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Siamese_Loader.py
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Siamese_Loader.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Sep 30 18:21:42 2019
@author: luist
"""
import numpy.random as rng
import numpy as np
import keras
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import ComplementNB
from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import BernoulliNB
from sklearn.linear_model import SGDClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.neural_network import MLPClassifier
from sklearn import tree
from sklearn import svm
from keras.layers import Input, Conv1D, Conv2D, Lambda, merge, Dense, Flatten,MaxPooling1D, Dropout, MaxPooling2D
from keras.models import Model, Sequential
from keras.regularizers import l2
from keras import backend as K
from keras.optimizers import SGD,Adam
from keras.losses import binary_crossentropy
#from sklearn.neighbors import KNeighborsClassifier
#from sklearn.metrics import accuracy_score
#from sklearn.metrics import confusion_matrix
class Load_Siamese:
def __init__(self,X_treino,Xval):
self.Xval = Xval
self.X_treino = X_treino
self.n_classes,self.n_exemplos,self.w,self.h = X_treino.shape
self.n_val,self.n_ex_val,_,_ = Xval.shape
def batch_function(self,n,s='train'):
if s == 'train':
X = self.X_treino
else:
X = self.Xval
n_classes, n_exemplos, w, h = X.shape
"""Cria um batch de n pares, metade da mesma classe e a outra metade de diferentes classes para o treino da rede"""
categorias = rng.choice(n_classes,size=(n,),replace=False)
pares=[np.zeros((n, w, h,1)) for i in range(2)]
targets=np.zeros((n,))
targets[n//2:] = 1
for i in range(n):
categoria = categorias[i]
index_1 = rng.randint(0,n_exemplos)
pares[0][i,:,:,:] = X[categoria,index_1].reshape(w,h,1)
index_2 = rng.randint(0,n_exemplos)
categoria_2 = categoria if i >= n//2 else (categoria + rng.randint(1,n_classes)) % n_classes
pares[1][i,:,:,:] = X[categoria_2,index_2].reshape(w,h,1)
return pares, targets
def one_shot_task(self,N,s='val'):
if s == 'train':
X = self.X_treino
else:
X = self.Xval
n_classes, n_exemplos, w, h = X.shape
"""Cria pares de uma imagem teste e de um outro conjunto para testar a rede - N-way one-shot learning"""
categorias = rng.choice(n_classes,size=(N,),replace=False)
indexes = rng.randint(0,n_exemplos,size=(N,))
true_categoria = categorias[0]
exemplo_1, exemplo_2 = rng.choice(n_exemplos,replace=False,size=(2,))
imagem_teste = np.asarray([X[true_categoria,exemplo_1,:,:]]*N).reshape(N,w,h,1)
support_set = X[categorias,indexes,:,:]
support_set[0,:,:] = X[true_categoria,exemplo_2]
support_set = support_set.reshape(N,w,h,1)
pares = [imagem_teste,support_set]
targets = np.zeros((N,))
targets[0] = 1
return pares, targets
def oneshot_test(self,model,N,k,s = "val"):
"""Avalia a accuracy média da rede na determinação da classe das imagens ao longo de um numero k de tasks"""
n_corretos = 0
for i in range(k):
inputs, targets = self.one_shot_task(N,s)
probs = model.predict(inputs)
if np.argmax(probs) == 0:
n_corretos+=1
percent_correct = (n_corretos / k)
return percent_correct
def knn_test(self,pairs,targets):
"""returns 1 if nearest neighbour gets the correct answer for a one-shot task given by (pairs, targets)"""
L2_distances = np.zeros_like(targets)
for i in range(len(targets)):
L2_distances[i] = np.sqrt(np.sum((pairs[0][i].flatten() - pairs[1][i].flatten())**2))
if np.argmin(L2_distances) == np.argmax(targets):
return 1
return 0
def random_test(self, N, trials, s='val'):
n_corretos = 0
for i in range(trials):
pairs, targets = self.one_shot_task(N,s)
correto = self.random_prediction(pairs,targets)
n_corretos += correto
return n_corretos/ trials
def random_prediction(self,pairs,targets):
random_predictions = np.zeros_like(targets)
for i in range(len(targets)):
random_predictions[i] = np.random.uniform(0,1)
if np.argmax(random_predictions) == np.argmax(targets):
return 1
return 0
def knn_prediction(self,pairs,targets):
"""returns 1 if nearest neighbour gets the correct answer for a one-shot task given by (pairs, targets)"""
