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"""
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from data.params import SYS_ARGS
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self.NUM_GPUS = 0
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self.BATCHSIZE_PER_GPU = PROPOSED_BATCH_PER_GPU
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loss = -tf.reduce_mean(y_hat_fake) + sum(all_regs)
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#tf.add_to_collection('glosses', loss)
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tf.compat.v1.add_to_collection('glosses', loss)
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return loss, loss
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bias = self.get.variables(name='b_' + str(i+1), shape=[self.X_SPACE_SIZE])
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x_hat = real + epsilon * (fake - real)
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y_hat_fake = self.network(inputs=fake, label=label)
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y_hat_real = self.network(inputs=real, label=label)
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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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flag = 'glosses'
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losses = tf.compat.v1.get_collection('glosses', scope)
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# losses = tf.compat.v1.get_collection(flag, scope)
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total_loss = tf.add_n(losses, name='total_loss')
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opt = args['opt']
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return train_op, mean_w, iterator, features_placeholder, labels_placeholder
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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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saver = tf.compat.v1.train.Saver()
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# init = tf.global_variables_initializer()
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init = tf.compat.v1.global_variables_initializer()
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logs = []
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#with tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
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with tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(allow_soft_placement=True, log_device_placement=False)) as sess:
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w_sum += w
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# This would allow the learnt model to be portable to another system
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#
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tf.compat.v1.reset_default_graph()
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demo = self._LABEL #np.zeros([self.ROW_COUNT,self.NUM_LABELS]) #args['de"shape":{"LABEL":list(self._LABEL.shape)} mo']
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# df = pd.DataFrame(np.round(f)).astype(np.int32)
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# if np.divide( np.sum(x), x.size) > .9 or p and np.sum(x) == x.size :
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