: "save/ShardedFilename/shard"
+      input: "save/num_shards"
+      attr {
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+        value {
+          list {
+            shape {
+            }
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+      op: "Const"
+      attr {
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+    }
+    node {
+      name: "save/SaveV2/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+              dim {
+                size: 2
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+      attr {
+        key: "dtype"
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+              dim {
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+              }
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+            string_val: ""
+            string_val: ""
+          }
+        }
+      }
+    }
+    node {
+      name: "save/SaveV2"
+      op: "SaveV2"
+      input: "save/ShardedFilename"
+      input: "save/SaveV2/tensor_names"
+      input: "save/SaveV2/shape_and_slices"
+      input: "layer/Variable"
+      input: "layer/Variable_1"
+      attr {
+        key: "dtypes"
+        value {
+          list {
+            type: DT_FLOAT
+            type: DT_FLOAT
+          }
+        }
+      }
+    }
+    node {
+      name: "save/control_dependency"
+      op: "Identity"
+      input: "save/ShardedFilename"
+      input: "^save/SaveV2"
+      attr {
+        key: "T"
+        value {
+          type: DT_STRING
+        }
+      }
+      attr {
+        key: "_class"
+        value {
+          list {
+            s: "loc:@save/ShardedFilename"
+          }
+        }
+      }
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
+            }
+          }
+        }
+      }
+    }
+    node {
+      name: "save/MergeV2Checkpoints/checkpoint_prefixes"
+      op: "Pack"
+      input: "save/ShardedFilename"
+      input: "^save/control_dependency"
+      attr {
+        key: "N"
+        value {
+          i: 1
+        }
+      }
+      attr {
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+      attr {
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+              dim {
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+          }
+        }
+      }
+      attr {
+        key: "axis"
+        value {
+          i: 0
+        }
+      }
+    }
+    node {
+      name: "save/MergeV2Checkpoints"
+      op: "MergeV2Checkpoints"
+      input: "save/MergeV2Checkpoints/checkpoint_prefixes"
+      input: "save/Const"
+      attr {
+        key: "delete_old_dirs"
+        value {
+          b: true
+        }
+      }
+    }
+    node {
+      name: "save/Identity"
+      op: "Identity"
+      input: "save/Const"
+      input: "^save/control_dependency"
+      input: "^save/MergeV2Checkpoints"
+      attr {
+        key: "T"
+        value {
+          type: DT_STRING
+        }
+      }
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
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+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2/tensor_names"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
+              dim {
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+              }
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+      attr {
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+        value {
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+        value {
+          tensor {
+            dtype: DT_STRING
+            tensor_shape {
+              dim {
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+            string_val: "layer/Variable"
+          }
+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+              }
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+      }
+      attr {
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+        value {
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+    }
+    node {
+      name: "save/RestoreV2"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2/tensor_names"
+      input: "save/RestoreV2/shape_and_slices"
+      attr {
+        key: "_output_shapes"
+        value {
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+    node {
+      name: "save/Assign"
+      op: "Assign"
+      input: "layer/Variable"
+      input: "save/RestoreV2"
+      attr {
+        key: "T"
+        value {
+          type: DT_FLOAT
+        }
+      }
+      attr {
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+        value {
