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128 lines
4.7 KiB
128 lines
4.7 KiB
"""
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Simple feed-forward neural network in PyTorch for baseline results on Scicite data.
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Created: July 5th, 2020
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"""
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import torch
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from utils.nn_reader import read_csv_nn
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import numpy as np
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class FeedForward(torch.nn.Module):
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"""
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Creates and trains a basic feedforward neural network.
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"""
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def __init__(self, input_size, hidden_size, output_size):
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""" Sets up all basic elements of NN. """
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super(FeedForward, self).__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.output_size = output_size
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self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
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self.tanh = torch.nn.Tanh()
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self.fc2 = torch.nn.Linear(self.hidden_size, self.output_size)
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self.sigmoid = torch.nn.Sigmoid()
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self.softmax = torch.nn.Softmax(dim=1)
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def forward(self, x):
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""" Computes output from a given input x. """
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hidden = self.fc1(x)
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tanh = self.tanh(hidden)
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output = self.fc2(tanh)
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output = self.softmax(output)
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return output
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def read_data(self):
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"""" Reads in training and test data and converts it to proper format. """
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self.X_train_, self.y_train_, self.X_test_ = read_csv_nn()
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yclass = np.array([(x[1] == 1) + 2 * (x[2] == 1) for x in self.y_train_])
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is0 = yclass == 0
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is1 = yclass == 1
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is2 = yclass == 2
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self.X0 = torch.FloatTensor(self.X_train_[is0])
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self.X1 = torch.FloatTensor(self.X_train_[is1])
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self.X2 = torch.FloatTensor(self.X_train_[is2])
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self.y0 = torch.LongTensor(np.zeros((sum(is0),)))
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self.y1 = torch.LongTensor(np.ones((sum(is1),)))
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self.y2 = torch.LongTensor(2 * np.ones((sum(is2),)))
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self.l0 = sum(is0)
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self.l1 = sum(is1)
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self.l2 = sum(is2)
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def fit(self, epochs=100, batch_size=16, lr=0.01, samples0=1000, samples1=1000, samples2=1000):
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""" Trains model, using cross entropy loss and SGD optimizer. """
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self.criterion = torch.nn.CrossEntropyLoss()
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self.optimizer = torch.optim.SGD(self.parameters(), lr)
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self.samples0 = samples0
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self.samples1 = samples1
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self.samples2 = samples2
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self.eval() # put into eval mode
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# initialize training data
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self.shuffle()
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y_pred = self.forward(self.X_train)
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before_train = self.criterion(y_pred, self.y_train)
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print('Test loss before training', before_train.item())
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# setup for batches
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l = self.samples0 + self.samples1 + self.samples2 # total length
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batch_indices = list(zip(list(range(0, l, batch_size))[:-1], list(range(16, l, batch_size))))
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batch_indices[-1] = (batch_indices[-1][0], l)
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# train model
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self.train() # put into training mode
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for epoch in range(epochs):
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batch = 0
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for a, b in batch_indices:
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self.optimizer.zero_grad()
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# forward pass
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y_pred = self.forward(self.X_train[a:b])
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loss = self.criterion(y_pred, self.y_train[a:b])
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# backward pass
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loss.backward()
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self.optimizer.step()
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batch += 1
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# get loss following epoch
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y_pred = self.forward(self.X_train)
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loss = self.criterion(y_pred, self.y_train)
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print('Epoch {}: train loss: {}'.format(epoch, loss.item()))
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# shuffle dataset
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self.shuffle()
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# display final loss
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self.eval() # back to eval mode
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y_pred = self.forward(self.X_train)
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after_train = self.criterion(y_pred, self.y_train)
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print('Training loss after training', after_train.item())
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def predict(self):
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""" Generates predictions from model, using test data. """
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# post-process to get predictions & get back to np format
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y_pred = self.forward(self.X_test)
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y_pred_np = y_pred.detach().numpy()
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predmax = np.amax(y_pred_np, axis=1)
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self.preds = 1 * (y_pred_np[:, 1] == predmax) + 2 * (y_pred_np[:, 2] == predmax)
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self.probs = y_pred.detach().numpy()
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def shuffle(self):
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""" Samples and shuffles training data. """
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# create permutations for shuffling
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p0 = torch.randperm(self.l0)
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p1 = torch.randperm(self.l1)
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p2 = torch.randperm(self.l2)
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n = self.l0 + self.l1 + self.l2
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p = torch.randperm(n)
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# sample and shuffle data
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self.X_train = \
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torch.cat((self.X0[p0][:self.samples0], self.X1[p1][:self.samples1], self.X2[p2][:self.samples2]))[p]
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self.y_train = torch.cat((self.y0[:self.samples0], self.y1[:self.samples1], self.y2[:self.samples2]))[p]
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