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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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def init_logs(self,**args):
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inputs input layer of the neural network
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name name of the scope the
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labels labels (attributes not synthesized) by default None
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n_labels number of labels default None
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"""
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inputs = args['inputs']
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name = args['name']
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labels = None if 'labels' not in args else args['labels']
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n_labels= None if 'n_labels' not in args else args['n_labels']
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shift = [0] if self.__class__.__name__.lower() == 'generator' else [1] #-- not sure what this is doing
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:inputs matrix of data that we need
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:dim dimensions of ...
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"""
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x = args['inputs']
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tmp_dim = self.Z_DIM if 'dim' not in args else args['dim']
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label = args['label']
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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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:stage
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"""
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This function seems to produce
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"""
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features_placeholder = tf.compat.v1.placeholder(shape=self._REAL.shape, dtype=tf.float32)
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LABEL= self._LABEL
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assert not np.isnan(w_sum), 'Model diverged with loss = NaN'
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format_str = 'epoch: %d, w_distance = %f (%.1f)'
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print(format_str % (epoch, -w_sum/(self.STEPS_PER_EPOCH*2), duration))
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# print (dir (w_distance))
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#
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# if 'no_value' in args and args['no_value'] not in ['na','','NA'] :
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label = y[:, 1] * len(ma) + tf.squeeze(tf.matmul(y[:, 2:], tf.constant(ma, dtype=tf.int32)))
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# return candidates[0] if len(candidates) == 1 else candidates
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# info =dict(info ,**{"selected":INDEX, "ratio": ratio[INDEX] })
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# else :
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# df = df[columns[0]].append(pd.Series(missing))
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