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keras in seperate project.
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‎SciSharp STACK Examples.sln

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@@ -11,6 +11,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "SharpCV.Exmaples", "src\Sha
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Binding", "..\TensorFlow.NET\src\TensorFlowNET.Core\Tensorflow.Binding.csproj", "{F0DEC3F1-418F-46D4-8AC4-A0A49EB79EB4}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Keras", "..\TensorFlow.NET\src\TensorFlowNET.Keras\Tensorflow.Keras.csproj", "{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
@@ -123,6 +125,30 @@ Global
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{F0DEC3F1-418F-46D4-8AC4-A0A49EB79EB4}.Release|x64.Build.0 = Release|x64
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{F0DEC3F1-418F-46D4-8AC4-A0A49EB79EB4}.Release|x86.ActiveCfg = Release|Any CPU
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{F0DEC3F1-418F-46D4-8AC4-A0A49EB79EB4}.Release|x86.Build.0 = Release|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
129+
{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug|x64.ActiveCfg = Debug|x64
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug|x64.Build.0 = Debug|x64
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug|x86.ActiveCfg = Debug|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug|x86.Build.0 = Debug|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug-Minimal|Any CPU.Build.0 = Debug|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug-Minimal|x64.ActiveCfg = Debug|x64
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug-Minimal|x64.Build.0 = Debug|x64
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug-Minimal|x86.ActiveCfg = Debug|Any CPU
139+
{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Debug-Minimal|x86.Build.0 = Debug|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Publish|Any CPU.ActiveCfg = Release|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Publish|Any CPU.Build.0 = Release|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Publish|x64.ActiveCfg = Debug|x64
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Publish|x64.Build.0 = Debug|x64
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Publish|x86.ActiveCfg = Release|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Publish|x86.Build.0 = Release|Any CPU
146+
{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Release|Any CPU.ActiveCfg = Release|Any CPU
147+
{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Release|Any CPU.Build.0 = Release|Any CPU
148+
{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Release|x64.ActiveCfg = Release|x64
149+
{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Release|x64.Build.0 = Release|x64
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Release|x86.ActiveCfg = Release|Any CPU
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{520BE7D7-C0DC-4E92-A339-BDF6775E02A7}.Release|x86.Build.0 = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE

‎src/TensorFlowNET.Examples/BasicModels/LinearRegressionEager.cs

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@@ -16,6 +16,7 @@ limitations under the License.
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using NumSharp;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasExt;
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namespace TensorFlowNET.Examples
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{
@@ -64,7 +65,7 @@ public void RunModelInEagerMode()
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// var rnd2 = rng.randn<float>();
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var W = tf.Variable(-0.06f, name: "weight");
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var b = tf.Variable(-0.73f, name: "bias");
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var optimizer = tf.optimizers.SGD(learning_rate);
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var optimizer = keras.optimizers.SGD(learning_rate);
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// Run training for the given number of steps.
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foreach (var step in range(1, training_steps + 1))

‎src/TensorFlowNET.Examples/BasicModels/LogisticRegressionEager.cs

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@@ -20,6 +20,7 @@ limitations under the License.
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using System.IO;
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using Tensorflow;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasExt;
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namespace TensorFlowNET.Examples
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{
@@ -64,7 +65,7 @@ public bool Run()
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public void RunEagerMode()
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{
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// Prepare MNIST data.
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var ((x_train, y_train), (x_test, y_test)) = tf.keras.datasets.mnist.load_data();
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var ((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data();
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// Flatten images to 1-D vector of 784 features (28*28).
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(x_train, x_test) = (x_train.reshape((-1, num_features)), x_test.reshape((-1, num_features)));
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// Normalize images value from [0, 255] to [0, 1].
@@ -101,7 +102,7 @@ public void RunEagerMode()
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};
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// Stochastic gradient descent optimizer.
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var optimizer = tf.optimizers.SGD(learning_rate);
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var optimizer = keras.optimizers.SGD(learning_rate);
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Action<Tensor, Tensor> run_optimization = (x, y) =>
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{

‎src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCnnEager.cs

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@@ -19,6 +19,7 @@ limitations under the License.
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using Tensorflow;
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using Tensorflow.Keras.Optimizers;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasExt;
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namespace TensorFlowNET.Examples
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{
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bout = tf.Variable(tf.zeros(num_classes));
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// ADAM optimizer.
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var optimizer = tf.optimizers.Adam(learning_rate);
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var optimizer = keras.optimizers.Adam(learning_rate);
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// Run training for the given number of steps.
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foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1))
@@ -194,7 +195,7 @@ Tensor accuracy(Tensor y_pred, Tensor y_true)
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public override void PrepareData()
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{
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((x_train, y_train), (x_test, y_test)) = tf.keras.datasets.mnist.load_data();
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((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data();
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// Convert to float32.
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// (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32));
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// Normalize images value from [0, 255] to [0, 1].

