#NEWLINE# def train_tensorflow( sess , trX , trY , train_steps , full_train , train_size , net_type , transform_dict , loss_type , optimizer , optimizer_dict ) : #NEWLINE# #INDENT# '\n    Automatically constructs, trains, and tests a tensorflow neural network, returning the r squared value of the\n    output.\n    :param sess: A tensorflow session.\n    :param trX: Numpy array that contains the training features.\n    :param trY: Numpy array that contains the training outputs. Must have shape of at least 1 on columns.\n    :param train_steps: Integer value denoting the number of times to iterate through training.\n    :param full_train: Boolean value denoting whether to use the full training set for each iteration.\n    :param train_size: Integer value denoting the number of samples to pull from the training set for each iteration of\n        training.\n    :param net_type: List of alternating string values and integer values. Must always start and end with a string\n        values. The strings denote the type of each layer. The integer values denote the end size of each layer, though\n        this is constrained for certain layer types. Sizes of zero drop that layer out.\n    :param transform_dict: Dictionary of strings to tuples of tensors that encode how to set up the layers of the neural\n        network.\n    :param loss_type: String denoting type of tensor to use for loss type. Use l2_loss for regression, cross_entropy for\n        classification.\n    :param optimizer: String denoting the type of optimization tensor to use for training the neural network.\n    :param optimizer_dict: Dictionary of strings to tuples of tensors that encode how to set up the optimizers of the\n        neural network.\n    :return: predict_op: Tensor that encodes the neural network.\n        X: Placeholder tensor for the features array.\n        y: Placeholder tensor for the output array.\n    ' #NEWLINE# X = tf . placeholder ( 'float' , [ None , trX . shape [ 1 ] ] ) #NEWLINE# y = tf . placeholder ( 'float' , [ None , trY . shape [ 1 ] ] ) #NEWLINE# py_x = make_model ( X , trX . shape [ 1 ] , trY . shape [ 1 ] , net_type , transform_dict ) #NEWLINE# if ( loss_type == 'l2_loss' ) : #NEWLINE# #INDENT# cost = tf . nn . l2_loss ( tf . sub ( py_x , y ) ) #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# if ( loss_type == 'cross_entropy' ) : #NEWLINE# #INDENT# cost = ( - tf . reduce_sum ( ( y * tf . log ( py_x ) ) ) ) #NEWLINE# #UNINDENT# #UNINDENT# if ( type ( optimizer ) == str ) : #NEWLINE# #INDENT# train_op = optimizer_dict [ optimizer ] [ 0 ] ( * optimizer_dict [ optimizer ] [ 1 ] ) . minimize ( cost ) #NEWLINE# #UNINDENT# else : #NEWLINE# #INDENT# train_op = optimizer [ 0 ] ( * optimizer [ 1 ] ) . minimize ( cost ) #NEWLINE# #UNINDENT# predict_op = py_x #NEWLINE# init = tf . initialize_all_variables ( ) #NEWLINE# sess . run ( init ) #NEWLINE# try : #NEWLINE# #INDENT# for i in range ( train_steps ) : #NEWLINE# #INDENT# if full_train : #NEWLINE# #INDENT# for ( start , end ) in zip ( range ( 0 , len ( trX ) , 100 ) , range ( 100 , len ( trX ) , 100 ) ) : #NEWLINE# #INDENT# sess . run ( train_op , feed_dict = { #NEWLINE# #INDENT# X : trX [ start : end ] , #NEWLINE# y : trY [ start : end ] , #NEWLINE# #UNINDENT# } ) #NEWLINE# #UNINDENT# #UNINDENT# else : #NEWLINE# #INDENT# indices = random_index_list ( train_size , len ( trY ) ) #NEWLINE# sess . run ( train_op , feed_dict = { #NEWLINE# #INDENT# X : trX [ indices ] , #NEWLINE# y : trY [ indices ] , #NEWLINE# #UNINDENT# } ) #NEWLINE# #UNINDENT# #UNINDENT# #UNINDENT# except : #NEWLINE# #INDENT# print ( 'Error during training' ) #NEWLINE# sess . close ( ) #NEWLINE# return None #NEWLINE# #UNINDENT# return ( predict_op , X , y )