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"""
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import pickle
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# if self.NUM_GPUS > 1 :
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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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mean, var = tf.nn.moments(inputs, shift, keep_dims=True)
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shape = inputs.shape[1].value
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grad_and_var = (grad, v)
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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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x = self.normalize(inputs=x, name='cln' + str(i), shift=1,labels=label, n_labels=self.NUM_LABELS)
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self.discriminator = Discriminator(**args)
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self._REAL = args['real']
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)
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tf.compat.v1.get_variable_scope().reuse_variables()
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sess.run(iterator_d.initializer,
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else:
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if self._LABEL is not None :
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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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# print (df)
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