TensorFlow.NET/test/TensorFlowNET.Examples/TextProcess/cnn_models/WordCnn.cs

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using Tensorflow;
using static Tensorflow.Python;

namespace TensorFlowNET.Examples.Text
{
    public class WordCnn : ITextModel
    {
        public WordCnn(int vocabulary_size, int document_max_len, int num_class)
        {
            var embedding_size = 128;
            var learning_rate = 0.001f;
            var filter_sizes = new int[3, 4, 5];
            var num_filters = 100;

            var x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");
            var y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y");
            var is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training");
            var global_step = tf.Variable(0, trainable: false);
            var keep_prob = tf.where(is_training, 0.5f, 1.0f);
            Tensor x_emb = null;

            with(tf.name_scope("embedding"), scope =>
            {
                var init_embeddings = tf.random_uniform(new int[] { vocabulary_size, embedding_size });
                var embeddings = tf.get_variable("embeddings", initializer: init_embeddings);
                x_emb = tf.nn.embedding_lookup(embeddings, x);
                x_emb = tf.expand_dims(x_emb, -1);
            });

            var pooled_outputs = new List<Tensor>();
            for (int len = 0; len < filter_sizes.Rank; len++)
            {
                int filter_size = filter_sizes.GetLength(len);
                var conv = tf.layers.conv2d(
                    x_emb,
                    filters: num_filters,
                    kernel_size: new int[] { filter_size, embedding_size },
                    strides: new int[] { 1, 1 },
                    padding: "VALID",
                    activation: tf.nn.relu());

                var pool = tf.layers.max_pooling2d(
                    conv,
                    pool_size: new[] { document_max_len - filter_size + 1, 1 },
                    strides: new[] { 1, 1 },
                    padding: "VALID");

                pooled_outputs.Add(pool);
            }

            var h_pool = tf.concat(pooled_outputs, 3);
            var h_pool_flat = tf.reshape(h_pool, new TensorShape(-1, num_filters * filter_sizes.Rank));
            Tensor h_drop = null;
            with(tf.name_scope("dropout"), delegate
            {
                h_drop = tf.nn.dropout(h_pool_flat, keep_prob);
            });

            Tensor logits = null;
            Tensor predictions = null;
            with(tf.name_scope("output"), delegate
            {
                logits = tf.layers.dense(h_drop, num_class);
                predictions = tf.argmax(logits, -1, output_type: tf.int32);
            });

            with(tf.name_scope("loss"), delegate
            {
                var sscel = tf.nn.sparse_softmax_cross_entropy_with_logits(logits: logits, labels: y);
                var loss = tf.reduce_mean(sscel);
                var adam = tf.train.AdamOptimizer(learning_rate);
                var optimizer = adam.minimize(loss, global_step: global_step);
            });

            with(tf.name_scope("accuracy"), delegate
            {
                var correct_predictions = tf.equal(predictions, y);
                var accuracy = tf.reduce_mean(tf.cast(correct_predictions, TF_DataType.TF_FLOAT), name: "accuracy");
            });
        }
    }
}