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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