TensorFlow.NET/src/KerasNET.Core/Model.cs at master · lokinfey/TensorFlow.NET · GitHub

History

127 lines (106 loc) · 3.85 KB

Raw

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

using Keras.Layers;

using NumSharp;

using System;

using System.Collections.Generic;

using System.Text;

using Tensorflow;

using static Keras.Keras;

using static Tensorflow.Python;

namespace Keras

{

public class Model

{

public Tensor Flow;

List<ILayer> layer_stack;

public TensorShape InputShape;

public Model()

{

layer_stack = new List<ILayer>();

}

public Model Add(ILayer layer)

{

layer_stack.Add(layer);

return this;

}

public Model Add(IEnumerable<ILayer> layers)

{

layer_stack.AddRange(layers);

return this;

}

public Tensor getFlow()

{

try

{

return Flow;

}

catch (Exception ex)

{

return null;

}

public (Operation, Tensor, Tensor) make_graph(Tensor features, Tensor labels)

{

// TODO : Creating Loss Functions And Optimizers.....

#region Model Layers Graph

var stddev = 1 / Math.Sqrt(2);

var d1 = new Dense(num_hidden);

d1.__build__(features.getShape());

var hidden_activations = tf.nn.relu(d1.__call__(features));

var d1_output = d1.output_shape(features.getShape());

var d2 = new Dense(1);

d2.__build__(d1.output_shape(features.getShape()), seed: 17, stddev: (float)(1/ Math.Sqrt(num_hidden)));

var logits = d2.__call__(hidden_activations);

var predictions = tf.sigmoid(tf.squeeze(logits));

#endregion

#region Model Graph Form Layer Stack

var flow_shape = features.TensorShape;

Flow = features;

for (int i = 0; i < layer_stack.Count; i++)

{

layer_stack[i].__build__(flow_shape);

flow_shape = layer_stack[i].output_shape(flow_shape);

Flow = layer_stack[i].__call__(Flow);

}

var predictions = tf.sigmoid(tf.squeeze(Flow));

#endregion

#region loss and optimizer

var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss");

var gs = tf.Variable(0, trainable: false, name: "global_step");

var train_op = tf.train.GradientDescentOptimizer(0.2f).minimize(loss, global_step: gs);

#endregion

return (train_op, loss, gs);

}

public float train(int num_steps, (NDArray, NDArray) training_dataset)

{

var (X, Y) = training_dataset;

var x_shape = X.shape;

var batch_size = x_shape[0];

var graph = tf.Graph().as_default();

var features = tf.placeholder(tf.float32, new TensorShape(batch_size, 2));

var labels = tf.placeholder(tf.float32, new TensorShape(batch_size));

var (train_op, loss, gs) = this.make_graph(features, labels);

var init = tf.global_variables_initializer();

float loss_value = 0;

with(tf.Session(graph), sess =>

{

sess.run(init);

var step = 0;

while (step < num_steps)

{

var result = sess.run(

new ITensorOrOperation[] { train_op, gs, loss },

new FeedItem(features, X),

new FeedItem(labels, Y));

loss_value = result[2];

step = result[1];

if (step % 1000 == 0)

Console.WriteLine($"Step {step} loss: {loss_value}");

}

Console.WriteLine($"Final loss: {loss_value}");

});

return loss_value;

}

TensorFlow.NET/src/KerasNET.Core/Model.cs at master · lokinfey/TensorFlow.NET · GitHub

Expand file tree

Breadcrumbs

Model.cs

Latest commit

History

Breadcrumbs

Model.cs

File metadata and controls