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Operator overloading between Variable and Tensor should return Tensor.
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Lines changed: 63 additions & 91 deletions

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‎README.md‎

Lines changed: 28 additions & 49 deletions
Original file line numberDiff line numberDiff line change
@@ -11,8 +11,6 @@
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*master branch is based on tensorflow 2.2 now, v0.15-tensorflow1.15 is from tensorflow1.15.*
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TF.NET is a member project of [SciSharp STACK](https://github.com/SciSharp).
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![tensors_flowing](docs/assets/tensors_flowing.gif)
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@@ -56,59 +54,40 @@ using static Tensorflow.Binding;
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Linear Regression:
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```c#
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// We can set a fixed init value in order to debug
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// Parameters
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int training_steps = 1000;
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float learning_rate = 0.01f;
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int display_step = 100;
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// We can set a fixed init value in order to demo
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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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// Construct a linear model
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var pred = tf.add(tf.multiply(X, W), b);
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// Mean squared error
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var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);
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// Gradient descent
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// Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
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var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
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// Initialize the variables (i.e. assign their default value)
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var init = tf.global_variables_initializer();
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// Start training
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using(tf.Session())
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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))
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{
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// Run the initializer
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sess.run(init);
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// Fit all training data
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for (int epoch = 0; epoch < training_epochs; epoch++)
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// Run the optimization to update W and b values.
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// Wrap computation inside a GradientTape for automatic differentiation.
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using var g = tf.GradientTape();
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// Linear regression (Wx + b).
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var pred = W * X + b;
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// Mean square error.
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var loss = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples);
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// should stop recording
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// Compute gradients.
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var gradients = g.gradient(loss, (W, b));
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// Update W and b following gradients.
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optimizer.apply_gradients(zip(gradients, (W, b)));
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if (step % display_step == 0)
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{
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foreach (var (x, y) in zip<float>(train_X, train_Y))
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sess.run(optimizer, (X, x), (Y, y));
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// Display logs per epoch step
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if ((epoch + 1) % display_step == 0)
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{
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var c = sess.run(cost, (X, train_X), (Y, train_Y));
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Console.WriteLine($"Epoch: {epoch + 1} cost={c} " + $"W={sess.run(W)} b={sess.run(b)}");
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}
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pred = W * X + b;
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loss = tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * n_samples);
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print($"step: {step}, loss: {loss.numpy()}, W: {W.numpy()}, b: {b.numpy()}");
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}
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Console.WriteLine("Optimization Finished!");
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var training_cost = sess.run(cost, (X, train_X), (Y, train_Y));
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Console.WriteLine($"Training cost={training_cost} W={sess.run(W)} b={sess.run(b)}");
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// Testing example
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var test_X = np.array(6.83f, 4.668f, 8.9f, 7.91f, 5.7f, 8.7f, 3.1f, 2.1f);
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var test_Y = np.array(1.84f, 2.273f, 3.2f, 2.831f, 2.92f, 3.24f, 1.35f, 1.03f);
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Console.WriteLine("Testing... (Mean square loss Comparison)");
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var testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * test_X.shape[0]),
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(X, test_X), (Y, test_Y));
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Console.WriteLine($"Testing cost={testing_cost}");
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var diff = Math.Abs((float)training_cost - (float)testing_cost);
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Console.WriteLine($"Absolute mean square loss difference: {diff}");
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return diff < 0.01;
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});
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}
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```
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Run this example in [Jupyter Notebook](https://github.com/SciSharp/SciSharpCube).

‎src/TensorFlowNET.Core/Operations/gen_math_ops.cs‎

Lines changed: 13 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -551,6 +551,19 @@ public static Tensor _clip_by_value(Tensor t, Tensor clip_value_min, Tensor clip
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public static Tensor greater<Tx, Ty>(Tx x, Ty y, string name = null)
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{
554+
if (tf.context.executing_eagerly())
555+
{
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var results = EagerTensorPass.Create();
557+
var inputs = EagerTensorPass.From(x, y);
558+
Status status = c_api.TFE_FastPathExecute(tf.context, tf.context.device_name,
559+
"Greater", name,
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inputs.Points, inputs.Length,
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null, null,
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results.Points, results.Length);
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status.Check(true);
564+
return results[0].Resolve();
565+
}
566+
554567
var _op = _op_def_lib._apply_op_helper("Greater", name: name, args: new { x, y });
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556569
return _op.outputs[0];

