Shua Dissen
135 lines
3.6 KiB
Python
135 lines
3.6 KiB
Python
from __future__ import print_function, division
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import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import torch.nn.functional as F
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from torch import optim
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import numpy as np
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train_data = np.load("timitTrain.npy")
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test_data = np.load("timitTest.npy")
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Xtrain = train_data[:,5:].astype(np.float32)
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Ytrain = train_data[:,1:5].astype(np.float32)
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Xtest = test_data[:,5:].astype(np.float32)
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Ytest = test_data[:,1:5].astype(np.float32)
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda" if use_cuda else "cpu")
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_, D = Xtrain.shape
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K = len(Ytrain)
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print(D, K)
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class Net(nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.Dense1 = nn.Linear(D, 1024)
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self.Dense2 = nn.Linear(1024, 512)
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self.Dense3 = nn.Linear(512, 256)
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self.out = nn.Linear(256, 4)
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def forward(self, x):
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x = torch.sigmoid(self.Dense1(x))
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x = torch.sigmoid(self.Dense2(x))
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x = torch.sigmoid(self.Dense3(x))
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return self.out(x)
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loss = nn.L1Loss()
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def train(model, loss, optimizer, inputs, labels):
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inputs = Variable(inputs.to(device))
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labels = Variable(labels.to(device))
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optimizer.zero_grad()
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logits = model.forward(inputs)
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output = loss.forward(logits, labels)
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output.backward()
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optimizer.step()
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return output.item()
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def predict(model, inputs):
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inputs = Variable(inputs)
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logits = model.forward(inputs.to(device))
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return logits.data.cpu().numpy()
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torch.manual_seed(0)
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Xtrain = torch.from_numpy(Xtrain).float().to(device)
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Ytrain = torch.from_numpy(Ytrain).float().to(device)
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Xtest = torch.from_numpy(Xtest).float().to(device)
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Ytest = torch.from_numpy(Ytest).float().to(device)
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model = Net().to(device)
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optimizer = optim.Adagrad(model.parameters(), lr=0.01)
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epochs = 80
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batchSize = 20
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n_batches = Xtrain.size()[0]
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costs = []
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test_accuracies = []
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print("Starting training ")
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for i in range(epochs):
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cost = 0.0
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for j in range(n_batches):
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Xbatch = Xtrain[j*batchSize:(j+1)*batchSize]
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Ybatch = Ytrain[j*batchSize:(j+1)*batchSize]
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cost += train(model, loss, optimizer, Xbatch, Ybatch)
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loss1 = 0.0
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loss2 = 0.0
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loss3 = 0.0
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loss4 = 0.0
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max_1 = 0.0
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max_2 = 0.0
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max_3 = 0.0
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max_4 = 0.0
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list_1 = []
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list_2 = []
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list_3 = []
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list_4 = []
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print('predicting...')
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Ypred = predict(model, Xtest)
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for k in range(0, len(Ytest)):
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# print(y_hat[i])
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l1 = np.abs(float(Ytest[k, 0]) - Ypred[k, 0])
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l2 = np.abs(float(Ytest[k, 1]) - Ypred[k, 1])
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l3 = np.abs(float(Ytest[k, 2]) - Ypred[k, 2])
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l4 = np.abs(float(Ytest[k, 3]) - Ypred[k, 3])
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list_1.append(l1)
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list_2.append(l2)
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list_3.append(l3)
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list_4.append(l4)
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max_1 = max(max_1, l1)
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max_2 = max(max_2, l2)
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max_3 = max(max_3, l3)
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max_4 = max(max_4, l4)
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loss1 += l1
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loss2 += l2
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loss3 += l3
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loss4 += l4
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loss1 /= len(Ytest)
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loss2 /= len(Ytest)
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loss3 /= len(Ytest)
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loss4 /= len(Ytest)
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total_loss = loss1 + loss2 + loss3 + loss4
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total_loss /= 4.0
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print('median: %.3f %.3f %.3f %.3f' % (np.median(list_1), np.median(list_2), np.median(list_3), np.median(list_4)))
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print('max loss: %.3f %.3f %.3f %.3f' % (max_1, max_2, max_3, max_4))
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print('Real test score: %.3f %.3f %.3f %.3f' % (loss1, loss2, loss3, loss4))
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print("Epoch: %d, acc: %.3f" % (i, total_loss))
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costs.append(cost/n_batches)
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test_accuracies.append(round(total_loss, 3))
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torch.save(model.state_dict(), "LPC_NN.pt")
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print(test_accuracies) |