Created
January 14, 2019 11:10
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Sampling a portfolio from a dirichlet distribution
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| self.alphas= tf.contrib.layers.fully_connected( | |
| inputs=l2, | |
| num_outputs=num_stocks+1, | |
| activation_fn=tf.nn.relu, | |
| weights_initializer=tf.initializers.glorot_uniform) | |
| self.alphas +=1 | |
| self.dirichlet = tfp.distributions.Dirichlet(self.alphas) | |
| self.action = self.dirichlet._sample_n(1) | |
| self.action = tf.squeeze(self.action) |
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