# self.last_fc = nn.Linear(in_features=in_size, out_features=output_size)
# def forward(self, x):
# for i, fc in enumerate(self.fcs):
# x = fc(x)
# x = nn.ReLU()(x)
# out = self.last_fc(x)
# return nn.Sigmoid()(out)
# Set device to be used
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print('Device used:', device)
model_pytorch = SimpleModel(input_size=input_size,
hidden_sizes=hidden_sizes,
output_size=output_size)
model_pytorch = model_pytorch.to(device)
# Set loss and optimizer
# Set binary cross entropy loss since 2 classes only
criterion = nn.BCELoss()
optimizer = optim.Adam(model_pytorch.parameters(), lr=1e-3)
num_epochs = 20
# Train model
time_start = time.time()
for epoch in range(num_epochs):
model_pytorch.train()
train_loss_total = 0
import numpy as np
import torch
test_size = 2000
input_size = 20
hidden_sizes = [50, 50]
output_size = 1
X_test = np.random.randn(test_size, input_size).astype(np.float32)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_pytorch = SimpleModel(input_size=input_size,
hidden_sizes=hidden_sizes,
output_size=output_size)
model_pytorch = model_pytorch.to(device) # important...
model_pytorch.load_state_dict(torch.load('./models/model_simple.pt'))
dummy_input = torch.from_numpy(X_test[0].reshape(1, -1)).float().to(device)
dummy_output = model_pytorch(dummy_input)
print(dummy_output)
# Export to ONNX format
torch.onnx.export(model_pytorch,
dummy_input,
'./models/model_simple.onnx',
input_names=['test_input'],
output_names=['test_output'])
