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"""
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usage :
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optional :
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--num_gpu number of gpus to use will default to 1
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--epoch steps per epoch default to 256
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"""
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import tensorflow as tf
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from tensorflow.contrib.layers import l2_regularizer
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import numpy as np
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import pandas as pd
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import time
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import os
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import sys
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self.NUM_GPUS = 1 if 'num_gpu' not in args else args['num_gpu']
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self.train_dir = os.sep.join([self.log_dir,'train',self.CONTEXT])
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self.out_dir = os.sep.join([self.log_dir,'output',self.CONTEXT])
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def load_meta(self,column):
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"""
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This function is designed to accomodate the uses of the sub-classes outside of a strict dependency model.
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Because prediction and training can happen independently
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"""
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return _object
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average_grads = []
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for grad_and_vars in zip(*tower_grads):
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grads = []
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for g, _ in grad_and_vars:
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expanded_g = tf.expand_dims(g, 0)
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grads.append(expanded_g)
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grad = tf.concat(axis=0, values=grads)
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grad = tf.reduce_mean(grad, 0)
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v = grad_and_vars[0][1]
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grad_and_var = (grad, v)
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average_grads.append(grad_and_var)
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return average_grads
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class Generator (GNet):
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"""
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This class is designed to handle generation of candidate datasets for this it will aggregate a discriminator, this allows the generator not to be random
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"""
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def __init__(self,**args):
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GNet.__init__(self,**args)
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self.discriminator = Discriminator(**args)
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def loss(self,**args):
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fake = args['fake']
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label = args['label']
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y_hat_fake = self.discriminator.network(inputs=fake, label=label)
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for i, dim in enumerate(self.D_STRUCTURE[1:]):
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kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[i], dim])
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bias = self.get.variables(name='b_' + str(i), shape=[dim])
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print (["\t",bias,kernel])
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x = tf.nn.relu(tf.add(tf.matmul(x, kernel), bias))
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x = self.normalize(inputs=x, name='cln' + str(i), shift=1,labels=label, n_labels=self.NUM_LABELS)
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i = len(self.D_STRUCTURE)
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kernel = self.get.variables(name='W_' + str(i), shape=[self.D_STRUCTURE[-1], 1])
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bias = self.get.variables(name='b_' + str(i), shape=[1])
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y = tf.add(tf.matmul(x, kernel), bias)
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return y
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def loss(self,**args) :
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"""
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This function compute the loss of
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:real
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:fake
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:label
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"""
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real = args['real']
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fake = args['fake']
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label = args['label']
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epsilon = tf.random.uniform(shape=[self.BATCHSIZE_PER_GPU,1],minval=0,maxval=1)
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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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class Train (GNet):
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vars_ = tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES, scope=stage)
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for _ in range(2):
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_, w = sess.run([train_d, w_distance])
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w_sum += w
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sess.run(train_g)
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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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suffix = self.get.suffix()
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# return df.to_dict(orient='list')
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df = pd.read_csv(SYS_ARGS['raw-data'])
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# cols = SYS_ARGS['column']
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# _map,_df = (Binary()).Export(df)
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# i = np.arange(_map[column]['start'],_map[column]['end'])
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max_epochs = np.int32(SYS_ARGS['max_epochs']) if 'max_epochs' in SYS_ARGS else 10
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# REAL = _df[:,i]
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REAL = pd.get_dummies(df[column]).astype(np.float32).values
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LABEL = pd.get_dummies(df[column_id]).astype(np.float32).values
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trainer = Train(context=context,max_epochs=max_epochs,real=REAL,label=LABEL,column=column,column_id=column_id)
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trainer.apply()
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#
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# We should train upon this data
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#
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# -- we need to convert the data-frame to binary matrix, given a column
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#
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pass
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elif 'generate' in SYS_ARGS:
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values = df[column].unique().tolist()
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values.sort()
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