53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
import torch
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import torch.nn as nn
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import numpy as np
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class Model(nn.Module):
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"""NN Model class"""
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def __init__(self, input_dim=300, hidden_dim=600, output_dim=1):
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"""Initializes new instance of Model class"""
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super(Model, self).__init__()
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self.input_dim = input_dim
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self.hidden_dim = hidden_dim
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self.output_dim = output_dim
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self.fc1 = nn.Linear(self.input_dim, self.hidden_dim)
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self.fc2 = nn.Linear(self.hidden_dim, self.hidden_dim)
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self.fc3 = nn.Linear(self.hidden_dim, self.output_dim)
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self.criterion = nn.BCELoss()
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self.optimizer = torch.optim.SGD(self.parameters(), lr=0.01)
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def forward(self, x):
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"""Step forward learning fn"""
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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x = torch.relu(x)
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x = self.fc3(x)
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x = torch.sigmoid(x)
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return x
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def run_training(self, X_train, Y_train, batch_size, epochs_count):
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for i in range(epochs_count):
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self.train()
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print(f"Epochs: {i + 1}/{epochs_count}")
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for i in range(0, Y_train.shape[0], batch_size):
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X = X_train[i: i + batch_size]
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X = torch.tensor(X)
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y = Y_train[i: i + batch_size]
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y = torch.tensor(
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y.astype(np.float32).to_numpy()).reshape(-1, 1)
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outputs = self(X.float())
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loss = self.criterion(outputs, y)
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self.optimizer.zero_grad()
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loss.backward()
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self.optimizer.step()
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