‎src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs

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@@ -17,6 +17,7 @@ limitations under the License.
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using NumSharp;
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using Tensorflow;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasExt;
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namespace TensorFlowNET.Examples
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{
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y = tf.placeholder(tf.int32, new[] { -1 });
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var cell = tf.nn.rnn_cell.BasicRNNCell(num_units: n_neurons);
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var (output, state) = tf.nn.dynamic_rnn(cell, X, dtype: tf.float32);
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var logits = tf.layers.dense(state, n_outputs);
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var logits = keras.layers.dense(state, n_outputs);
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var cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels: y, logits: logits);
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loss = tf.reduce_mean(cross_entropy);
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var adam = tf.train.AdamOptimizer(learning_rate: learning_rate);

‎src/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRnnKeras.cs

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@@ -21,6 +21,7 @@ limitations under the License.
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Optimizers;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasExt;
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namespace TensorFlowNET.Examples
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{
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public override void PrepareData()
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{
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((x_train, y_train), (x_test, y_test)) = tf.keras.datasets.mnist.load_data();
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((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data();
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// Convert to float32.
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// (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32));
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// Normalize images value from [0, 255] to [0, 1].
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public LSTMModel(LSTMModelArgs args)
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: base(args)
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{
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optimizer = tf.optimizers.Adam(args.LearningRate);
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optimizer = keras.optimizers.Adam(args.LearningRate);
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var layers = tf.keras.layers;
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var layers = keras.layers;
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lstm = layers.LSTM(args.NumUnits);
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output = layers.Dense(args.NumClasses);
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}

‎src/TensorFlowNET.Examples/ImageProcessing/ImageClassificationKeras.cs

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using System.Collections.Generic;
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using Tensorflow;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasExt;
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namespace TensorFlowNET.Examples
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{
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TensorShape img_dim = (180, 180);
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var data_dir = @"C:/Users/haipi/.keras/datasets/flower_photos";
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var train_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir,
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var train_ds = keras.preprocessing.image_dataset_from_directory(data_dir,
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validation_split: 0.2f,
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subset: "training",
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seed: 123,
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image_size: img_dim,
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batch_size: batch_size);
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var val_ds = tf.keras.preprocessing.image_dataset_from_directory(data_dir,
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var val_ds = keras.preprocessing.image_dataset_from_directory(data_dir,
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validation_split: 0.2f,
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subset: "validation",
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seed: 123,
@@ -56,22 +57,22 @@ public override void PrepareData()
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int num_classes = 5;
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// var normalization_layer = tf.keras.layers.Rescaling(1.0f / 255);
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var layers = tf.keras.layers;
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var model = tf.keras.Sequential(new List<Layer>
60+
var layers = keras.layers;
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var model = keras.Sequential(new List<ILayer>
6162
{
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layers.Rescaling(1.0f / 255, input_shape: (img_dim.dims[0], img_dim.dims[1], 3)),
63-
layers.Conv2D(16, 3, padding: "same", activation: tf.keras.activations.Relu),
64+
layers.Conv2D(16, 3, padding: "same", activation: keras.activations.Relu),
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layers.MaxPooling2D(),
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/*layers.Conv2D(32, 3, padding: "same", activation: "relu"),
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layers.MaxPooling2D(),
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layers.Conv2D(64, 3, padding: "same", activation: "relu"),
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layers.MaxPooling2D(),*/
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layers.Flatten(),
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layers.Dense(128, activation: tf.keras.activations.Relu),
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layers.Dense(128, activation: keras.activations.Relu),
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layers.Dense(num_classes)
7273
});
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model.compile("adam", tf.keras.losses.SparseCategoricalCrossentropy(from_logits: true));
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model.compile("adam", keras.losses.SparseCategoricalCrossentropy(from_logits: true));
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}
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}
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‎src/TensorFlowNET.Examples/ImageProcessing/MnistCnnKerasSubclass .cs

