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"""
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import pickle
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self.NUM_LABELS = args['label'].shape[1]
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self.init_logs(**args)
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def init_logs(self,**args):
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self.log_dir = args['logs'] if 'logs' in args else 'logs'
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self.mkdir(self.log_dir)
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#
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#
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for key in ['train','output'] :
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self.mkdir(os.sep.join([self.log_dir,key]))
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self.mkdir (os.sep.join([self.log_dir,key,self.CONTEXT]))
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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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if self.logger :
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#
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# We will clear the logs from the data-store
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#
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column = self.ATTRIBUTES['synthetic']
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db = self.logger.db
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if db[column].count() > 0 :
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db.backup.insert({'name':column,'logs':list(db[column].find()) })
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db[column].drop()
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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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# suffix = "-".join(column) if isinstance(column,list)else column
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suffix = self.get.suffix()
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_name = os.sep.join([self.out_dir,'meta-'+suffix+'.json'])
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if os.path.exists(_name) :
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attr = json.loads((open(_name)).read())
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for key in attr :
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value = attr[key]
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setattr(self,key,value)
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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 log_meta(self,**args) :
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_object = {
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# '_id':'meta',
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'CONTEXT':self.CONTEXT,
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'ATTRIBUTES':self.ATTRIBUTES,
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'BATCHSIZE_PER_GPU':self.BATCHSIZE_PER_GPU,
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'Z_DIM':self.Z_DIM,
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"X_SPACE_SIZE":self.X_SPACE_SIZE,
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"D_STRUCTURE":self.D_STRUCTURE,
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"G_STRUCTURE":self.G_STRUCTURE,
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"NUM_GPUS":self.NUM_GPUS,
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"NUM_LABELS":self.NUM_LABELS,
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"MAX_EPOCHS":self.MAX_EPOCHS,
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"ROW_COUNT":self.ROW_COUNT
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}
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if args and 'key' in args and 'value' in args :
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key = args['key']
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value= args['value']
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object[key] = value
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# suffix = "-".join(self.column) if isinstance(self.column,list) else self.column
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suffix = self.get.suffix()
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_name = os.sep.join([self.out_dir,'meta-'+suffix])
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f = open(_name+'.json','w')
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f.write(json.dumps(_object))
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return _object
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def mkdir (self,path):
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if not os.path.exists(path) :
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os.mkdir(path)
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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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kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, 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 __init__(self,**args):
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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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self.generator = Generator(**args)
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self.values = args['values']
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|
def load_meta(self, column):
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|
super().load_meta(column)
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self.generator.load_meta(column)
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def apply(self,**args):
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|
# print (self.train_dir)
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|
# suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
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suffix = self.get.suffix()
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model_dir = os.sep.join([self.train_dir,suffix+'-'+str(self.MAX_EPOCHS)])
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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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tf.compat.v1.reset_default_graph()
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z = tf.random.normal(shape=[self.BATCHSIZE_PER_GPU, self.Z_DIM])
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|
y = tf.compat.v1.placeholder(shape=[self.BATCHSIZE_PER_GPU, self.NUM_LABELS], dtype=tf.int32)
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|
|
for i in np.arange(CANDIDATE_COUNT) :
|
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|
|
if labels :
|
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|
|
f = sess.run(fake,feed_dict={y:labels})
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|
else:
|
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|
|
f = sess.run(fake)
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|
|
tf.compat.v1.reset_default_graph()
|
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|
|
|
|
|
|
return df.to_dict(orient='list')
|
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|
|
# return df.to_dict(orient='list')
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|
|
# count = str(len(os.listdir(self.out_dir)))
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|
|
# _name = os.sep.join([self.out_dir,self.CONTEXT+'-'+count+'.csv'])
|
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|
|
# df.to_csv(_name,index=False)
|
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|
|
|
|
|
|
|
|
|
# output.extend(np.round(f))
|
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|
|
|
|
|
# for m in range(2):
|
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|
|
# for n in range(2, self.NUM_LABELS):
|
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|
|
# idx1 = (demo[:, m] == 1)
|
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|
|
# idx2 = (demo[:, n] == 1)
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|
|
# idx = [idx1[j] and idx2[j] for j in range(len(idx1))]
|
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|
|
# num = np.sum(idx)
|
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|
|
# print ("___________________list__")
|
|
|
|
# print (idx1)
|
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|
|
# print (idx2)
|
|
|
|
# print (idx)
|
|
|
|
# print (num)
|
|
|
|
# print ("_____________________")
|
|
|
|
# nbatch = int(np.ceil(num / self.BATCHSIZE_PER_GPU))
|
|
|
|
# label_input = np.zeros((nbatch*self.BATCHSIZE_PER_GPU, self.NUM_LABELS))
|
|
|
|
# label_input[:, n] = 1
|
|
|
|
# label_input[:, m] = 1
|
|
|
|
# output = []
|
|
|
|
# for i in range(nbatch):
|
|
|
|
# f = sess.run(fake,feed_dict={y: label_input[i* self.BATCHSIZE_PER_GPU:(i+1)* self.BATCHSIZE_PER_GPU]})
|
|
|
|
# output.extend(np.round(f))
|
|
|
|
# output = np.array(output)[:num]
|
|
|
|
# print ([m,n,output])
|
|
|
|
|
|
|
|
# np.save(self.out_dir + str(m) + str(n), output)
|
|
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|