def evaluate(model, data):
x_data = data['x']
y_data = data['y']
batch_size = 100
size = len(x_data)
correct = 0
loss_value = 0
loss = SoftmaxCrossEntropyLoss('loss')
for start_idx in range(0, size, batch_size):
end_idx = min(start_idx + batch_size, size)
x = np.array(x_data[start_idx:end_idx])
y = y_data[start_idx:end_idx]
ans = model.forward(x)
output = softmax(ans)
loss_value += len(y) * loss.forward(ans, onehot_encoding(y, 5))
correct += len(y) * calculate_acc(output, y)
return loss_value / size, correct / size
for i in range(epoch):
for start_idx in range(0, size, batch_size):
end_idx = min(start_idx + batch_size, size)
x = np.array(x_data[start_idx:end_idx])
label = y_data[start_idx:end_idx]
y = onehot_encoding(label, 5)
x = np.array(x)
val_loss, val_acc = 0, 0
val_loss, val_acc = evaluate(model, val_data)
ans = model.forward(x)
train_acc = calculate_acc(softmax(ans), label)
train_loss = loss.forward(ans, y)
log_dict = {
'step': global_step,
'train_loss': train_loss,
'train_acc': train_acc,
'val_loss': val_loss,
'val_acc': val_acc
}
logs.append(log_dict)
msg = 'epoch: %d steps: %d \n train_loss: %.3f train_acc: %.3f \n valid_loss: %.3f valid_acc: %.3f' % (
i, global_step, train_loss, train_acc, val_loss, val_acc)
print(msg)