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@@ -21,6 +21,7 @@ limitations under the License.
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Optimizers;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasExt;
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namespace TensorFlowNET.Examples
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{
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6768
});
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// ADAM optimizer.
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var optimizer = tf.optimizers.Adam(learning_rate);
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var optimizer = keras.optimizers.Adam(learning_rate);
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// Run training for the given number of steps.
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foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1))
@@ -144,7 +145,7 @@ Tensor accuracy(Tensor y_pred, Tensor y_true)
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145146
public override void PrepareData()
146147
{
147-
((x_train, y_train), (x_test, y_test)) = tf.keras.datasets.mnist.load_data();
148+
((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data();
148149
// Convert to float32.
149150
// (x_train, x_test) = (np.array(x_train, np.float32), np.array(x_test, np.float32));
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// Normalize images value from [0, 255] to [0, 1].
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172173
public ConvNet(ConvNetArgs args)
173174
: base(args)
174175
{
175-
var layers = tf.keras.layers;
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var layers = keras.layers;
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// Convolution Layer with 32 filters and a kernel size of 5.
178-
conv1 = layers.Conv2D(32, kernel_size: 5, activation: tf.keras.activations.Relu);
179+
conv1 = layers.Conv2D(32, kernel_size: 5, activation: keras.activations.Relu);
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180181
// Max Pooling (down-sampling) with kernel size of 2 and strides of 2.
181182
maxpool1 = layers.MaxPooling2D(2, strides: 2);
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183184
// Convolution Layer with 64 filters and a kernel size of 3.
184-
conv2 = layers.Conv2D(64, kernel_size: 3, activation: tf.keras.activations.Relu);
185+
conv2 = layers.Conv2D(64, kernel_size: 3, activation: keras.activations.Relu);
185186
// Max Pooling (down-sampling) with kernel size of 2 and strides of 2.
186187
maxpool2 = layers.MaxPooling2D(2, strides: 2);
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‎src/TensorFlowNET.Examples/ImageProcessing/MnistFnnKerasFunctional.cs

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@@ -15,10 +15,9 @@ limitations under the License.
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******************************************************************************/
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using NumSharp;
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using System;
19-
using Tensorflow;
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using Tensorflow.Keras.Engine;
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using static Tensorflow.Binding;
20+
using static Tensorflow.KerasExt;
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2322
namespace TensorFlowNET.Examples
2423
{
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6362
var inputs = keras.Input(shape: 784);
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6564
// 1st dense layer
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var outputs = layers.Dense(64, activation: tf.keras.activations.Relu).Apply(inputs);
65+
var outputs = layers.Dense(64, activation: keras.activations.Relu).Apply(inputs);
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6867
// 2nd dense layer
69-
outputs = layers.Dense(64, activation: tf.keras.activations.Relu).Apply(outputs);
68+
outputs = layers.Dense(64, activation: keras.activations.Relu).Apply(outputs);
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// output layer
7271
outputs = layers.Dense(10).Apply(outputs);

‎src/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedEager.cs

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@@ -19,6 +19,7 @@ limitations under the License.
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using Tensorflow;
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using Tensorflow.Keras.Optimizers;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasExt;
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2324
namespace TensorFlowNET.Examples
2425
{
@@ -74,7 +75,7 @@ public bool Run()
7475
var trainable_variables = new IVariableV1[] { h1, h2, wout, b1, b2, bout };
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7677
// Stochastic gradient descent optimizer.
77-
var optimizer = tf.optimizers.SGD(learning_rate);
78+
var optimizer = keras.optimizers.SGD(learning_rate);
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7980
// Run training for the given number of steps.
8081
foreach (var (step, (batch_x, batch_y)) in enumerate(train_data, 1))
@@ -157,7 +158,7 @@ Tensor neural_net(Tensor x)
157158
public override void PrepareData()
158159
{
159160
// Prepare MNIST data.
160-
((x_train, y_train), (x_test, y_test)) = tf.keras.datasets.mnist.load_data();
161+
((x_train, y_train), (x_test, y_test)) = keras.datasets.mnist.load_data();
161162
// Flatten images to 1-D vector of 784 features (28*28).
162163
(x_train, x_test) = (x_train.reshape((-1, num_features)), x_test.reshape((-1, num_features)));
163164
// Normalize images value from [0, 255] to [0, 1].

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