<s> from __future__ import division <EOL> import matplotlib . pyplot as plt <EOL> import tensorflow as tf <EOL> import numpy as np <EOL> import heapq <EOL> import pylab <EOL> import time <EOL> def testNetwork ( num_hidden_units , decay , retain_input , retain_hidden ) : <EOL> import input_data <EOL> datasets = input_data . read_data_sets ( ) <EOL> NUM_PIXELS = datasets . train_set . inputs ( ) . shape [ <NUM_LIT:1> ] <EOL> NUM_CLASSES = datasets . train_set . targets ( ) . shape [ <NUM_LIT:1> ] <EOL> x = tf . placeholder ( tf . float32 , [ None , NUM_PIXELS ] ) <EOL> y = tf . placeholder ( tf . float32 , [ None , NUM_CLASSES ] ) <EOL> NUM_HIDDEN_UNITS = num_hidden_units <EOL> LEARNING_RATE = <NUM_LIT> <EOL> DECAY = decay <EOL> MOMENTUM = <NUM_LIT:0.1> <EOL> EPOCHS = <NUM_LIT> <EOL> BATCH_SIZE = <NUM_LIT:100> <EOL> RETAIN_INPUT = retain_input <EOL> RETAIN_HIDDEN = retain_hidden <EOL> hidden_layer_1_weights = tf . Variable ( tf . random_normal ( [ NUM_PIXELS , NUM_HIDDEN_UNITS ] , stddev = <NUM_LIT> ) ) <EOL> hidden_layer_2_weights = tf . Variable ( tf . random_normal ( [ NUM_HIDDEN_UNITS , NUM_HIDDEN_UNITS ] , stddev = <NUM_LIT> ) ) <EOL> output_layer_weights = tf . Variable ( tf . random_normal ( [ NUM_HIDDEN_UNITS , NUM_CLASSES ] , stddev = <NUM_LIT> ) ) <EOL> p_retain_input = tf . placeholder ( tf . float32 ) <EOL> p_retain_hidden = tf . placeholder ( tf . float32 ) <EOL> x = tf . nn . dropout ( x , p_retain_input ) <EOL> hidden_layer_1 = tf . nn . relu ( tf . matmul ( x , hidden_layer_1_weights ) ) <EOL> hidden_layer_1 = tf . nn . dropout ( hidden_layer_1 , p_retain_hidden ) <EOL> hidden_layer_2 = tf . nn . softmax ( tf . matmul ( hidden_layer_1 , hidden_layer_2_weights ) ) <EOL> hidden_layer_2 = tf . nn . dropout ( hidden_layer_2 , p_retain_hidden ) <EOL> model = tf . matmul ( hidden_layer_2 , output_layer_weights ) <EOL> cost = tf . reduce_mean ( tf . nn . softmax_cross_entropy_with_logits ( model , y ) ) <EOL> optimizer = tf . train . RMSPropOptimizer ( LEARNING_RATE , DECAY ) . minimize ( cost ) <EOL> correct_prediction = tf . equal ( tf . argmax ( model , <NUM_LIT:1> ) , tf . argmax ( y , <NUM_LIT:1> ) ) <EOL> accuracy = tf . reduce_mean ( tf . cast ( correct_prediction , tf . float32 ) ) <EOL> print ( '<STR_LIT>' ) <EOL> sess = tf . Session ( ) <EOL> init = tf . initialize_all_variables ( ) <EOL> sess . run ( init ) <EOL> avg_cost = <NUM_LIT:0.0> <EOL> train_accuracies = [ ] <EOL> validation_accuracies = [ ] <EOL> for epoch in range ( EPOCHS ) : <EOL> num_batches = int ( datasets . train_set . num_examples ( ) / BATCH_SIZE ) <EOL> for batch in range ( num_batches ) : <EOL> batch_xs , batch_ys = datasets . train_set . next_batch ( BATCH_SIZE ) <EOL> sess . run ( optimizer , feed_dict = { x : batch_xs , y : batch_ys , p_retain_input : RETAIN_INPUT , p_retain_hidden : RETAIN_HIDDEN } ) <EOL> avg_cost += sess . run ( cost , feed_dict = { x : batch_xs , y : batch_ys , p_retain_input : RETAIN_INPUT , p_retain_hidden : RETAIN_HIDDEN } ) / num_batches <EOL> train_accuracy = sess . run ( accuracy , feed_dict = { x : datasets . train_set . inputs ( ) , y : datasets . train_set . targets ( ) , p_retain_input : <NUM_LIT:1> , p_retain_hidden : <NUM_LIT:1> } ) <EOL> train_accuracies . append ( train_accuracy ) <EOL> validation_accuracy = sess . run ( accuracy , feed_dict = { x : datasets . validation_set . inputs ( ) , y : datasets . validation_set . targets ( ) , p_retain_input : <NUM_LIT:1> , p_retain_hidden : <NUM_LIT:1> } ) <EOL> validation_accuracies . append ( validation_accuracy ) <EOL> print ( "<STR_LIT>" . format ( epoch , train_accuracy , validation_accuracy ) ) <EOL> return heapq . nlargest ( <NUM_LIT:3> , np . array ( validation_accuracies ) ) <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> top_3_validation_accuracies = [ ] <EOL> num_hidden_units_intervals = [ <NUM_LIT:5> , <NUM_LIT:10> , <NUM_LIT> , <NUM_LIT:50> , <NUM_LIT:100> , <NUM_LIT:200> , <NUM_LIT> ] <EOL> decay_intervals = [ <NUM_LIT> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT:0.1> ] <EOL> retain_input_intervals = [ <NUM_LIT> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT:0.1> ] <EOL> retain_hidden_intervals = [ <NUM_LIT> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT:0.1> ] <EOL> accuracies = testNetwork ( <NUM_LIT:100> , <NUM_LIT> , <NUM_LIT> , <NUM_LIT> ) <EOL> print ( accuracies ) <EOL> exit ( ) <EOL> iteration = <NUM_LIT:0> <EOL> for num_hidden_units in num_hidden_units_intervals : <EOL> for decay in decay_intervals : <EOL> for retain_input in retain_input_intervals : <EOL> for retain_hidden in retain_hidden_intervals : <EOL> iteration += <NUM_LIT:1> <EOL> accuracies = testNetwork ( num_hidden_units , decay , retain_input , retain_hidden ) <EOL> top_3_validation_accuracies . append ( accuracies ) <EOL> print ( "<STR_LIT>" <EOL> . format ( num_hidden_units , decay , retain_input , retain_hidden ) ) <EOL> print ( iteration , accuracies ) <EOL> print ( "<STR_LIT>" ) <EOL> print ( accuracies ) </s>