‎src/TensorFlowNET.Core/Variables/ResourceVariable.Operators.cs‎

Lines changed: 21 additions & 41 deletions
Original file line numberDiff line numberDiff line change
@@ -24,24 +24,24 @@ public partial class ResourceVariable
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{
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public static OpDefLibrary _op_def_lib = new OpDefLibrary();
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27-
public static ResourceVariable operator +(ResourceVariable x, int y) => op_helper("add", x, y);
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public static ResourceVariable operator +(ResourceVariable x, float y) => op_helper("add", x, y);
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public static ResourceVariable operator +(ResourceVariable x, double y) => op_helper("add", x, y);
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public static ResourceVariable operator +(ResourceVariable x, ResourceVariable y) => op_helper("add", x, y);
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public static ResourceVariable operator -(ResourceVariable x, int y) => op_helper("sub", x, y);
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public static ResourceVariable operator -(ResourceVariable x, float y) => op_helper("sub", x, y);
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public static ResourceVariable operator -(ResourceVariable x, double y) => op_helper("sub", x, y);
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public static ResourceVariable operator -(ResourceVariable x, Tensor y) => op_helper("sub", x, y);
35-
public static ResourceVariable operator -(ResourceVariable x, ResourceVariable y) => op_helper("sub", x, y);
27+
public static Tensor operator +(ResourceVariable x, int y) => op_helper("add", x, y);
28+
public static Tensor operator +(ResourceVariable x, float y) => op_helper("add", x, y);
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public static Tensor operator +(ResourceVariable x, double y) => op_helper("add", x, y);
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public static Tensor operator +(ResourceVariable x, ResourceVariable y) => op_helper("add", x, y);
31+
public static Tensor operator -(ResourceVariable x, int y) => op_helper("sub", x, y);
32+
public static Tensor operator -(ResourceVariable x, float y) => op_helper("sub", x, y);
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public static Tensor operator -(ResourceVariable x, double y) => op_helper("sub", x, y);
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public static Tensor operator -(ResourceVariable x, Tensor y) => op_helper("sub", x, y);
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public static Tensor operator -(ResourceVariable x, ResourceVariable y) => op_helper("sub", x, y);
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37-
public static ResourceVariable operator *(ResourceVariable x, ResourceVariable y) => op_helper("mul", x, y);
38-
public static ResourceVariable operator *(ResourceVariable x, NDArray y) => op_helper("mul", x, y);
37+
public static Tensor operator *(ResourceVariable x, ResourceVariable y) => op_helper("mul", x, y);
38+
public static Tensor operator *(ResourceVariable x, NDArray y) => op_helper("mul", x, y);
3939

40-
public static ResourceVariable operator <(ResourceVariable x, Tensor y) => less(x.value(), y);
40+
public static Tensor operator <(ResourceVariable x, Tensor y) => op_helper("less", x, y);
4141

42-
public static ResourceVariable operator >(ResourceVariable x, Tensor y) => greater(x.value(), y);
42+
public static Tensor operator >(ResourceVariable x, Tensor y) => op_helper("greater", x, y);
4343

44-
private static ResourceVariable op_helper<T>(string default_name, ResourceVariable x, T y)
44+
private static Tensor op_helper<T>(string default_name, ResourceVariable x, T y)
4545
=> tf_with(ops.name_scope(null, default_name, new { x, y }), scope =>
4646
{
4747
string name = scope;
@@ -61,39 +61,19 @@ private static ResourceVariable op_helper<T>(string default_name, ResourceVariab
6161
case "mul":
6262
result = gen_math_ops.mul(xVal, yTensor, name: name);
6363
break;
64+
case "less":
65+
result = gen_math_ops.less(xVal, yTensor, name);
66+
break;
67+
case "greater":
68+
result = gen_math_ops.greater(xVal, yTensor, name);
69+
break;
6470
default:
6571
throw new NotImplementedException("");
6672
}
6773

6874
// x.assign(result);
6975
// result.ResourceVar = x;
70-
return tf.Variable(result);
76+
return result;
7177
});
72-
73-
private static ResourceVariable less<Tx, Ty>(Tx x, Ty y, string name = null)
74-
{
75-
if (tf.context.executing_eagerly())
76-
{
77-
var results = EagerTensorPass.Create();
78-
var inputs = EagerTensorPass.From(x, y);
79-
Status status = c_api.TFE_FastPathExecute(tf.context, tf.context.device_name,
80-
"Less", name,
81-
inputs.Points, inputs.Length,
82-
null, null,
83-
results.Points, results.Length);
84-
status.Check(true);
85-
return tf.Variable(results[0].Resolve());
86-
}
87-
88-
var _op = _op_def_lib._apply_op_helper("Less", name: name, args: new { x, y });
89-
90-
return tf.Variable(_op.outputs[0]);
91-
}
92-
private static ResourceVariable greater<Tx, Ty>(Tx x, Ty y, string name = null)
93-
{
94-
var _op = _op_def_lib._apply_op_helper("Greater", name: name, args: new { x, y });
95-
96-
return tf.Variable(_op.outputs[0]);
97-
}
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}
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}

‎test/TensorFlowNET.UnitTest/Basics/VariableTest.cs‎

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -57,7 +57,7 @@ public void Accumulation()
5757
{
5858
var x = tf.Variable(10, name: "x");
5959
for (int i = 0; i < 5; i++)
60-
x = x + 1;
60+
x.assign(x + 1);
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Assert.AreEqual(15, (int)x.numpy());
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}

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