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+            s: "loc:@layer/Variable"
+          }
+        }
+      }
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
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+              dim {
+                size: 784
+              }
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+      }
+      attr {
+        key: "use_locking"
+        value {
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+        }
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+      attr {
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+        value {
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+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_1/tensor_names"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
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+            tensor_shape {
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+              }
+            }
+            string_val: "layer/Variable_1"
+          }
+        }
+      }
+    }
+    node {
+      name: "save/RestoreV2_1/shape_and_slices"
+      op: "Const"
+      attr {
+        key: "_output_shapes"
+        value {
+          list {
+            shape {
+              dim {
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+              }
+            }
+          }
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+      attr {
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+            tensor_shape {
+              dim {
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+      }
+    }
+    node {
+      name: "save/RestoreV2_1"
+      op: "RestoreV2"
+      input: "save/Const"
+      input: "save/RestoreV2_1/tensor_names"
+      input: "save/RestoreV2_1/shape_and_slices"
+      attr {
+        key: "_output_shapes"
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+            }
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+          }
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+      }
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+    node {
+      name: "save/Assign_1"
+      op: "Assign"
+      input: "layer/Variable_1"
+      input: "save/RestoreV2_1"
+      attr {
+        key: "T"
+        value {
+          type: DT_FLOAT
+        }
+      }
+      attr {
+        key: "_class"
+        value {
+          list {
+            s: "loc:@layer/Variable_1"
+          }
+        }
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+      attr {
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+        value {
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+        value {
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+      }
+      attr {
+        key: "validate_shape"
+        value {
+          b: true
+        }
+      }
+    }
+    node {
+      name: "save/restore_shard"
+      op: "NoOp"
+      input: "^save/Assign"
+      input: "^save/Assign_1"
+    }
+    node {
+      name: "save/restore_all"
+      op: "NoOp"
+      input: "^save/restore_shard"
+    }
+    versions {
+      producer: 24
+    }
+  }
+  saver_def {
+    filename_tensor_name: "save/Const:0"
+    save_tensor_name: "save/Identity:0"
+    restore_op_name: "save/restore_all"
+    max_to_keep: 5
+    sharded: true
+    keep_checkpoint_every_n_hours: 10000.0
+    version: V2
+  }
+  collection_def {
+    key: "train_op"
+    value {
+      node_list {
+        value: "GradientDescent"
+      }
+    }
+  }
+  collection_def {
+    key: "trainable_variables"
+    value {
+      bytes_list {
+        value: "\n\020layer/Variable:0\022\025layer/Variable/Assign\032\025layer/Variable/read:02\rlayer/zeros:0"
+        value: "\n\022layer/Variable_1:0\022\027layer/Variable_1/Assign\032\027layer/Variable_1/read:02\017layer/zeros_1:0"
+      }
+    }
+  }
+  collection_def {
+    key: "variables"
+    value {
+      bytes_list {
+        value: "\n\020layer/Variable:0\022\025layer/Variable/Assign\032\025layer/Variable/read:02\rlayer/zeros:0"
+        value: "\n\022layer/Variable_1:0\022\027layer/Variable_1/Assign\032\027layer/Variable_1/read:02\017layer/zeros_1:0"
+      }
+    }
+  }
+  signature_def {
+    key: "serving_default"
+    value {
+      inputs {
+        key: "x"
+        value {
+          name: "Placeholder:0"
+          dtype: DT_FLOAT
+          tensor_shape {
+            dim {
+              size: -1
+            }
+            dim {
+              size: 784
+            }
