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"""
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from data.params import SYS_ARGS
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self.BATCHSIZE_PER_GPU = PROPOSED_BATCH_PER_GPU
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elif not os.path.exists(path):
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name = args['name']
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# super().load_meta(**args)
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class Discriminator(GNet):
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y_hat = self.network(inputs=x_hat, label=label)
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grad = tf.gradients(y_hat, [x_hat])[0]
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slopes = tf.sqrt(tf.reduce_sum(tf.square(grad), 1))
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gradient_penalty = tf.reduce_mean((slopes - 1.) ** 2)
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#all_regs = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
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all_regs = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.REGULARIZATION_LOSSES)
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w_distance = -tf.reduce_mean(y_hat_real) + tf.reduce_mean(y_hat_fake)
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loss = w_distance + 10 * gradient_penalty + sum(all_regs)
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#tf.add_to_collection('dlosses', loss)
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tf.compat.v1.add_to_collection('dlosses', loss)
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return w_distance, loss
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total_loss = tf.add_n(losses, name='total_loss')
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per_gpu_w = []
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def apply(self,**args):
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# max_epochs = args['max_epochs'] if 'max_epochs' in args else 10
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REAL = self._REAL
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LABEL= self._LABEL
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if (self.logger):
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pass
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with tf.device('/cpu:0'):
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opt_d = tf.compat.v1.train.AdamOptimizer(1e-4)
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opt_g = tf.compat.v1.train.AdamOptimizer(1e-4)
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train_d, w_distance, iterator_d, features_placeholder_d, labels_placeholder_d = self.network(stage='D', opt=opt_d)
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train_g, _, iterator_g, features_placeholder_g, labels_placeholder_g = self.network(stage='G', opt=opt_g)
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# saver = tf.train.Saver()
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#
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#
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# model_dir = os.sep.join([self.train_dir,str(self.MAX_EPOCHS)])
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# x = df.sum(axis=1).values
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# # Log the findings here in terms of ratio, missing, candidate count
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# # print ([np.max(ratio),len(missing),len(found),i])
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# i = np.where(ii == 0)[0]
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