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"""
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import pickle
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# if db[column].count() > 0 :
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value= args['value']
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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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mean, var = tf.nn.moments(inputs, shift, keep_dims=True)
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shape = inputs.shape[1].value
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for i, dim in enumerate(self.G_STRUCTURE[:-1]):
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kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, dim])
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h1 = self.normalize(inputs=tf.matmul(x, kernel),shift=0, name='cbn' + str(i), labels=label, n_labels=self.NUM_LABELS)
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h2 = tf.nn.relu(h1)
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x = x + h2
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tmp_dim = dim
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i = len(self.G_STRUCTURE) - 1
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#
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# This seems to be an extra hidden layer:
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# It's goal is to map continuous values to discrete values (pre-trained to do this)
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kernel = self.get.variables(name='W_' + str(i), shape=[tmp_dim, self.G_STRUCTURE[-1]])
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h1 = self.normalize(inputs=tf.matmul(x, kernel), name='cbn' + str(i),
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labels=label, n_labels=self.NUM_LABELS)
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h2 = tf.nn.tanh(h1)
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x = x + h2
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# This seems to be the output layer
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#
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kernel = self.get.variables(name='W_' + str(i+1), shape=[self.Z_DIM, self.X_SPACE_SIZE])
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bias = self.get.variables(name='b_' + str(i+1), shape=[self.X_SPACE_SIZE])
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x = tf.nn.sigmoid(tf.add(tf.matmul(x, kernel), bias))
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return x
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return w_distance, loss
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# @TODO: Ziqi needs to explain what's going on here
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# labels_placeholder = None
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# suffix = "-".join(self.ATTRIBUTES['synthetic']) if isinstance(self.ATTRIBUTES['synthetic'],list) else self.ATTRIBUTES['synthetic']
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# if 'no_value' in args and args['no_value'] not in ['na','','NA'] :
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tf.compat.v1.reset_default_graph()
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# # If the generator had a reductive effect we should be able to get random values from either :
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# # - The space of outliers
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# # - existing values for smaller spaces that have suffered over training
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# #
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# N = ii.sum()
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# missing_values = self.MISSING_VALUES if self.MISSING_VALUES else self.values
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# missing = np.random.choice(missing_values,N)
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# # missing = []
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# #
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# # @TODO:
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# # Log the findings here in terms of ratio, missing, candidate count
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# # print ([np.max(ratio),len(missing),len(found),i])
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# i = np.where(ii == 0)[0]
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# return df.to_dict(orient='list')
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