diff options
| author | Lester Solbakken <lesters@oath.com> | 2018-01-10 09:11:04 +0100 |
|---|---|---|
| committer | Lester Solbakken <lesters@oath.com> | 2018-01-11 11:17:08 +0100 |
| commit | f3ae39125a550565e6edd6256c063072476f9d5e (patch) | |
| tree | fc0437783fb6af3a088c5cb5807648378a3715d0 /searchlib/src/test | |
| parent | 36ce12ede6f05199f219a028d6687bab1eee0a6e (diff) | |
TensorFlow import of name scopes and new activation functions.
Diffstat (limited to 'searchlib/src/test')
6 files changed, 8943 insertions, 117 deletions
diff --git a/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/mnist.py b/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/mnist.py new file mode 100644 index 00000000000..090ab2a9b81 --- /dev/null +++ b/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/mnist.py @@ -0,0 +1,95 @@ + +# 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 = 40 +batch_size = 50 + +X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X") +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 + + +with tf.name_scope("dnn"): + hidden1 = neuron_layer(X, n_hidden1, name="hidden1", activation=tf.nn.elu) + hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=tf.nn.relu) + hidden3 = neuron_layer(hidden2, n_hidden3, name="hidden3", activation=tf.nn.sigmoid) + logits = neuron_layer(hidden3, 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={X: X_batch, y: y_batch}) + acc_train = accuracy.eval(feed_dict={X: X_batch, y: y_batch}) + acc_val = accuracy.eval(feed_dict={X: 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={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() + diff --git a/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/saved_model.pbtxt b/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/saved_model.pbtxt new file mode 100644 index 00000000000..c8f7ecf11f8 --- /dev/null +++ b/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/saved_model.pbtxt @@ -0,0 +1,8550 @@ +saved_model_schema_version: 1 +meta_graphs { + meta_info_def { + stripped_op_list { + op { + name: "Add" + input_arg { + name: "x" + type_attr: "T" + } + input_arg { + name: "y" + type_attr: "T" + } + output_arg { + name: "z" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_HALF + type: DT_FLOAT + type: DT_DOUBLE + type: DT_UINT8 + type: DT_INT8 + type: DT_INT16 + type: DT_INT32 + type: DT_INT64 + type: DT_COMPLEX64 + type: DT_COMPLEX128 + type: DT_STRING + } + } + } + } + op { + name: "ApplyGradientDescent" + input_arg { + name: "var" + type_attr: "T" + is_ref: true + } + input_arg { + name: "alpha" + type_attr: "T" + } + input_arg { + name: "delta" + type_attr: "T" + } + output_arg { + name: "out" + type_attr: "T" + is_ref: true + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_FLOAT + type: DT_DOUBLE + type: DT_INT64 + type: DT_INT32 + type: DT_UINT8 + type: DT_UINT16 + type: DT_INT16 + type: DT_INT8 + type: DT_COMPLEX64 + type: DT_COMPLEX128 + type: DT_QINT8 + type: DT_QUINT8 + type: DT_QINT32 + type: DT_HALF + } + } + } + attr { + name: "use_locking" + type: "bool" + default_value { + b: false + } + } + } + op { + name: "Assign" + input_arg { + name: "ref" + type_attr: "T" + is_ref: true + } + input_arg { + name: "value" + type_attr: "T" + } + output_arg { + name: "output_ref" + type_attr: "T" + is_ref: true + } + attr { + name: "T" + type: "type" + } + attr { + name: "validate_shape" + type: "bool" + default_value { + b: true + } + } + attr { + name: "use_locking" + type: "bool" + default_value { + b: true + } + } + allows_uninitialized_input: true + } + op { + name: "BroadcastGradientArgs" + input_arg { + name: "s0" + type_attr: "T" + } + input_arg { + name: "s1" + type_attr: "T" + } + output_arg { + name: "r0" + type_attr: "T" + } + output_arg { + name: "r1" + type_attr: "T" + } + attr { + name: "T" + type: "type" + default_value { + type: DT_INT32 + } + allowed_values { + list { + type: DT_INT32 + type: 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+ 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/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/variables/variables.data-00000-of-00001 b/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/variables/variables.data-00000-of-00001 Binary files differnew file mode 100644 index 00000000000..e286d3bb9de --- /dev/null +++ b/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/variables/variables.data-00000-of-00001 diff --git a/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/variables/variables.index b/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/variables/variables.index Binary files differnew file mode 100644 index 00000000000..7643ec22a7d --- /dev/null +++ b/searchlib/src/test/files/integration/tensorflow/3_layer_mnist/saved/variables/variables.index diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/Mnist_SoftmaxTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/Mnist_SoftmaxTestCase.java deleted file mode 100644 index 6536a2f2a06..00000000000 --- a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/Mnist_SoftmaxTestCase.java +++ /dev/null @@ -1,117 +0,0 @@ -package com.yahoo.searchlib.rankingexpression.integration.tensorflow; - -import com.yahoo.searchlib.rankingexpression.RankingExpression; -import com.yahoo.searchlib.rankingexpression.evaluation.Context; -import com.yahoo.searchlib.rankingexpression.evaluation.MapContext; -import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; -import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode; -import com.yahoo.tensor.Tensor; -import com.yahoo.tensor.TensorType; -import org.junit.Ignore; -import org.junit.Test; -import org.tensorflow.SavedModelBundle; -import org.tensorflow.Session; - -import java.nio.FloatBuffer; -import java.util.List; - -import static org.junit.Assert.assertEquals; -import static org.junit.Assert.assertNotNull; - -/** - * @author bratseth - */ -public class Mnist_SoftmaxTestCase { - - @Ignore - @Test - public void testImporting() { - String modelDir = "src/test/files/integration/tensorflow/mnist_softmax/saved"; - SavedModelBundle model = SavedModelBundle.load(modelDir, "serve"); - TensorFlowModel result = new TensorFlowImporter().importModel(model); - - // Check constants - assertEquals(2, result.constants().size()); - - Tensor constant0 = result.constants().get("Variable"); - assertNotNull(constant0); - assertEquals(new TensorType.Builder().indexed("d0", 784).indexed("d1", 10).build(), - constant0.type()); - assertEquals(7840, constant0.size()); - - Tensor constant1 = result.constants().get("Variable_1"); - assertNotNull(constant1); - assertEquals(new TensorType.Builder().indexed("d0", 10).build(), - constant1.type()); - assertEquals(10, constant1.size()); - - // Check signatures - assertEquals(1, result.signatures().size()); - TensorFlowModel.Signature signature = result.signatures().get("serving_default"); - assertNotNull(signature); - - // ... signature inputs - assertEquals(1, signature.inputs().size()); - TensorType argument0 = signature.inputArgument("x"); - assertNotNull(argument0); - assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), argument0); - - // ... signature outputs - assertEquals(1, signature.outputs().size()); - RankingExpression output = signature.outputExpression("y"); - assertNotNull(output); - assertEquals("add", output.getName()); - assertEquals("" + - "join(rename(matmul(Placeholder, rename(constant(Variable), (d0, d1), (d1, d3)), d1), d3, d1), " + - "rename(constant(Variable_1), d0, d1), " + - "f(a,b)(a + b))", - toNonPrimitiveString(output)); - - // ... skipped outputs - assertEquals(0, signature.skippedOutputs().size()); - - // Test execution - assertEqualResult(model, result, "Variable/read"); - assertEqualResult(model, result, "Variable_1/read"); - assertEqualResult(model, result, "MatMul"); - assertEqualResult(model, result, "add"); - } - - private void assertEqualResult(SavedModelBundle model, TensorFlowModel result, String operationName) { - Tensor tfResult = tensorFlowExecute(model, operationName); - Context context = contextFrom(result); - Tensor placeholder = placeholderArgument(); - context.put("Placeholder", new TensorValue(placeholder)); - Tensor vespaResult = result.expressions().get(operationName).evaluate(context).asTensor(); - assertEquals("Operation '" + operationName + "' produces equal results", vespaResult, tfResult); - } - - private Tensor tensorFlowExecute(SavedModelBundle model, String operationName) { - Session.Runner runner = model.session().runner(); - org.tensorflow.Tensor<?> placeholder = org.tensorflow.Tensor.create(new long[]{ 1, 784 }, FloatBuffer.allocate(784)); - runner.feed("Placeholder", placeholder); - List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run(); - assertEquals(1, results.size()); - return new TensorConverter().toVespaTensor(results.get(0)); - } - - private Context contextFrom(TensorFlowModel result) { - MapContext context = new MapContext(); - result.constants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor))); - return context; - } - - private String toNonPrimitiveString(RankingExpression expression) { - // toString on the wrapping expression will map to primitives, which is harder to read - return ((TensorFunctionNode)expression.getRoot()).function().toString(); - } - - private Tensor placeholderArgument() { - int size = 784; - Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", size).build()); - for (int i = 0; i < size; i++) - b.cell(0, 0, i); - return b.build(); - } - -} diff --git a/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TensorflowImportTestCase.java b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TensorflowImportTestCase.java new file mode 100644 index 00000000000..2aec1988a22 --- /dev/null +++ b/searchlib/src/test/java/com/yahoo/searchlib/rankingexpression/integration/tensorflow/TensorflowImportTestCase.java @@ -0,0 +1,298 @@ +package com.yahoo.searchlib.rankingexpression.integration.tensorflow; + +import com.yahoo.searchlib.rankingexpression.RankingExpression; +import com.yahoo.searchlib.rankingexpression.evaluation.Context; +import com.yahoo.searchlib.rankingexpression.evaluation.MapContext; +import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue; +import com.yahoo.searchlib.rankingexpression.rule.TensorFunctionNode; +import com.yahoo.tensor.Tensor; +import com.yahoo.tensor.TensorType; +import org.junit.Ignore; +import org.junit.Test; +import org.tensorflow.SavedModelBundle; +import org.tensorflow.Session; + +import java.nio.FloatBuffer; +import java.util.List; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; + +/** + * @author bratseth + */ +public class TensorflowImportTestCase { + + @Ignore + @Test + public void testMnistSoftmaxImport() { + String modelDir = "src/test/files/integration/tensorflow/mnist_softmax/saved"; + SavedModelBundle model = SavedModelBundle.load(modelDir, "serve"); + TensorFlowModel result = new TensorFlowImporter().importModel(model); + + // Check constants + assertEquals(2, result.constants().size()); + + Tensor constant0 = result.constants().get("Variable"); + assertNotNull(constant0); + assertEquals(new TensorType.Builder().indexed("d0", 784).indexed("d1", 10).build(), + constant0.type()); + assertEquals(7840, constant0.size()); + + Tensor constant1 = result.constants().get("Variable_1"); + assertNotNull(constant1); + assertEquals(new TensorType.Builder().indexed("d0", 10).build(), + constant1.type()); + assertEquals(10, constant1.size()); + + // Check signatures + assertEquals(1, result.signatures().size()); + TensorFlowModel.Signature signature = result.signatures().get("serving_default"); + assertNotNull(signature); + + // ... signature inputs + assertEquals(1, signature.inputs().size()); + TensorType argument0 = signature.inputArgument("x"); + assertNotNull(argument0); + assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), argument0); + + // ... signature outputs + assertEquals(1, signature.outputs().size()); + RankingExpression output = signature.outputExpression("y"); + assertNotNull(output); + assertEquals("add", output.getName()); + assertEquals("" + + "join(rename(matmul(Placeholder, rename(constant(Variable), (d0, d1), (d1, d3)), d1), d3, d1), " + + "rename(constant(Variable_1), d0, d1), " + + "f(a,b)(a + b))", + toNonPrimitiveString(output)); + + // Test execution + assertEqualResult(model, result, "Placeholder", "Variable/read"); + assertEqualResult(model, result, "Placeholder", "Variable_1/read"); + assertEqualResult(model, result, "Placeholder", "MatMul"); + assertEqualResult(model, result, "Placeholder", "add"); + } + + @Ignore + @Test + public void test3LayerMnistImport() { + String modelDir = "src/test/files/integration/tensorflow/3_layer_mnist/saved"; + SavedModelBundle model = SavedModelBundle.load(modelDir, "serve"); + TensorFlowModel result = new TensorFlowImporter().importModel(model); + + // Check constants + assertEquals(8, result.constants().size()); + + Tensor outputBias = result.constants().get("dnn/outputs/bias"); + assertNotNull(outputBias); + assertEquals(new TensorType.Builder().indexed("d0", 10).build(), outputBias.type()); + assertEquals(10, outputBias.size()); + + Tensor outputWeights = result.constants().get("dnn/outputs/weights"); + assertNotNull(outputWeights); + assertEquals(new TensorType.Builder().indexed("d0", 40).indexed("d1", 10).build(), outputWeights.type()); + assertEquals(400, outputWeights.size()); + + Tensor hidden3Bias = result.constants().get("dnn/hidden3/bias"); + assertNotNull(hidden3Bias); + assertEquals(new TensorType.Builder().indexed("d0", 40).build(), hidden3Bias.type()); + assertEquals(40, hidden3Bias.size()); + + Tensor hidden3Weights = result.constants().get("dnn/hidden3/weights"); + assertNotNull(hidden3Weights); + assertEquals(new TensorType.Builder().indexed("d0", 100).indexed("d1", 40).build(), hidden3Weights.type()); + assertEquals(4000, hidden3Weights.size()); + + Tensor hidden2Bias = result.constants().get("dnn/hidden2/bias"); + assertNotNull(hidden2Bias); + assertEquals(new TensorType.Builder().indexed("d0", 100).build(), hidden2Bias.type()); + assertEquals(100, hidden2Bias.size()); + + Tensor hidden2Weights = result.constants().get("dnn/hidden2/weights"); + assertNotNull(hidden2Weights); + assertEquals(new TensorType.Builder().indexed("d0", 300).indexed("d1", 100).build(), hidden2Weights.type()); + assertEquals(30000, hidden2Weights.size()); + + Tensor hidden1Bias = result.constants().get("dnn/hidden1/bias"); + assertNotNull(hidden1Bias); + assertEquals(new TensorType.Builder().indexed("d0", 300).build(), hidden1Bias.type()); + assertEquals(300, hidden1Bias.size()); + + Tensor hidden1Weights = result.constants().get("dnn/hidden1/weights"); + assertNotNull(hidden1Weights); + assertEquals(new TensorType.Builder().indexed("d0", 784).indexed("d1", 300).build(), hidden1Weights.type()); + assertEquals(235200, hidden1Weights.size()); + + // Check signatures + assertEquals(1, result.signatures().size()); + TensorFlowModel.Signature signature = result.signatures().get("serving_default"); + assertNotNull(signature); + + // ... signature inputs + assertEquals(1, signature.inputs().size()); + TensorType argument0 = signature.inputArgument("x"); + assertNotNull(argument0); + assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), argument0); + + // ... signature outputs + assertEquals(1, signature.outputs().size()); + RankingExpression output = signature.outputExpression("y"); + assertNotNull(output); + assertEquals("dnn/outputs/add", output.getName()); + assertEquals("" + + "join(" + + "rename(" + + "matmul(" + + "map(" + + "join(" + + "rename(" + + "matmul(" + + "map(" + + "join(" + + "rename(" + + "matmul(" + + "map(" + + "join(" + + "rename(" + + "matmul(" + + "X, " + + "rename(" + + "constant(dnn/hidden1/weights), " + + "(d0, d1), " + + "(d1, d3)" + + "), " + + "d1" + + "), " + + "d3, " + + "d1" + + "), " + + "rename(" + + "constant(dnn/hidden1/bias), " + + "d0, " + + "d1" + + "), " + + "f(a,b)(a + b)" + + "), " + + "f(a)(if(a < 0, exp(a)-1, a))" + + "), " + + "rename(" + + "constant(dnn/hidden2/weights), " + + "(d0, d1), " + + "(d1, d3)" + + "), " + + "d1" + + "), " + + "d3, " + + "d1" + + "), " + + "rename(" + + "constant(dnn/hidden2/bias), " + + "d0, " + + "d1" + + "), " + + "f(a,b)(a + b)" + + "), " + + "f(a)(max(0, a))" + + "), " + + "rename(" + + "constant(dnn/hidden3/weights), " + + "(d0, d1), " + + "(d1, d3)" + + "), " + + "d1" + + "), " + + "d3, " + + "d1" + + "), " + + "rename(" + + "constant(dnn/hidden3/bias), " + + "d0, " + + "d1" + + "), " + + "f(a,b)(a + b)" + + "), " + + "f(a)(1 / (1 + exp(-a)))" + + "), " + + "rename(" + + "constant(dnn/outputs/weights), " + + "(d0, d1), " + + "(d1, d3)" + + "), " + + "d1" + + "), " + + "d3, " + + "d1" + + "), " + + "rename(" + + "constant(dnn/outputs/bias), " + + "d0, " + + "d1" + + "), " + + "f(a,b)(a + b)" + + ")", + toNonPrimitiveString(output)); + + // Test constants + assertEqualResult(model, result, "X", "dnn/hidden1/weights/read"); + assertEqualResult(model, result, "X", "dnn/hidden1/bias/read"); + assertEqualResult(model, result, "X", "dnn/hidden2/weights/read"); + assertEqualResult(model, result, "X", "dnn/hidden2/bias/read"); + assertEqualResult(model, result, "X", "dnn/hidden3/weights/read"); + assertEqualResult(model, result, "X", "dnn/hidden3/bias/read"); + assertEqualResult(model, result, "X", "dnn/outputs/weights/read"); + assertEqualResult(model, result, "X", "dnn/outputs/bias/read"); + + // Test execution + assertEqualResult(model, result, "X", "dnn/hidden1/MatMul"); + assertEqualResult(model, result, "X", "dnn/hidden1/add"); + assertEqualResult(model, result, "X", "dnn/hidden1/Elu"); + assertEqualResult(model, result, "X", "dnn/hidden2/MatMul"); + assertEqualResult(model, result, "X", "dnn/hidden2/add"); + assertEqualResult(model, result, "X", "dnn/hidden2/Relu"); + assertEqualResult(model, result, "X", "dnn/hidden3/MatMul"); + assertEqualResult(model, result, "X", "dnn/hidden3/add"); + assertEqualResult(model, result, "X", "dnn/hidden3/Sigmoid"); + assertEqualResult(model, result, "X", "dnn/outputs/MatMul"); + assertEqualResult(model, result, "X", "dnn/outputs/add"); + } + + + private void assertEqualResult(SavedModelBundle model, TensorFlowModel result, String inputName, String operationName) { + Tensor tfResult = tensorFlowExecute(model, inputName, operationName); + Context context = contextFrom(result); + Tensor placeholder = placeholderArgument(); + context.put(inputName, new TensorValue(placeholder)); + Tensor vespaResult = result.expressions().get(operationName).evaluate(context).asTensor(); + assertEquals("Operation '" + operationName + "' produces equal results", vespaResult, tfResult); + } + + private Tensor tensorFlowExecute(SavedModelBundle model, String inputName, String operationName) { + Session.Runner runner = model.session().runner(); + org.tensorflow.Tensor<?> placeholder = org.tensorflow.Tensor.create(new long[]{ 1, 784 }, FloatBuffer.allocate(784)); + runner.feed(inputName, placeholder); + List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run(); + assertEquals(1, results.size()); + return new TensorConverter().toVespaTensor(results.get(0)); + } + + private Context contextFrom(TensorFlowModel result) { + MapContext context = new MapContext(); + result.constants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(tensor))); + return context; + } + + private String toNonPrimitiveString(RankingExpression expression) { + // toString on the wrapping expression will map to primitives, which is harder to read + return ((TensorFunctionNode)expression.getRoot()).function().toString(); + } + + private Tensor placeholderArgument() { + int size = 784; + Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", size).build()); + for (int i = 0; i < size; i++) + b.cell(0, 0, i); + return b.build(); + } + +} |