some CI changed

samples/random_test.ipynb

@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sequential(\n",
+       "  (0): Layer1d(\n",
+       "    (neuron): Linear(in_features=290, out_features=100)\n",
+       "    (batch_nor): BatchNorm1d(100, eps=0.1, momentum=0.1, affine=True)\n",
+       "    (act_func): ReLU()\n",
+       "    (dropout): Dropout(p=0.3)\n",
+       "  )\n",
+       "  (1): Layer1d(\n",
+       "    (neuron): Linear(in_features=100, out_features=1)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from xenonpy.model.nn import Generator1d\n",
+    "import numpy as np\n",
+    "\n",
+    "g = Generator1d(290, 1, n_neuron=[100, 70, 50], p_drop=(0.2, 0.3, 0.4),\n",
+    "                          batch_normalize=[True], momentum=(0.1, 0.2))\n",
+    "m = g(1)\n",
+    "next(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sequential(\n",
+       "  (0): Layer1d(\n",
+       "    (neuron): Linear(in_features=290, out_features=70)\n",
+       "    (batch_nor): BatchNorm1d(70, eps=0.1, momentum=0.1, affine=True)\n",
+       "    (act_func): ReLU()\n",
+       "    (dropout): Dropout(p=0.3)\n",
+       "  )\n",
+       "  (1): Layer1d(\n",
+       "    (neuron): Linear(in_features=70, out_features=35)\n",
+       "    (batch_nor): BatchNorm1d(35, eps=0.1, momentum=0.1, affine=True)\n",
+       "    (act_func): ReLU()\n",
+       "    (dropout): Dropout(p=0.3)\n",
+       "  )\n",
+       "  (2): Layer1d(\n",
+       "    (neuron): Linear(in_features=35, out_features=1)\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from math import ceil\n",
+    "\n",
+    "g = Generator1d(290, 1, n_neuron=[100, 70, 50], p_drop=(0.2, 0.3, 0.4),\n",
+    "                          batch_normalize=[True], momentum=(0.1, 0.2))\n",
+    "\n",
+    "m = g(2, scheduler=lambda i, paras:  dict(paras, n_out=ceil(paras['n_out'] * 0.5)))\n",
+    "next(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5830"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(list(m))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(10).tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

tests/test_layer.py

@@ -0,0 +1,33 @@
+# Copyright 2017 TsumiNa. All rights reserved.
+# Use of this source code is governed by a BSD-style
+# license that can be found in the LICENSE file.
+
+from torch.nn import Module
+from xenonpy.model.nn import Generator1d
+from xenonpy.model.nn import Layer1d
+
+
+def test_layer():
+    layer = Layer1d(10, 1)
+    assert isinstance(layer, Module)
+
+
+def test_generator1d1():
+    g = Generator1d(290, 1, n_neuron=[100, 70, 50], p_drop=(0.2, 0.3, 0.4),
+                    batch_normalize=[True], momentum=(0.1, 0.2))
+    m = g(1)
+    assert len(list(m)) == 18, '3x3x2'
+
+
+def test_generator1d2():
+    g = Generator1d(290, 1, n_neuron=[100, 70, 50], p_drop=(0.2, 0.3, 0.4),
+                    batch_normalize=[True], momentum=(0.1, 0.2))
+    m = g(1, n_models=10)
+    assert len(list(m)) == 10, '0 < n_models <= 3x3x2'
+
+
+def test_generator1d3():
+    g = Generator1d(290, 1, n_neuron=[100, 70, 50], p_drop=(0.2, 0.3, 0.4),
+                    batch_normalize=[True], momentum=(0.1, 0.2))
+    m = g(1, n_models=120)
+    assert len(list(m)) == 18, 'n_models > 3x3x2, should got 3x3x2'

travis/install.sh

@@ -17,10 +17,10 @@ conda update -q conda
 conda info -a
 
 if [[ "$PYTHON_VERSION" == "3.5" ]]; then
-    conda env create -f environment_py35.yml
+    conda env create -f devtools/environment_py35.yml
     source activate xepy35
 else
-    conda env create -f environment_py36.yml
+    conda env create -f devtools/environment_py36.yml
     source activate xepy36
 fi
 

xenonpy/model/nn/__init__.py

@@ -11,5 +11,5 @@
     except ImportError:
         warnings.warn("Can't fing pytorch, will not load neural network modules.", RuntimeWarning)
     else:
-        from .base_model import *
-        from .layer import *
+        from .base import *
+        from .random_model import *