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Added all keras activations · feelsyt/TensorFlow.NET@074d06d · GitHub

/ TensorFlow.NET Public
forked from SciSharp/TensorFlow.NET

Commit 074d06d

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Added all keras activations
1 parent f7ef39c commit 074d06d

4 files changed

Lines changed: 229 additions & 10 deletions

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‎src/TensorFlowNET.Core/APIs/tf.layers.cs‎

Lines changed: 16 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -144,6 +144,20 @@ public Tensor max_pooling2d(Tensor inputs,
144144
return layer.apply(inputs);
145145
}
146146

147+
/// <summary>
148+
/// Densely-connected layer class. aka fully-connected<br></br>
149+
/// `outputs = activation(inputs * kernel + bias)`
150+
/// </summary>
151+
/// <param name="inputs"></param>
152+
/// <param name="units">Python integer, dimensionality of the output space.</param>
153+
/// <param name="activation"></param>
154+
/// <param name="use_bias">Boolean, whether the layer uses a bias.</param>
155+
/// <param name="kernel_initializer"></param>
156+
/// <param name="bias_initializer"></param>
157+
/// <param name="trainable"></param>
158+
/// <param name="name"></param>
159+
/// <param name="reuse"></param>
160+
/// <returns></returns>
147161
public Tensor dense(Tensor inputs,
148162
int units,
149163
IActivation activation = null,
@@ -160,7 +174,8 @@ public Tensor dense(Tensor inputs,
160174
var layer = new Dense(units, activation,
161175
use_bias: use_bias,
162176
bias_initializer: bias_initializer,
163-
kernel_initializer: kernel_initializer);
177+
kernel_initializer: kernel_initializer,
178+
trainable: trainable);
164179

165180
return layer.apply(inputs);
166181
}

‎src/TensorFlowNET.Core/APIs/tf.math.cs‎

Lines changed: 32 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -14,6 +14,8 @@ You may obtain a copy of the License at
1414
limitations under the License.
1515
******************************************************************************/
1616

17+
using Tensorflow.Operations;
18+
1719
namespace Tensorflow
1820
{
1921
public partial class tensorflow
@@ -211,6 +213,36 @@ public Tensor logical_xor(Tensor x, Tensor y, string name = "LogicalXor")
211213
/// <returns></returns>
212214
public Tensor _clip_by_value(Tensor t, Tensor clip_value_min, Tensor clip_value_max, string name = null)
213215
=> gen_math_ops._clip_by_value(t, clip_value_min, clip_value_max);
216+
217+
/// <summary>
218+
/// Clips tensor values to a specified min and max.
219+
/// </summary>
220+
/// <param name="t">
221+
/// A <c>Tensor</c>.
222+
/// </param>
223+
/// <param name="clip_value_min">
224+
/// A 0-D (scalar) <c>Tensor</c>, or a <c>Tensor</c> with the same shape
225+
/// as <c>t</c>. The minimum value to clip by.
226+
/// </param>
227+
/// <param name="clip_value_max">
228+
/// A 0-D (scalar) <c>Tensor</c>, or a <c>Tensor</c> with the same shape
229+
/// as <c>t</c>. The maximum value to clip by.
230+
/// </param>
231+
/// <param name="name">
232+
/// If specified, the created operation in the graph will be this one, otherwise it will be named 'ClipByValue'.
233+
/// </param>
234+
/// <returns>
235+
/// A clipped <c>Tensor</c> with the same shape as input 't'.
236+
/// The Operation can be fetched from the resulting Tensor, by fetching the Operation property from the result.
237+
/// </returns>
238+
/// <remarks>
239+
/// Given a tensor <c>t</c>, this operation returns a tensor of the same type and
240+
/// shape as <c>t</c> with its values clipped to <c>clip_value_min</c> and <c>clip_value_max</c>.
241+
/// Any values less than <c>clip_value_min</c> are set to <c>clip_value_min</c>. Any values
242+
/// greater than <c>clip_value_max</c> are set to <c>clip_value_max</c>.
243+
/// </remarks>
244+
public Tensor clip_by_value (Tensor t, Tensor clip_value_min, Tensor clip_value_max, string name = "ClipByValue")
245+
=> gen_ops.clip_by_value(t, clip_value_min, clip_value_max, name);
214246

215247
public Tensor sub(Tensor a, Tensor b)
216248
=> gen_math_ops.sub(a, b);

‎src/TensorFlowNET.Core/APIs/tf.tensor.cs‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -18,8 +18,8 @@ namespace Tensorflow
1818
{
1919
public partial class tensorflow
2020
{
21-
public Tensor convert_to_tensor(object value,
22-
string name = null) => ops.convert_to_tensor(value, name: name);
21+
public Tensor convert_to_tensor(object value, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, TF_DataType preferred_dtype = TF_DataType.DtInvalid)
22+
=> ops.convert_to_tensor(value, dtype, name, preferred_dtype);
2323

2424
public Tensor strided_slice(Tensor input, Tensor begin, Tensor end, Tensor strides = null,
2525
int begin_mask = 0,

‎src/TensorFlowNET.Core/Operations/Activation/gen_nn_ops.activations.cs‎

Lines changed: 179 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -14,20 +14,192 @@ You may obtain a copy of the License at
1414
limitations under the License.
1515
******************************************************************************/
1616

