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Reduce the time of keras unittest. #999

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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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24 changes: 7 additions & 17 deletions test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelLoad.cs
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Original file line number Diff line number Diff line change
@@ -1,20 +1,10 @@
using Microsoft.VisualStudio.TestTools.UnitTesting;
using System;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using Tensorflow.Keras.Engine;
using Tensorflow.Keras.Saving.SavedModel;
using Tensorflow.Keras.Losses;
using Tensorflow.Keras.Metrics;
using Tensorflow;
using Tensorflow.Keras.Optimizers;
using static Tensorflow.KerasApi;
using Tensorflow.NumPy;
using Tensorflow.Keras.UnitTest.Helpers;
using static TensorFlowNET.Keras.UnitTest.SaveModel.SequentialModelSave;
using Tensorflow.NumPy;
using static Tensorflow.Binding;

namespace TensorFlowNET.Keras.UnitTest.SaveModel;

Expand All @@ -24,10 +14,10 @@ public class SequentialModelLoad
[TestMethod]
public void SimpleModelFromAutoCompile()
{
var model = keras.models.load_model(@"Assets/simple_model_from_auto_compile");
var model = tf.keras.models.load_model(@"Assets/simple_model_from_auto_compile");
model.summary();

model.compile(new Adam(0.0001f), new LossesApi().SparseCategoricalCrossentropy(), new string[] { "accuracy" });
model.compile(new Adam(0.0001f), tf.keras.losses.SparseCategoricalCrossentropy(), new string[] { "accuracy" });

// check the weights
var kernel1 = np.load(@"Assets/simple_model_from_auto_compile/kernel1.npy");
Expand All @@ -44,7 +34,7 @@ public void SimpleModelFromAutoCompile()
{
TrainDir = "mnist",
OneHot = false,
ValidationSize = 50000,
ValidationSize = 58000,
}).Result;

model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
Expand All @@ -54,10 +44,10 @@ public void SimpleModelFromAutoCompile()
public void AlexnetFromSequential()
{
new SequentialModelSave().AlexnetFromSequential();
var model = keras.models.load_model(@"./alexnet_from_sequential");
var model = tf.keras.models.load_model(@"./alexnet_from_sequential");
model.summary();

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

var num_epochs = 1;
var batch_size = 8;
Expand Down
70 changes: 34 additions & 36 deletions test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelSave.cs
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Original file line number Diff line number Diff line change
@@ -1,10 +1,8 @@
using Microsoft.VisualStudio.TestTools.UnitTesting;
using System.Collections.Generic;
using System.Diagnostics;
using Tensorflow;
using Tensorflow.Keras;
using Tensorflow.Keras.Engine;
using Tensorflow.Keras.Losses;
using Tensorflow.Keras.Optimizers;
using Tensorflow.Keras.UnitTest.Helpers;
using static Tensorflow.Binding;
Expand All @@ -25,8 +23,8 @@ public void SimpleModelFromAutoCompile()
var outputs = tf.keras.layers.Softmax(axis: 1).Apply(x);
var model = tf.keras.Model(inputs, outputs);

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

var data_loader = new MnistModelLoader();
Expand All @@ -37,7 +35,7 @@ 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 All @@ -48,18 +46,18 @@ public void SimpleModelFromAutoCompile()
[TestMethod]
public void SimpleModelFromSequential()
{
Model model = KerasApi.keras.Sequential(new List<ILayer>()
Model model = keras.Sequential(new List<ILayer>()
{
keras.layers.InputLayer((28, 28, 1)),
keras.layers.Flatten(),
keras.layers.Dense(100, "relu"),
keras.layers.Dense(10),
keras.layers.Softmax()
tf.keras.layers.InputLayer((28, 28, 1)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(100, "relu"),
tf.keras.layers.Dense(10),
tf.keras.layers.Softmax()
});

model.summary();

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

var data_loader = new MnistModelLoader();
var num_epochs = 1;
Expand All @@ -69,7 +67,7 @@ public void SimpleModelFromSequential()
{
TrainDir = "mnist",
OneHot = false,
ValidationSize = 50000,
ValidationSize = 58000,
}).Result;

model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
Expand All @@ -80,39 +78,39 @@ public void SimpleModelFromSequential()
[TestMethod]
public void AlexnetFromSequential()
{
Model model = KerasApi.keras.Sequential(new List<ILayer>()
Model model = keras.Sequential(new List<ILayer>()
{
keras.layers.InputLayer((227, 227, 3)),
keras.layers.Conv2D(96, (11, 11), (4, 4), activation:"relu", padding:"valid"),
keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((3, 3), strides:(2, 2)),
tf.keras.layers.InputLayer((227, 227, 3)),
tf.keras.layers.Conv2D(96, (11, 11), (4, 4), activation:"relu", padding:"valid"),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.MaxPooling2D((3, 3), strides:(2, 2)),

keras.layers.Conv2D(256, (5, 5), (1, 1), "same", activation: "relu"),
keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((3, 3), (2, 2)),
tf.keras.layers.Conv2D(256, (5, 5), (1, 1), "same", activation: "relu"),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.MaxPooling2D((3, 3), (2, 2)),

keras.layers.Conv2D(384, (3, 3), (1, 1), "same", activation: "relu"),
keras.layers.BatchNormalization(),
tf.keras.layers.Conv2D(384, (3, 3), (1, 1), "same", activation: "relu"),
tf.keras.layers.BatchNormalization(),

keras.layers.Conv2D(384, (3, 3), (1, 1), "same", activation: "relu"),
keras.layers.BatchNormalization(),
tf.keras.layers.Conv2D(384, (3, 3), (1, 1), "same", activation: "relu"),
tf.keras.layers.BatchNormalization(),

keras.layers.Conv2D(256, (3, 3), (1, 1), "same", activation: "relu"),
keras.layers.BatchNormalization(),
keras.layers.MaxPooling2D((3, 3), (2, 2)),
tf.keras.layers.Conv2D(256, (3, 3), (1, 1), "same", activation: "relu"),
tf.keras.layers.BatchNormalization(),
tf.keras.layers.MaxPooling2D((3, 3), (2, 2)),

keras.layers.Flatten(),
keras.layers.Dense(4096, activation: "relu"),
keras.layers.Dropout(0.5f),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(4096, activation: "relu"),
tf.keras.layers.Dropout(0.5f),

keras.layers.Dense(4096, activation: "relu"),
keras.layers.Dropout(0.5f),
tf.keras.layers.Dense(4096, activation: "relu"),
tf.keras.layers.Dropout(0.5f),

keras.layers.Dense(1000, activation: "linear"),
keras.layers.Softmax(1)
tf.keras.layers.Dense(1000, activation: "linear"),
tf.keras.layers.Softmax(1)
});

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

var num_epochs = 1;
var batch_size = 8;
Expand Down