rnn-speed.py 文件源码

python
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项目:LSTM-TensorSpark 作者: EmanuelOverflow 项目源码 文件源码
def evaluate(self, t_data, t_label, s):
        state = self.fit_next(t_data, s, train=False)
        label = tf.Variable(t_label, name="label", trainable=False, dtype=tf.float32)
        s.run(tf.initialize_variables([label]))
        with tf.name_scope('evaluate'):
            return self.output_layer.evaluate(tf.transpose(state[0]), label)


        # decay_fn = tf.train.exponential_decay
        # Tutta sta roba da aggiornare???
        # loss = tf.argmax(self.ht, 1)
        # learning_rate_decay_fn=decay_fn
        # optimization = tf.contrib.layers.optimize_loss(self.ht, global_step=tf.Variable([1, 1]), optimizer=optimizer,
        #                                                learning_rate=0.01,
        #                                                variables=[self.weight_forget, self.weight_input, self.weight_output,
        #                                                           self.weight_C, self.biases_forget, self.biases_input,
        #                                                           self.biases_C, self.biases_output])
        # opt_op = optimizer.minimize(loss, var_list=[self.weight_forget, self.weight_input, self.weight_output,
        # self.weight_C, self.biases_forget, self.biases_input, self.biases_C,
        # self.biases_output])

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