8000 Reduce the time of keras unittest. by AsakusaRinne · Pull Request #999 · SciSharp/TensorFlow.NET · GitHub
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Reduce the time of keras unittest. #999

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Merged
merged 3 commits into from
Mar 5, 2023
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Reduce the time of keras unittest. #999

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f22876a
Reduce the time of keras unittest.
AsakusaRinne Mar 4, 2023
a36af5c
Merge branch 'master' of github.com:AsakusaRinne/TensorFlow.NET into …
AsakusaRinne Mar 5, 2023
151ef06
Refine the keras SavedModel unittest.
AsakusaRinne Mar 5, 2023
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0 .../TensorFlowNET.Keras.UnitTest/Gradient.cs → ...sorFlowNET.Keras.UnitTest/GradientTest.cs
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2 changes: 1 addition & 1 deletion test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelLoad.cs
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8000
Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ public void SimpleModelFromAutoCompile()
{
TrainDir = "mnist",
OneHot = false,
ValidationSize = 50000,
ValidationSize = 58000,
}).Result;

model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
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18 changes: 9 additions & 9 deletions test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelSave.cs
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Expand Up @@ -18,15 +18,15 @@ public class SequentialModelSave
[TestMethod]
public void SimpleModelFromAutoCompile()
{
var inputs = tf.keras.layers.Input((28, 28, 1));
var x = tf.keras.layers.Flatten().Apply(inputs);
x = tf.keras.layers.Dense(100, activation: tf.nn.relu).Apply(x);
x = tf.keras.layers.Dense(units: 10).Apply(x);
var outputs = tf.keras.layers.Softmax(axis: 1).Apply(x);
var model = tf.keras.Model(inputs, outputs);
var inputs = keras.layers.Input((28, 28, 1));
var x = keras.layers.Flatten().Apply(inputs);
x = keras.layers.Dense(100, activation: tf.nn.relu).Apply(x);
x = keras.layers.Dense(units: 10).Apply(x);
var outputs = keras.layers.Softmax(axis: 1).Apply(x);
var model = keras.Model(inputs, outputs);

model.compile(new Adam(0.001f),
tf.keras.losses.SparseCategoricalCrossentropy(),
keras.losses.SparseCategoricalCrossentropy(),
new string[] { "accuracy" });

var data_loader = new MnistModelLoader();
Expand All @@ -37,7 +37,7 @@ public void SimpleModelFromAutoCompile()
{
TrainDir = "mnist",
OneHot = false,
ValidationSize = 10000,
ValidationSize = 58000,
}).Result;

model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
Expand Down Expand Up @@ -69,7 +69,7 @@ public void SimpleModelFromSequential()
{
TrainDir = "mnist",
OneHot = false,
ValidationSize = 50000,
ValidationSize = 58000,
}).Result;

model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
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