def test_nbr_neuron(list_test): color_list=['r','g','b','k','m','c','y'] color_list *= 3 k=0 for i in list_test : my_layer1 = Layer.Linear(6,i) my_layer2 = ActivationFunctions.Tanh() my_layer5 = Layer.Linear(i,i) my_layer6 = ActivationFunctions.Tanh() my_layer3 = Layer.Linear(i,1) my_layer4 = ActivationFunctions.Sigmoid() my_NN = Neural_network.NeuralNet([my_layer1, my_layer2, my_layer5, my_layer6, my_layer3, my_layer4]) chi2_list, error_list = User.train(my_NN, data_train_input, data_train_target, num_epochs = num_epoch_max, optimizer = Optimizer.SGD(lr = my_lr), batch_size=my_batch_size) data_test_prediction = User.prediction(my_NN,data_test_input) error_final = Error_round.error_round(data_test_prediction, data_test_target) plt.plot(range(num_epoch_max), error_list, label= str(i), c=color_list[k]) plt.plot([num_epoch_max],[error_final], marker='o', c=color_list[k]) plt.xlabel('Epoch') plt.ylabel('Training round error') k+=1 plt.legend(title='Neurons') plt.title('Optimisation of the number of neurons') plt.show()
def test_nbr_layer(list_test, n_neuron): color_list=['r','g','b','k','m','c','y'] color_list *= 3 k=0 my_layerini1 = Layer.Linear(6,n_neuron) my_layerini2 = ActivationFunctions.Tanh() my_layerfini1 = Layer.Linear(n_neuron,1) my_layerfini2 = ActivationFunctions.Sigmoid() for i in list_test : layers_new = [my_layerini1, my_layerini2] for j in range(i) : layers_new += [Layer.Linear(n_neuron,n_neuron),ActivationFunctions.Tanh()] layers_new += [my_layerfini1, my_layerfini2] my_NN = Neural_network.NeuralNet(layers_new) chi2_list, error_list = User.train(my_NN, data_train_input, data_train_target, num_epochs = num_epoch_max,optimizer = Optimizer.SGD(lr = my_lr), batch_size=my_batch_size) data_test_prediction = User.prediction(my_NN,data_test_input) error_final = Error_round.error_round(data_test_prediction, data_test_target) plt.plot(range(num_epoch_max), error_list, label= str(i),c=color_list[k]) plt.plot([num_epoch_max],[error_final], marker='o', c=color_list[k]) plt.xlabel('Epoch') plt.ylabel('Training round error') k+=1 plt.legend(title='Hidden layers') plt.title('Optimisation of the number of hidden layers') plt.show()
def test_NeuralNet() : my_layer1 = Layer.Linear(3,2) my_layer2 = ActivationFunctions.Tanh() my_NN = Neural_network.NeuralNet([my_layer1,my_layer2]) input = np.array([[1,2,3],[4,5,6]]) grad = np.array([[0.5,0.2],[0.1,0.3]]) print('forward', my_NN.forward(input)) print('backward', my_NN.backward(grad)) '''OK'''
def test_train_prediction() : my_layer1 = Layer.Linear(3,2) my_layer2 = ActivationFunctions.Tanh() my_NN = Neural_network.NeuralNet([my_layer1,my_layer2]) input = np.array([[1,2,3],[4,5,6]]) target = np.array([[0.5,0.2],[0.1,0.3]]) User.train(my_NN, input, target, batch_size = 1) #By careful, we must have size_training = number of rows in our data input_predict = np.array([[1,1,4],[0.5,2,4]]) print(User.prediction(my_NN,input_predict)) ''' OK '''
def test_XOR() : my_layer1 = Layer.Linear(2,3) my_layer2 = ActivationFunctions.Tanh() my_layer3 = Layer.Linear(3,1) my_layer4 = ActivationFunctions.Sigmoid() #my_layer3 = lib2.Arondi() my_NN = Neural_network.NeuralNet([my_layer1,my_layer2,my_layer3,my_layer4]) input =np.array([[0, 0], [1, 0], [0, 1], [1, 1]]) target = np.array([[0], [1], [1], [0]]) User.train(my_NN, input, target, batch_size = 1,,num_epochs= 1000) # By careful, we must have size_training = number of rows in our data input_predict = np.array([[0, 0], [1, 0], [0, 1], [1, 1]]) print(User.prediction(my_NN,input_predict)) ''' OK '''
