Merge pull request #4 from haowen-xu/examples

Examples

.coveragerc

@@ -15,6 +15,7 @@ ignore_errors = True
 omit =
     tests/*
     scripts/*
+    tensorkit/examples/*
     tensorkit/backend/pytorch_/_make_dtypes_mapper.py
     setup.py
     *.pyt

.travis.yml

@@ -8,9 +8,13 @@ install:
   - pip install --upgrade coverage coveralls
   - pip install -r requirements-dev.txt
 script:
-  # run tests with PyTorch
+  # run tests with PyTorch 1.3.1
   - pip install torch==1.3.1
   - TENSORKIT_BACKEND=PyTorch TENSORKIT_DISABLE_JIT=true coverage run -a -m pytest
   - TENSORKIT_BACKEND=PyTorch TENSORKIT_DISABLE_JIT=false coverage run -a -m pytest
+#  # run tests with PyTorch 1.4.0
+#  - pip install torch==1.4.0
+#  - TENSORKIT_BACKEND=PyTorch TENSORKIT_DISABLE_JIT=true coverage run -a -m pytest
+#  - TENSORKIT_BACKEND=PyTorch TENSORKIT_DISABLE_JIT=false coverage run -a -m pytest
 after_success:
   - coveralls

README.md

@@ -0,0 +1,6 @@
+## TensorKit
+
+### Requirements
+
+* PyTorch: 1.3.1
+

tensorkit/__init__.py

@@ -1,7 +1,8 @@
 __version__ = '0.0.1'
 
 
-from . import backend, distributions, flows, init, layers, losses, variational
+from . import (backend, distributions, flows, init, layers, optim, train,
+               utils, variational)
 from .bayes import *
 # from .distributions import *
 # from .layers import *

tensorkit/arg_check.py

@@ -22,7 +22,7 @@ def validate_positive_int(arg_name: str, arg_value) -> int:
 
 # layer argument validators
 def validate_layer(arg_name: str, layer) -> 'Module':
-    from tensorkit.tensor import is_jit_layer
+    from tensorkit.layers import is_jit_layer
     if isinstance(layer, Module) or is_jit_layer(layer):
         return layer
     else:

tensorkit/backend/losses.py → tensorkit/backend/optim.py

@@ -1,9 +1,9 @@
 from ..settings_ import settings
 
 if settings.backend == 'PyTorch':
-    from .pytorch_ import losses
-    from .pytorch_.losses import *
+    from .pytorch_ import optim
+    from .pytorch_.optim import *
 else:
     RuntimeError(f'Backend {settings.backend} not supported.')
 
-__all__ = losses.__all__
+__all__ = optim.__all__