<s> import theano <EOL> import numpy as np <EOL> from sklearn . preprocessing import OneHotEncoder <EOL> from sklearn import cross_validation , metrics , datasets <EOL> from neupy import algorithms , layers , environment <EOL> environment . reproducible ( ) <EOL> theano . config . floatX = '<STR_LIT>' <EOL> mnist = datasets . fetch_mldata ( '<STR_LIT>' ) <EOL> target_scaler = OneHotEncoder ( ) <EOL> target = mnist . target . reshape ( ( - <NUM_LIT:1> , <NUM_LIT:1> ) ) <EOL> target = target_scaler . fit_transform ( target ) . todense ( ) <EOL> data = mnist . data / <NUM_LIT> <EOL> data = data - data . mean ( axis = <NUM_LIT:0> ) <EOL> n_samples = data . shape [ <NUM_LIT:0> ] <EOL> data = data . reshape ( ( n_samples , <NUM_LIT:1> , <NUM_LIT> , <NUM_LIT> ) ) <EOL> x_train , x_test , y_train , y_test = cross_validation . train_test_split ( <EOL> data . astype ( np . float32 ) , <EOL> target . astype ( np . float32 ) , <EOL> train_size = ( <NUM_LIT:6> / <NUM_LIT> ) <EOL> ) <EOL> network = algorithms . Adadelta ( <EOL> [ <EOL> layers . Convolution ( ( <NUM_LIT:32> , <NUM_LIT:1> , <NUM_LIT:3> , <NUM_LIT:3> ) ) , <EOL> layers . Relu ( ) , <EOL> layers . Convolution ( ( <NUM_LIT> , <NUM_LIT:32> , <NUM_LIT:3> , <NUM_LIT:3> ) ) , <EOL> layers . Relu ( ) , <EOL> layers . MaxPooling ( ( <NUM_LIT:2> , <NUM_LIT:2> ) ) , <EOL> layers . Dropout ( <NUM_LIT> ) , <EOL> layers . Reshape ( ) , <EOL> layers . Relu ( <NUM_LIT> ) , <EOL> layers . Dropout ( <NUM_LIT> ) , <EOL> layers . Softmax ( <NUM_LIT:200> ) , <EOL> layers . ArgmaxOutput ( <NUM_LIT:10> ) , <EOL> ] , <EOL> error = '<STR_LIT>' , <EOL> step = <NUM_LIT:1.0> , <EOL> verbose = True , <EOL> shuffle_data = True , <EOL> epochs_step_minimizator = <NUM_LIT:8> , <EOL> addons = [ algorithms . SimpleStepMinimization ] , <EOL> ) <EOL> network . architecture ( ) <EOL> network . train ( x_train , y_train , x_test , y_test , epochs = <NUM_LIT:6> ) <EOL> y_predicted = network . predict ( x_test ) <EOL> y_test_labels = np . asarray ( y_test . argmax ( axis = <NUM_LIT:1> ) ) . reshape ( len ( y_test ) ) <EOL> print ( metrics . classification_report ( y_test_labels , y_predicted ) ) <EOL> score = metrics . accuracy_score ( y_test_labels , y_predicted ) <EOL> print ( "<STR_LIT>" . format ( <NUM_LIT:100> * score ) ) </s>