def predict(symbImgs, topology): # Flatten using knn first knn = kNN.kNN() flat_syms = knn.flatten_symbols(symbImgs) binary_symbs = preprocessor.binarize(flat_syms) flattened = logistic_regression.flatten(binary_symbs) knn.load_MNIST() m_data = logistic_regression.flatten(knn.mnist_data) m_labels = knn.mnist_labels nn = neural_network.NeuralNet(topology) print("> Fitting NN") m_labels = toArray(m_labels[:10000]) nn.fit(m_data[:10000], m_labels) results = [] print("> Predicting NN") for i in range(1, len(flattened) + 1): if i % 1000 == 0: print(i) results.append(nn.predict(flattened[i - 1:i])) return results
#script which generates prediction for the MNIST dataset import pandas as pd import numpy as np import matplotlib.pyplot as plt import neural_network as nn import sys import os sys.path.append(os.getcwd()) os.chdir("..") data = np.array(pd.read_csv("mnist_train.csv")) X = data[:, 1:] Y = data[:, 0][:, np.newaxis] os.chdir("neural-network") model = nn.NeuralNet(hidden_size=60, n_labels=10, lamb=1, alpha=0.4) theta1, theta2 = model.import_theta("weights_digits2.csv", X) prediction, accuracy = model.predict(theta1, theta2, X, Y) def show_pred(): a = np.random.randint(60000) print("The predicted number is", prediction[a]) fig, ax = plt.subplots() ax.imshow(X[a, :].reshape((28, 28)), cmap="Greys") plt.show()
#quitar la columna diagnosis y qrs X = arch.drop(columns=['diagnosis']) # reemplazaremos el diagnostico con 0 y 1 (0 no esta enfermo, 1 esta enfermo) arch['diagnosis'] = arch['diagnosis'].replace(1, 0) arch['diagnosis'] = arch['diagnosis'].replace(2, 1) y_label = arch['diagnosis'].values.reshape(X.shape[0], 1) # estandarizar el dataset sc = StandardScaler() sc.fit(X) X = sc.transform(X) nn = neural_network.NeuralNet() nn.entrenar(X, y_label) #predecir result = nn.predecir(X) print(result) precistion = nn.exactitud(y_label, result) # verificar que se paresca print("procentaje de precision", float(precistion), "%") result = nn.predecir(X) print( "-----------------------------------------------------------------------------------------------------------:" )
def train_simultaneousNN( inputs_train: Tensor, targets_train: Tensor, loss: Loss.Loss = Loss.MeanSquareError(), optimizer: OptimizerClass.Optimizer = OptimizerClass.SGD(), num_epochs: int = 5000, batch_size: int = 32) -> tuple: size_training = inputs_train.shape[0] Result_chi2 = [[], [], [], [], [], [], [], [], []] list_epoch = np.array(range(10, 50, 5)) / 100 * num_epochs '''initialisation des 9 NN''' #verifier question seed() list_net = [] for i in range(9): layers = [] layers.append(Layer.Linear(6, 4)) layers.append(ActivationFunctions.Tanh()) layers.append(Layer.Linear(4, 2)) layers.append(ActivationFunctions.Tanh()) layers.append(Layer.Linear(2, 1)) layers.append(ActivationFunctions.Sigmoid()) list_net.append(Neural_network.NeuralNet(layers)) destroyed_NN = [] nbr_batch = size_training // batch_size ''' training des 9 NN''' for epoch in range(num_epochs): for k in range(9): if k not in destroyed_NN: Chi2_train = 0 for i in range(0, size_training, batch_size): # 1) feed forward y_actual = list_net[k].forward(inputs_train[i:i + batch_size]) # 2) compute the loss and the gradients Chi2_train += loss.loss(targets_train[i:i + batch_size], y_actual) grad_ini = loss.grad(targets_train[i:i + batch_size], y_actual) # 3)feed backwards grad_fini = list_net[k].backward(grad_ini) # 4) update the net