17+
using System;
18+
using static Tensorflow.Binding;
19+
1720
namespace Tensorflow.Operations.Activation
1821
{
22+
public class sigmoid : IActivation
23+
{
24+
public Tensor Activate(Tensor x, string name = null)
25+
{
26+
return tf.sigmoid(x);
27+
}
28+
}
29+
30+
public class tanh : IActivation
31+
{
32+
public Tensor Activate(Tensor x, string name = null)
33+
{
34+
return tf.tanh(x);
35+
}
36+
}
37+
38+
public class leakyrelu : IActivation
39+
{
40+
private readonly float _alpha;
41+
42+
public leakyrelu(float alpha = 0.3f) {
43+
_alpha = alpha;
44+
}
45+
46+
public Tensor Activate(Tensor x, string name = null)
47+
{
48+
return nn_ops.leaky_relu(x, _alpha);
49+
}
50+
}
51+
52+
public class elu : IActivation
53+
{
54+
private readonly float _alpha;
55+
56+
public elu(float alpha = 0.1f)
57+
{
58+
_alpha = alpha;
59+
}
60+
61+
public Tensor Activate(Tensor x, string name = null)
62+
{
63+
var res = gen_ops.elu(x);
64+
if (Math.Abs(_alpha - 0.1f) < 0.00001f)
65+
{
66+
return res;
67+
}
68+
69+
return array_ops.@where(x > 0, res, _alpha * res);
70+
}
71+
}
72+
73+
public class softmax : IActivation
74+
{
75+
private readonly int _axis;
76+
77+
/// <summary>Initializes a new instance of the <see cref="T:System.Object"></see> class.</summary>
78+
public softmax(int axis = -1)
79+
{
80+
_axis = axis;
81+
}
82+
83+
public Tensor Activate(Tensor x, string name = null)
84+
{
85+
return nn_ops.softmax(x, _axis);
86+
}
87+
}
88+
89+
public class softplus : IActivation
90+
{
91+
public Tensor Activate(Tensor x, string name = null)
92+
{
93+
return gen_ops.softplus(x);
94+
}
95+
}
96+
97+
public class softsign : IActivation
98+
{
99+
public Tensor Activate(Tensor x, string name = null)
100+
{
101+
return gen_ops.softsign(x);
102+
}
103+
}
104+
105+
public class linear : IActivation
106+
{
107+
public Tensor Activate(Tensor x, string name = null)
108+
{
109+
return x;
110+
}
111+
}
112+
113+
114+
public class exponential : IActivation
115+
{
116+
public Tensor Activate(Tensor x, string name = null)
117+
{
118+
return tf.exp(x, name: name);
119+
}
120+
}
121+
122+
19123
public class relu : IActivation
20124
{
21-
public Tensor Activate(Tensor features, string name = null)
125+
private readonly float _threshold;
126+
private readonly float _alpha;
127+
private readonly float? _maxValue;
128+
129+
public relu(float threshold = 0f, float alpha = 0.2f, float? max_value = null)
130+
{
131+
_threshold = threshold;
132+
_alpha = alpha;
133+
_maxValue = max_value;
134+
}
135+
136+
public Tensor Activate(Tensor x, string name = null)
22137
{
23-
OpDefLibrary _op_def_lib = new OpDefLibrary();
138+
//based on keras/backend.py
139+
if (Math.Abs(_alpha) > 0.000001f)
140+
{
141+
if (!_maxValue.HasValue && Math.Abs(_threshold) < 0.0001)
142+
{
143+
return nn_ops.leaky_relu(x, _alpha);
144+
}
145+
}
146+
147+
Tensor negative_part;
148+
if (Math.Abs(_threshold) > 0.000001f)
149+
{
150+
negative_part = gen_ops.relu(-x + _threshold);
151+
} else
152+
{
153+
negative_part = gen_ops.relu(-x + _threshold);
154+
}
155+
156+
if (Math.Abs(_threshold) > 0.000001f)
157+
{
158+
x = x * math_ops.cast(tf.greater(x, _threshold), TF_DataType.TF_FLOAT);
159+
} else if (Math.Abs(_maxValue.Value - 6f) < 0.0001f)
160+
{
161+
x = gen_ops.relu6(x);
162+
} else
163+
{
164+
x = gen_ops.relu(x);
165+
}
166+
167+
bool clip_max = _maxValue.HasValue;
168+
if (clip_max)
169+
{
170+
Tensor maxval = constant_op.constant(_maxValue, x.dtype.as_base_dtype());
171+
var zero = constant_op.constant(0.0f, x.dtype.as_base_dtype());
172+
x = gen_ops.clip_by_value(x, zero, maxval);
173+
}
24174

25-
var _op = _op_def_lib._apply_op_helper("Relu", name: name, args: new
175+
if (Math.Abs(_alpha) > 0.00001)
26176
{
27-
features
28-
});
177+
var a = constant_op.constant(_alpha, x.dtype.as_base_dtype());
178+
x -= a * negative_part;
179+
}
29180

30-
return _op.outputs[0];
181+
return x;
182+
}
183+
}
184+
185+
public class selu : IActivation
186+
{
187+
public Tensor Activate(Tensor x, string name = null)
188+
{
189+
const float alpha = 1.6732632423543772848170429916717f;
190+
const float scale = 1.0507009873554804934193349852946f;
191+
return scale * new elu(alpha).Activate(x, name);
192+
}
193+
}
194+
195+
public class hard_sigmoid : IActivation
196+
{
197+
public Tensor Activate(Tensor x, string name = null)
198+
{
199+
x = (0.2 * x) + 0.5;
200+
var zero = tf.convert_to_tensor(0.0f, x.dtype.as_base_dtype());
201+
var one = tf.convert_to_tensor(1.0f, x.dtype.as_base_dtype());
202+
return tf.clip_by_value(x, zero, one);
31203
}
32204
}
33-
}
205+
}

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