"use strict";
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("@tensorflow/tfjs-core");
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var fullyConnectedLayer_1 = require("../common/fullyConnectedLayer");
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var prelu_1 = require("./prelu");
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var sharedLayers_1 = require("./sharedLayers");
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function RNet(x, params) {
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return tf.tidy(function () {
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var convOut = sharedLayers_1.sharedLayer(x, params);
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var vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
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var fc1 = fullyConnectedLayer_1.fullyConnectedLayer(vectorized, params.fc1);
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var prelu4 = prelu_1.prelu(fc1, params.prelu4_alpha);
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var fc2_1 = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_1);
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var max = tf.expandDims(tf.max(fc2_1, 1), 1);
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var prob = tf.softmax(tf.sub(fc2_1, max), 1);
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var regions = fullyConnectedLayer_1.fullyConnectedLayer(prelu4, params.fc2_2);
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var scores = tf.unstack(prob, 1)[1];
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return { scores: scores, regions: regions };
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});
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}
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exports.RNet = RNet;
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//# sourceMappingURL=RNet.js.map
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