pytorch code:

```
def test_torch():
import torch
import torch.nn as nn
from torch.utils import data
from torch.utils.data import dataloader
data = torch.randn(100000, 2)
y = torch.sin(data[:, 0]*data[:, 1]).unsqueeze(1)
print(y.shape)
data = torch.cat([data, y], dim=1)
model = nn.Sequential(
nn.Linear(2, 100),
nn.ReLU(),
nn.Linear(100, 100),
nn.ReLU(),
nn.Linear(100, 100),
nn.ReLU(),
nn.Linear(100, 100),
nn.ReLU(),
nn.Linear(100, 100),
nn.ReLU(),
nn.Linear(100, 100),
nn.ReLU(),
nn.Linear(100, 100),
nn.ReLU(),
nn.Linear(100, 1)
)
for p in model.parameters():
print(p.shape, p.requires_grad)
criterion = torch.nn.L1Loss()
opt = torch.optim.SGD(model.parameters(), 0.001)
dl = dataloader.DataLoader(data, batch_size=500)
for epoch in range(100):
for d in dl:
pred = model(d[:, :2])
loss = criterion(pred, d[:, 2])
opt.zero_grad()
loss.backward()
opt.step()
print("epoch:[{}], loss: {}".format(epoch, loss.item()))
```

tensorflow version:

```
def test_tf():
import tensorflow as tf
import numpy as np
from tensorflow import keras
model = tf.keras.Sequential([
keras.layers.Dense(units=10, activation='relu', input_shape=[2]),
keras.layers.Dense(units=10, activation='relu'),
keras.layers.Dense(units=10, activation='relu'),
keras.layers.Dense(units=10, activation='relu'),
keras.layers.Dense(units=10, activation='relu'),
keras.layers.Dense(units=10, activation='relu'),
keras.layers.Dense(units=10, activation='relu'),
keras.layers.Dense(units=1),
]
)
model.compile(optimizer=keras.optimizers.Adam(1e-3), loss="mean_squared_error")
xs = np.random.randn(100000, 2).astype(np.float32)
ys = np.sin(xs[:,0] * xs[:, 1])
model.fit(xs, ys, epochs=100, batch_size=500)
```