added README.md

README.md

@@ -2,8 +2,122 @@
 tags:
 - pytorch_model_hub_mixin
 - model_hub_mixin
+metrics:
+- mae
+library_name: pytorch
+pipeline_tag: time-series-forecasting
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# mlp-geron-time-series
+
+A multi-layer perceptron (MLP) trained on Aurelien Geron's synthetic time series dataset - from Lesson 8 of the Udacity course "Intro to TensorFlow for Deep Learning". It is a PyTorch adaptation of the TensorFlow model trained in the lesson.
+
+The time series contains a linear trend, a yearly seasonality and some white noise.
+
+The MLP takes windows of 30 time steps of the series as input, and predicts the next time step of the series as output.
+
+Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/bonus/udacity/Intro_to_TF_for_DL/Lesson_8_A.ipynb
+
+Experiment tracking: https://wandb.ai/sadhaklal/mlp-geron-time-series
+
+## Usage
+
+```
+import numpy as np
+
+# Create synthetic time series:
+def trend(time, slope=0):
+    return slope * time
+
+def seasonal_pattern(season_time):
+    """Just an arbitrary pattern; you can change it if you wish."""
+    return np.where(season_time < 0.4, np.cos(season_time * 2 * np.pi), 1 / np.exp(3 * season_time))
+
+def seasonality(time, period, amplitude=1, phase=0):
+    """Repeats the same pattern in each period."""
+    season_time = ((time + phase) % period) / period
+    return amplitude * seasonal_pattern(season_time)
+
+def white_noise(time, noise_level=1, seed=None):
+    return np.random.RandomState(seed).randn(len(time)) * noise_level
+
+time = np.arange(4 * 365 + 1) # 1 is added since every four years there is a leap year.
+baseline = 10
+slope = 0.05
+amplitude = 50
+series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)
+noise_level = 5
+noise = white_noise(time, noise_level, seed=42)
+series += noise
+
+# Define training & validation periods:
+split_time = 1000
+x_train = series[:split_time]
+x_valid = series[split_time:]
+
+# Create custom `Dataset` class; it'll be used by the `model_forecast` function below:
+import torch
+from torch.utils.data import Dataset, DataLoader
+
+class WindowDataset(Dataset):
+    def __init__(self, series, window_size):
+        window_size += 1
+        self.windows = []
+        for i in range(0, len(series) - window_size + 1, 1):
+            self.windows.append(series[i:i + window_size])
+
+    def __len__(self):
+        return len(self.windows)
+
+    def __getitem__(self, idx):
+        window = self.windows[idx]
+        return window[:-1], window[-1]
+
+# Re-create model & load weights:
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+device = torch.device("cpu")
+
+class DenseModel(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, window_size):
+        super().__init__()
+        self.dense = nn.Sequential(
+            nn.Linear(window_size, 10),
+            nn.ReLU(),
+            nn.Linear(10, 10),
+            nn.ReLU(),
+            nn.Linear(10, 1)
+        )
+
+    def forward(self, x):
+        return self.dense(x)
+
+window_size = 30
+model = DenseModel.from_pretrained("sadhaklal/mlp-geron-time-series", window_size=window_size)
+model.to(device)
+
+# Forecast on validation period:
+def model_forecast(model, series):
+    series = torch.tensor(series, dtype=torch.float32)
+    ds = WindowDataset(series, window_size)
+    dl = DataLoader(ds, batch_size=32, shuffle=False)
+    forecast = []
+    for x_batch, y_batch in dl:
+        x_batch, y_batch = x_batch.to(device), y_batch.to(device)
+        with torch.no_grad():
+            preds = model(x_batch)
+        forecast.append(preds.squeeze())
+    forecast = torch.cat(forecast)
+    return forecast.cpu().numpy()
+
+dense_forecast = model_forecast(model, series[split_time - window_size:])
+```
+
+## Metric
+
+MAE on the validation period: 5.01
+
+---
+
+This model has been pushed to the Hub using the [PyTorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration.