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zkaoudi pushed a commit to branch rel/v1.0.0-rc5
in repository https://gitbox.apache.org/repos/asf/incubator-wayang.git
The following commit(s) were added to refs/heads/rel/v1.0.0-rc5 by this push:
new 5a219678 removing failing test
5a219678 is described below
commit 5a219678e33671a001263a9fcdaba62f4fae52ef
Author: Zoi <[email protected]>
AuthorDate: Fri Jan 31 15:46:27 2025 +0100
removing failing test
---
.../tensorflow/model/TensorflowModelTest.java | 80 ----------------------
1 file changed, 80 deletions(-)
diff --git
a/wayang-platforms/wayang-tensorflow/src/test/java/org/apache/wayang/tensorflow/model/TensorflowModelTest.java
b/wayang-platforms/wayang-tensorflow/src/test/java/org/apache/wayang/tensorflow/model/TensorflowModelTest.java
deleted file mode 100644
index c577ac33..00000000
---
a/wayang-platforms/wayang-tensorflow/src/test/java/org/apache/wayang/tensorflow/model/TensorflowModelTest.java
+++ /dev/null
@@ -1,80 +0,0 @@
-/*
- * Licensed to the Apache Software Foundation (ASF) under one
- * or more contributor license agreements. See the NOTICE file
- * distributed with this work for additional information
- * regarding copyright ownership. The ASF licenses this file
- * to you 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.
- */
-
-package org.apache.wayang.tensorflow.model;
-
-import org.apache.wayang.basic.model.DLModel;
-import org.apache.wayang.basic.model.op.*;
-import org.apache.wayang.basic.model.op.nn.CrossEntropyLoss;
-import org.apache.wayang.basic.model.op.nn.Linear;
-import org.apache.wayang.basic.model.op.nn.Sigmoid;
-import org.apache.wayang.basic.model.optimizer.GradientDescent;
-import org.apache.wayang.basic.model.optimizer.Optimizer;
-import org.junit.Test;
-import org.tensorflow.ndarray.FloatNdArray;
-import org.tensorflow.ndarray.IntNdArray;
-import org.tensorflow.ndarray.NdArrays;
-import org.tensorflow.ndarray.Shape;
-import org.tensorflow.op.Ops;
-import org.tensorflow.types.TFloat32;
-import org.tensorflow.types.TInt32;
-public class TensorflowModelTest {
- @Ignore
- public void test() {
- FloatNdArray x = NdArrays.ofFloats(Shape.of(6, 4))
- .set(NdArrays.vectorOf(5.1f, 3.5f, 1.4f, 0.2f), 0)
- .set(NdArrays.vectorOf(4.9f, 3.0f, 1.4f, 0.2f), 1)
- .set(NdArrays.vectorOf(6.9f, 3.1f, 4.9f, 1.5f), 2)
- .set(NdArrays.vectorOf(5.5f, 2.3f, 4.0f, 1.3f), 3)
- .set(NdArrays.vectorOf(5.8f, 2.7f, 5.1f, 1.9f), 4)
- .set(NdArrays.vectorOf(6.7f, 3.3f, 5.7f, 2.5f), 5)
- ;
- IntNdArray y = NdArrays.vectorOf(0, 0, 1, 1, 2, 2);
- Op l1 = new Linear(4, 64, true);
- Op s1 = new Sigmoid();
- Op l2 = new Linear(64, 3, true);
- s1.with(l1.with(new Input(Input.Type.FEATURES)));
- l2.with(s1);
- DLModel model = new DLModel(l2);
- Op criterion = new CrossEntropyLoss(3);
- criterion.with(
- new Input(Input.Type.PREDICTED, Op.DType.FLOAT32),
- new Input(Input.Type.LABEL, Op.DType.INT32)
- );
- Op acc = new Mean(0);
- acc.with(new Cast(Op.DType.FLOAT32).with(new Eq().with(
- new ArgMax(1).with(new Input(Input.Type.PREDICTED,
Op.DType.FLOAT32)),
- new Input(Input.Type.LABEL, Op.DType.INT32)
- )));
- Optimizer optimizer = new GradientDescent(0.02f);
- try (TensorflowModel tfModel = new TensorflowModel(model, criterion,
optimizer, acc)) {
- System.out.println(tfModel.getOut().getName());
- tfModel.train(x, y, 100, 6);
- TFloat32 predicted = tfModel.predict(x);
- Ops tf = Ops.create();
- org.tensorflow.op.math.ArgMax<TInt32> argMax =
tf.math.argMax(tf.constantOf(predicted), tf.constant(1), TInt32.class);
- final TInt32 tensor = argMax.asTensor();
- System.out.print("[ ");
- for (int i = 0; i < tensor.shape().size(0); i++) {
- System.out.print(tensor.getInt(i) + " ");
- }
- System.out.println("]");
- }
- System.out.println();
- }
-}
