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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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"""
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# suffix = "-".join(column) if isinstance(column,list)else column
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suffix = self.CONTEXT #self.get.suffix()
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This function will perform a batch normalization on an network layer
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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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This function will build the network that will generate the synthetic candidates
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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.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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return total_loss, w
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def input_fn(self):
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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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duration = time.time() - start_time
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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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# # row = {"module":"gan-train","action":"epochs","input":{"logs":logs}} #,"model":pickle.dump(sess)}
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# # self.logger.write(row)
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# self.logs['epochs'] += logs
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# #
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# # @TODO:
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# # We should upload the files in the checkpoint
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# # This would allow the learnt model to be portable to another system
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self.oROW_COUNT = self.ROW_COUNT
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# candidates.append (np.round(_matrix).astype(np.int64))
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