Merge pull request #29 from dcslin/broadcast-mult-add-example
added broadcast mult, math ops, and added rnn examples
diff --git a/docs-site/docs/examples.md b/docs-site/docs/examples.md
index 421a9bb..c143563 100644
--- a/docs-site/docs/examples.md
+++ b/docs-site/docs/examples.md
@@ -50,6 +50,18 @@
| ---------- | ------------------------------------------------------------------------- | ---------------------------------------------------------------------------------- |
| Bert-Squad | [SQuAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) | [Colab](https://colab.research.google.com/drive/1kud-lUPjS_u-TkDAzihBTw0Vqr0FjCE-) |
+## Text Classification
+
+| Model | Dataset | Links |
+| ----------- | ----------- | ---------- |
+| Simple LSTM | IMDB | [python]() |
+
+## Text Ranking
+| Model | Dataset | Links |
+| ----------- | ----------- | ---------- |
+| BiLSTM | InsuranceQA | [python]() |
+
+
## Misc.
- Restricted Boltzmann Machine over the MNIST dataset, [source](),
diff --git a/docs-site/docs/tensor.md b/docs-site/docs/tensor.md
index 1889a0e..6e983b5 100644
--- a/docs-site/docs/tensor.md
+++ b/docs-site/docs/tensor.md
@@ -45,6 +45,15 @@
[0. 0.]]
```
+`tensor` transformation up to 6 dims
+``` python
+>>> a = tensor.random((2,3,4,5,6,7))
+>>> a.shape
+(2, 3, 4, 5, 6, 7)
+>>> a.reshape((2,3,4,5,7,6)).transpose((3,2,1,0,4,5)).shape
+(5, 4, 3, 2, 7, 6)
+```
+
### Tensor Arithmetic Methods
`tensor` is evaluated in real time.
@@ -58,6 +67,37 @@
[0. 0.2 0. ]]
```
+`tensor` broadcasting arithmetic:
+``` python
+>>> a
+[[1. 2. 3.]
+ [4. 5. 6.]]
+>>> b
+[[1. 2. 3.]]
+>>> a + b
+[[2. 4. 6.]
+ [5. 7. 9.]]
+>>> a * b
+[[ 1. 4. 9.]
+ [ 4. 10. 18.]]
+>>> a / b
+[[1. 1. 1. ]
+ [4. 2.5 2. ]]
+>>> a/=b # inplace operation
+>>> a
+[[1. 1. 1. ]
+ [4. 2.5 2. ]]
+```
+
+`tensor` broadcasting on matrix multiplication (GEMM)
+``` python
+>>> from singa import tensor
+>>> a = tensor.random((2,2,2,3))
+>>> b = tensor.random((2,3,4))
+>>> tensor.mult(a,b).shape
+(2, 2, 2, 4)
+```
+
### Tensor Functions
Functions in module `singa.tensor` return new `tensor` object after applying the
@@ -86,55 +126,55 @@
>>> x.to_device(device.get_default_device())
```
-### Simple Neural Network Example
+
+### use Tensor to train MLP
```python
-from singa import device
-from singa import tensor
-from singa import opt
-from singa import autograd
-class MLP:
- def __init__(self):
- self.linear1 = autograd.Linear(3, 4)
- self.linear2 = autograd.Linear(4, 5)
- def forward(self, x):
- y=self.linear1(x)
- return self.linear2(y)
-def train(model, x, t, dev, epochs=10):
- for i in range(epochs):
- y = model.forward(x)
- loss = autograd.mse_loss(y, t)
- print("loss: ", loss)
- sgd = opt.SGD()
- for p, gp in autograd.backward(loss):
- sgd.update(p, gp)
- sgd.step()
- print("training completed")
-if __name__ == "__main__":
- autograd.training = True
- model = MLP()
- dev = device.get_default_device()
- x = tensor.Tensor((2, 3), dev)
- t = tensor.Tensor((2, 5), dev)
- x.gaussian(1,1)
- t.gaussian(1,1)
- train(model, x, t, dev)
+
+"""
+ code snipet from examples/mlp/module.py
+"""
+
+label = get_label()
+data = get_data()
+
+dev = device.create_cuda_gpu_on(0)
+sgd = opt.SGD(0.05)
+
+# define tensor for input data and label
+tx = tensor.Tensor((400, 2), dev, tensor.float32)
+ty = tensor.Tensor((400,), dev, tensor.int32)
+model = MLP(data_size=2, perceptron_size=3, num_classes=2)
+
+# attached model to graph
+model.set_optimizer(sgd)
+model.compile([tx], is_train=True, use_graph=True, sequential=False)
+model.train()
+
+for i in range(1001):
+ tx.copy_from_numpy(data)
+ ty.copy_from_numpy(label)
+ out, loss = model(tx, ty, 'fp32', spars=None)
+
+ if i % 100 == 0:
+ print("training loss = ", tensor.to_numpy(loss)[0])
```
Output:
-```
-loss: [4.917431]
-loss: [2.5147934]
-loss: [2.0670078]
-loss: [1.9179827]
-loss: [1.8192691]
-loss: [1.7269677]
-loss: [1.6308627]
-loss: [1.52674]
-loss: [1.4122975]
-loss: [1.2866782]
-training completed
+``` bash
+$ python3 examples/mlp/module.py
+training loss = 0.6158037
+training loss = 0.52852553
+training loss = 0.4571422
+training loss = 0.37274635
+training loss = 0.30146334
+training loss = 0.24906921
+training loss = 0.21128304
+training loss = 0.18390492
+training loss = 0.16362564
+training loss = 0.148164
+training loss = 0.13589878
```
## Tensor Implementation