+          }
+        }
+      }
+      outputs {
+        key: "y"
+        value {
+          name: "layer/add:0"
+          dtype: DT_FLOAT
+          tensor_shape {
+            dim {
+              size: -1
+            }
+            dim {
+              size: 10
+            }
+          }
+        }
+      }
+      method_name: "tensorflow/serving/predict"
+    }
+  }
+}
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001 b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001
new file mode 100644
index 00000000000..826b0280abf
--- /dev/null
+++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.data-00000-of-00001
diff --git a/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index
new file mode 100644
index 00000000000..d00fc5b06ed
--- /dev/null
+++ b/application/src/test/app-packages/model-evaluation/models/tensorflow/mnist_softmax/saved/variables/variables.index
diff --git a/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java b/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java
index 369b2cbe42b..3d7eed1e729 100644
--- a/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java
+++ b/application/src/test/java/com/yahoo/application/container/ContainerModelEvaluationTest.java
@@ -45,7 +45,7 @@ public class ContainerModelEvaluationTest {
     }
     private void assertLoadedModels(JDisc jdisc) {
         {
-            String expected = "{\"xgboost_xgboost_2_2\":\"http://localhost/model-evaluation/v1/xgboost_xgboost_2_2\",\"onnx_mnist_softmax\":\"http://localhost/model-evaluation/v1/onnx_mnist_softmax\",\"vespa_example\":\"http://localhost/model-evaluation/v1/vespa_example\",\"onnx_softmax_func\":\"http://localhost/model-evaluation/v1/onnx_softmax_func\",\"lightgbm_regression\":\"http://localhost/model-evaluation/v1/lightgbm_regression\"}";
+            String expected = "{\"xgboost_xgboost_2_2\":\"http://localhost/model-evaluation/v1/xgboost_xgboost_2_2\",\"onnx_mnist_softmax\":\"http://localhost/model-evaluation/v1/onnx_mnist_softmax\",\"tensorflow_mnist_softmax_saved\":\"http://localhost/model-evaluation/v1/tensorflow_mnist_softmax_saved\",\"tensorflow_mnist_saved\":\"http://localhost/model-evaluation/v1/tensorflow_mnist_saved\",\"vespa_example\":\"http://localhost/model-evaluation/v1/vespa_example\",\"lightgbm_regression\":\"http://localhost/model-evaluation/v1/lightgbm_regression\"}";
             assertResponse("http://localhost/model-evaluation/v1", expected, jdisc);
         }
 
@@ -60,8 +60,9 @@ public class ContainerModelEvaluationTest {
         }
 
         {
-            String expected = "{\"cells\":[{\"address\":{\"d0\":\"0\"},\"value\":0.3006095290184021},{\"address\":{\"d0\":\"1\"},\"value\":0.33222490549087524},{\"address\":{\"d0\":\"2\"},\"value\":0.36716532707214355}]}";
-            assertResponse("http://localhost/model-evaluation/v1/onnx_softmax_func/default.output/eval?input=" + inputTensor(), expected, jdisc);
+            // Note: The specific response value here has not been verified
+            String expected = "{\"cells\":[{\"address\":{\"d0\":\"0\",\"d1\":\"0\"},\"value\":-0.5066885003407351},{\"address\":{\"d0\":\"0\",\"d1\":\"1\"},\"value\":0.3912837743150205},{\"address\":{\"d0\":\"0\",\"d1\":\"2\"},\"value\":-0.12401806321703948},{\"address\":{\"d0\":\"0\",\"d1\":\"3\"},\"value\":-0.7019029168606575},{\"address\":{\"d0\":\"0\",\"d1\":\"4\"},\"value\":0.13120114146441697},{\"address\":{\"d0\":\"0\",\"d1\":\"5\"},\"value\":0.6611923203384626},{\"address\":{\"d0\":\"0\",\"d1\":\"6\"},\"value\":-0.22365810810026446},{\"address\":{\"d0\":\"0\",\"d1\":\"7\"},\"value\":-0.0740018307465809},{\"address\":{\"d0\":\"0\",\"d1\":\"8\"},\"value\":0.056492490256153896},{\"address\":{\"d0\":\"0\",\"d1\":\"9\"},\"value\":-0.18422015072393733}]}";
+            assertResponse("http://localhost/model-evaluation/v1/tensorflow_mnist_saved/serving_default.y/eval?input=" + inputTensor(), expected, jdisc);
         }
     }
 
@@ -78,10 +79,9 @@ public class ContainerModelEvaluationTest {
     }
 
     private String inputTensor() {
-        Tensor.Builder b = Tensor.Builder.of(TensorType.fromSpec("tensor<float>(d0[3])"));
-        b.cell(0.1, 0);
-        b.cell(0.2, 1);
-        b.cell(0.3, 2);
+        Tensor.Builder b = Tensor.Builder.of(TensorType.fromSpec("tensor(d0[],d1[784])"));
+        for (int i = 0; i < 784; i++)
+            b.cell(0.0, 0, i);
         return URLEncoder.encode(b.build().toString(), StandardCharsets.UTF_8);
     }