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Fix issue #760 #992

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Merged
merged 1 commit into from
Mar 3, 2023
Merged

Fix issue #760 #992

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2 changes: 1 addition & 1 deletion src/TensorFlowNET.Core/Operations/gen_array_ops.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -269,7 +269,7 @@ public static (Tensor, Tensor) unique(Tensor x, TF_DataType out_idx = TF_DataTyp

public static Tensor[] unpack(Tensor value, int num, int axis = 0, string name = null)
=> tf.Context.ExecuteOp("Unpack", name, new ExecuteOpArgs(value, num)
.SetAttributes(new { axis }));
.SetAttributes(new { axis, num }));

public static Tensor where(Tensor condition, string name = null)
{
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76 changes: 76 additions & 0 deletions test/TensorFlowNET.Keras.UnitTest/Gradient.cs
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Original file line number Diff line number Diff line change
@@ -0,0 +1,76 @@
using Microsoft.VisualStudio.TestTools.UnitTesting;
using System.Linq;
using Tensorflow;
using Tensorflow.Keras.Engine;
using static Tensorflow.Binding;
using static Tensorflow.KerasApi;
using Tensorflow.NumPy;

namespace TensorFlowNET.Keras.UnitTest;

[TestClass]
public class GradientTest
{
public Model get_actor(int num_states)
{
var inputs = keras.layers.Input(shape: num_states);
var outputs = keras.layers.Dense(1, activation: keras.activations.Tanh).Apply(inputs);

Model model = keras.Model(inputs, outputs);

return model;
}

public Model get_critic(int num_states, int num_actions)
{
// State as input
var state_input = keras.layers.Input(shape: num_states);

// Action as input
var action_input = keras.layers.Input(shape: num_actions);

var concat = keras.layers.Concatenate(axis: 1).Apply(new Tensors(state_input, action_input));

var outputs = keras.layers.Dense(1).Apply(concat);

Model model = keras.Model(new Tensors(state_input, action_input), outputs);
model.summary();

return model;
}

[TestMethod]
public void GetGradient_Test()
{
var numStates = 3;
var numActions = 1;
var batchSize = 64;
var gamma = 0.99f;

var target_actor_model = get_actor(numStates);
var target_critic_model = get_critic(numStates, numActions);
var critic_model = get_critic(numStates, numActions);

Tensor state_batch = tf.convert_to_tensor(np.zeros((batchSize, numStates)), TF_DataType.TF_FLOAT);
Tensor action_batch = tf.convert_to_tensor(np.zeros((batchSize, numActions)), TF_DataType.TF_FLOAT);
Tensor reward_batch = tf.convert_to_tensor(np.zeros((batchSize, 1)), TF_DataType.TF_FLOAT);
Tensor next_state_batch = tf.convert_to_tensor(np.zeros((batchSize, numStates)), TF_DataType.TF_FLOAT);

using (var tape = tf.GradientTape())
{
var target_actions = target_actor_model.Apply(next_state_batch, training: true);
var target_critic_value = target_critic_model.Apply(new Tensors(new Tensor[] { next_state_batch, target_actions }), training: true);

var y = reward_batch + tf.multiply(gamma, target_critic_value);

var critic_value = critic_model.Apply(new Tensors(new Tensor[] { state_batch, action_batch }), training: true);

var critic_loss = math_ops.reduce_mean(math_ops.square(y - critic_value));

var critic_grad = tape.gradient(critic_loss, critic_model.TrainableVariables);

Assert.IsNotNull(critic_grad);
Assert.IsNotNull(critic_grad.First());
}
}
}