L2_distances = np.zeros_like(targets)
for i in range(len(targets)):
L2_distances[i] = np.sqrt(np.sum((pairs[0][i].flatten() - pairs[1][i].flatten())**2))
if np.argmin(L2_distances) == np.argmax(targets):
return 1
return 0
def validate_cnn(self,N, trials, tam, s= 'val'):
pairs_train, targets_train = self.batch_function(tam)
lista1 = pairs_train[0]
lista2 = pairs_train[1]
pairs_train2 = []
for i in range(len(lista1)):
seq3=[]
seq = lista1[i].flatten()
seq2 = lista2[i].flatten()
for j in seq:
seq3.append(j)
for k in seq2:
seq3.append(k)
pairs_train2.append(np.asarray(seq3))
n_corretos = 0
pairs2train=np.asarray(pairs_train2).reshape(tam,54*100*2,1)
targets_train = np.asarray(targets_train).reshape(tam,1)
print(pairs2train.shape)
print(targets_train.shape)
n_timesteps, n_features, n_outputs = pairs2train.shape[1], pairs2train.shape[2], targets_train.shape[1]
cnn_net = self.cnn_model(pairs2train, targets_train,n_timesteps, n_features, n_outputs)
lista_acc=[]
for n in range(2,N+1):
for t in range(trials):
print(t)
pairs_val2=[]
pairs_val,targets_val = self.one_shot_task(n,s)
lista11= pairs_val[0]
lista22 = pairs_val[1]
for i2 in range(len(lista11)):
seq3=[]
seq = lista11[i2].flatten()
seq2 = lista22[i2].flatten()
for j2 in seq:
seq3.append(j2)
for k2 in seq2:
seq3.append(k2)
pairs_val2.append(np.asarray(seq3))
pairs2val=np.asarray(pairs_val2).reshape(n,54*100*2,1)
targets_val = np.asarray(targets_val).reshape(n,1)
print(pairs2val.shape)
print(targets_val.shape)
val2 = cnn_net.predict(pairs2val)
print("Target:",targets_val)
print("Previsão Probabilidade:",val2)
pred=[]
for i in val2:
pred.append(i[0])
if np.argmax(pred) == 0:
n_corretos +=1
percent_correct = (n_corretos / trials)
lista_acc.append(percent_correct)
n_corretos= 0
print(lista_acc)
return lista_acc
def cnn_model(self,trainX, trainy,n_timesteps, n_features, n_outputs):
verbose, epochs, batch_size = 1, 10, 50
conv_model = Sequential()
conv_model.add(Conv1D(64,10,activation='relu', input_shape=(n_timesteps,n_features)))
conv_model.add(MaxPooling1D())
conv_model.add(Conv1D(128,7,activation='relu'))
conv_model.add(MaxPooling1D())
conv_model.add(Conv1D(128,2,activation='relu'))
conv_model.add(MaxPooling1D())
conv_model.add(Conv1D(256,2,activation='relu'))
conv_model.add(MaxPooling1D())
conv_model.add(Dropout(0.5))
conv_model.add(MaxPooling1D())
conv_model.add(Flatten())
conv_model.add(Dense(1024,activation="relu"))
conv_model.add(Dense(n_outputs, activation='sigmoid'))
conv_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
conv_model.fit(trainX, trainy, epochs=epochs, batch_size=batch_size, verbose=verbose)
return conv_model
def validate_models(self, N, trials, tam, model, s= 'val'):
pairs_train, targets_train = self.batch_function(tam)
lista1 = pairs_train[0]
lista2 = pairs_train[1]
pairs_train2 = []
for i in range(len(lista1)):
seq3=[]
seq = lista1[i].flatten()
seq2 = lista2[i].flatten()
for j in seq:
seq3.append(j)
for k in seq2:
seq3.append(k)
pairs_train2.append(np.asarray(seq3))
n_corretos = 0
lista_acc=[]
for n in range(2,N+1):
for t in range(trials):
print(t)
pairs_val2=[]
pairs_val,targets_val = self.one_shot_task(n,s)
lista11= pairs_val[0]
lista22 = pairs_val[1]
for i2 in range(len(lista11)):
seq3=[]
seq = lista11[i2].flatten()
seq2 = lista22[i2].flatten()
for j2 in seq:
seq3.append(j2)
for k2 in seq2:
seq3.append(k2)
pairs_val2.append(np.asarray(seq3))
# pairs2val=np.asarray(pairs_val2).reshape(n,54*100*2)
kernel = 1.0 * RBF(1.0)
if (model == 'SVM'):
reg = svm.SVC(probability = True)
if (model == 'RF'):
reg = RandomForestClassifier(max_depth=2, random_state=0)
if (model == 'MLP'):
reg = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(10,5,2), random_state=1)
reg.fit(pairs_train2, targets_train)
pred = reg.predict_proba(pairs_val2)
print("Target:",targets_val)
print("Previsão Probabilidade:",pred)
pred_list=[]
for i in pred:
pred_list.append(i[0])
if np.argmax(pred_list) == 0:
n_corretos +=1
percent_correct = (n_corretos / trials)
lista_acc.append(percent_correct)
n_corretos= 0
print(lista_acc)
return lista_acc