optimizer.step(list_net[k], n_epoch=epoch) Chi2_train = Chi2_train / nbr_batch Result_chi2[k].append(Chi2_train) '''Supression du NN le moins efficace ''' if epoch in list_epoch: Comparaison = [[], []] for k in range(9): if k not in destroyed_NN: ErrorSlope = np.polyfit(np.array(range(epoch - 49, epoch)), Result_chi2[k][-50:-1], 1)[0] MixedError = Result_chi2[k][-1] * (1 - np.arctan(ErrorSlope) / (np.pi / 2)) Comparaison[0].append(k) Comparaison[1].append(MixedError) k = Comparaison[0][Comparaison[1].index(max(Comparaison[1]))] destroyed_NN.append(k) if epoch % 100 == 0: print('epoch : ' + str(epoch) + "/" + str(num_epochs) + "\r", end="") for k in range(9): if k not in destroyed_NN: my_NN = list_net[k] return my_NN, Result_chi2
x = x.astype(float) means, sd = NN.get_moments(x) x = NN.standardize(x, means, sd) #x = x/255 x_test = np.delete(x_test, np.s_[:1], 1) x_test = np.transpose(x_test) x_dim = x.shape t = np.zeros((10, x_dim[1])) arange = np.arange(x_dim[1]) t[truth, arange] = 1 print(t) nn = NN.NeuralNet(nHidUnits, nInputs, nOutputU) # weights = initWeights(nHidUnits,nInputs,nOutputU) # w_trained = train(x,weights,nHidLay,t,sigmoid_,softmax_,600,0.1) # predict(x_test, w_trained, nHidLay, sigmoid_pos, softmax_, truth_test) #print(weights[0]) for seg in range(1, 9): startI = int((seg - 1) * 0.2 * x_dim[1]) endI = int(seg * 0.2 * x_dim[1]) trainX = np.concatenate((x[:, :startI], x[:, endI:]), axis=1) trainTruth = np.concatenate((t[:, :startI], t[:, endI:]), axis=1) nn.train(trainX, trainTruth, NN.sigmoid_, NN.softmax_, 400, 0.1) #print(w_trained[0])
my_initial_lr = 0.1 my_decay_coeff = 1 / 200 '''Construction of the neural network ''' my_layer1 = Layer.Linear(6, 5) my_layer2 = ActivationFunctions.Tanh() my_layer3 = Layer.Linear(5, 4) my_layer4 = ActivationFunctions.Tanh() my_layer5 = Layer.Linear(4, 3) my_layer6 = ActivationFunctions.Tanh() my_layer7 = Layer.Linear(3, 2) my_layer8 = ActivationFunctions.Tanh() my_layer9 = Layer.Linear(2, 1) my_layer10 = ActivationFunctions.Sigmoid() my_NN = Neural_network.NeuralNet([ my_layer1, my_layer2, my_layer3, my_layer4, my_layer5, my_layer6, my_layer7, my_layer8, my_layer9, my_layer10 ]) ## Importation of the training and testing data os.chdir(path_ini[:-4]) data_training_path = 'Data/data_train.csv' data_test_path = 'Data/data_test.csv' Data_train = pd.read_csv(data_training_path) Data_test = pd.read_csv(data_test_path) param = ['cosTBz', 'R2', 'chiProb', 'Ks_distChi', 'm_KSpipi_norm', 'Mbc_norm'] '''training set''' data_train_input = np.array(Data_train[param][:train_size]) data_train_target = np.array(Data_train[['isSignal']][:train_size])
import Neural_Network_Library.user as User ''' plt.close() # Parameters' choice '''seed ''' np.random.seed(1) '''Construction of the neural network ''' my_layer1 = Layer.Linear(6, 4) my_layer2 = ActivationFunctions.Tanh() my_layer5 = Layer.Linear(4, 2) my_layer6 = ActivationFunctions.Tanh() my_layer3 = Layer.Linear(2, 1) my_layer4 = ActivationFunctions.Sigmoid() my_NN = Neural_network.NeuralNet( [my_layer1, my_layer2, my_layer5, my_layer6, my_layer3, my_layer4]) '''Maximal number of epochs ''' Nmax = 50000 '''size of the training set and the testing set ''' train_size = 3000 test_size = 1500 '''size of the batch''' my_batch_size = 100 ''' learning rate''' my_lr = 0.0005 '''importation of the training and testing data''' os.chdir(path_ini[:-12]) #changement ici Data_train = pd.read_csv('Data/data_train.csv') Data_test = pd.read_csv('Data/data_test.csv')