MLP.py

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Fri Oct 28 21:46:23 2016

@author: stephen
"""
 
from __future__ import print_function
 
from tensorflow import keras
import numpy as np
import pandas as pd

np.random.seed(813306)

      
def readucr(filename):
    data = np.loadtxt(filename, delimiter = ',')
    Y = data[:,0]
    X = data[:,1:]
    return X, Y
  
nb_epochs = 5000

     
#flist = ['Adiac', 'Beef', 'CBF', 'ChlorineConcentration', 'CinC_ECG_torso', 'Coffee', 'Cricket_X', 'Cricket_Y', 'Cricket_Z', 
#'DiatomSizeReduction', 'ECGFiveDays', 'FaceAll', 'FaceFour', 'FacesUCR', '50words', 'FISH', 'Gun_Point', 'Haptics', 
#'InlineSkate', 'ItalyPowerDemand', 'Lighting2', 'Lighting7', 'MALLAT', 'MedicalImages', 'MoteStrain', 'NonInvasiveFatalECG_Thorax1', 
#'NonInvasiveFatalECG_Thorax2', 'OliveOil', 'OSULeaf', 'SonyAIBORobotSurface', 'SonyAIBORobotSurfaceII', 'StarLightCurves', 'SwedishLeaf', 'Symbols', 
#'synthetic_control', 'Trace', 'TwoLeadECG', 'Two_Patterns', 'uWaveGestureLibrary_X', 'uWaveGestureLibrary_Y', 'uWaveGestureLibrary_Z', 'wafer', 'WordsSynonyms', 'yoga']

flist = ['Adiac']
for each in flist:
    fname = each
    x_train, y_train = readucr(fname+'/'+fname+'_TRAIN')
    x_test, y_test = readucr(fname+'/'+fname+'_TEST')
    nb_classes =len(np.unique(y_test))
    y_train = (y_train - y_train.min())/(y_train.max()-y_train.min())*(nb_classes-1)
    y_test = (y_test - y_test.min())/(y_test.max()-y_test.min())*(nb_classes-1)
    batch_size = min(x_train.shape[0]/10, 16)
    
    Y_train = keras.utils.to_categorical(y_train, nb_classes)
    Y_test = keras.utils.to_categorical(y_test, nb_classes)
     
    x_train_mean = x_train.mean()
    x_train_std = x_train.std()
    x_train = (x_train - x_train_mean)/(x_train_std)
     
   # x_test_min = np.min(x_test, axis = 1, keepdims=1)
   # x_test_max = np.max(x_test, axis = 1, keepdims=1)
    x_test = (x_test - x_train_mean)/(x_train_std)
     
    #x_train = x_train.reshape(x_train.shape + (1,))
    #x_test = x_test.reshape(x_test.shape + (1,))
    
    x = keras.layers.Input(x_train.shape[1:])
    y= keras.layers.Dropout(0.1)(x)
    y = keras.layers.Dense(500, activation='relu')(y)
    y = keras.layers.Dropout(0.2)(y)
    y = keras.layers.Dense(500, activation='relu')(y)
    y = keras.layers.Dropout(0.2)(y)
    y = keras.layers.Dense(500, activation = 'relu')(y)
    y = keras.layers.Dropout(0.3)(y)
    out = keras.layers.Dense(nb_classes, activation='softmax')(y)
     
    model = keras.models.Model(inputs=x, outputs=out)
     
    optimizer = keras.optimizers.Adadelta()    
    model.compile(loss='categorical_crossentropy',
                  optimizer=optimizer,
                  metrics=['accuracy'])
     
    reduce_lr = keras.callbacks.ReduceLROnPlateau(monitor = 'loss', factor=0.5,
                      patience=200, min_lr=0.1)
    
    hist = model.fit(x_train, Y_train, batch_size=batch_size, nb_epoch=nb_epochs,
              verbose=1, validation_data=(x_test, Y_test), 
                #callbacks = [TestCallback((x_train, Y_train)), reduce_lr, keras.callbacks.TensorBoard(log_dir='./log'+fname, histogram_freq=1)])
                 callbacks=[reduce_lr])
    
    #Print the testing results which has the lowest training loss.
    log = pd.DataFrame(hist.history)
    print(log.loc[log['loss'].idxmin]['loss'], log.loc[log['loss'].idxmin]['val_acc'])