prepare new release

nbs/103c_models.FCNPlus.ipynb

@@ -44,13 +44,17 @@
    "source": [
     "# export\n",
     "class FCNPlus(nn.Sequential):\n",
-    "    def __init__(self, c_in, c_out, layers=[128, 256, 128], kss=[7, 5, 3], coord=False, separable=False,\n",
+    "    def __init__(self, c_in, c_out, layers=[128, 256, 128], kss=[7, 5, 3], coord=False, separable=False, use_bn=False, fc_dropout=0.,\n",
     "                 zero_norm=False, act=nn.ReLU, act_kwargs={}, residual=False):\n",
     "        assert len(layers) == len(kss)\n",
     "        backbone = _FCNBlockPlus(c_in, layers=layers, kss=kss, coord=coord, separable=separable,\n",
     "                                 zero_norm=zero_norm, act=nn.ReLU, act_kwargs={}, residual=False)\n",
     "        self.head_nf = layers[2]\n",
-    "        head = nn.Sequential(nn.AdaptiveAvgPool1d(1), Squeeze(-1), nn.Linear(layers[-1], c_out))\n",
+    "        head_layers = [nn.AdaptiveAvgPool1d(1), Squeeze(-1)]\n",
+    "        if use_bn: head_layers += [nn.BatchNorm1d(layers[-1])]\n",
+    "        if fc_dropout != 0: head_layers += [nn.Dropout(fc_dropout)]\n",
+    "        head_layers += [nn.Linear(layers[-1], c_out)]\n",
+    "        head = nn.Sequential(*head_layers)\n",
     "        super().__init__(OrderedDict([('backbone', backbone), ('head', head)]))\n",
     "\n",
     "\n",
@@ -176,101 +180,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/javascript": [
-       "IPython.notebook.save_checkpoint();"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Javascript object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converted 000_utils.ipynb.\n",
-      "Converted 000b_data.validation.ipynb.\n",
-      "Converted 000c_data.preparation.ipynb.\n",
-      "Converted 001_data.external.ipynb.\n",
-      "Converted 002_data.core.ipynb.\n",
-      "Converted 003_data.preprocessing.ipynb.\n",
-      "Converted 003b_data.transforms.ipynb.\n",
-      "Converted 003c_data.mixed_augmentation.ipynb.\n",
-      "Converted 003d_data.image.ipynb.\n",
-      "Converted 003e_data.features.ipynb.\n",
-      "Converted 005_data.tabular.ipynb.\n",
-      "Converted 006_data.mixed.ipynb.\n",
-      "Converted 007_metrics.ipynb.\n",
-      "Converted 008_learner.ipynb.\n",
-      "Converted 009_optimizer.ipynb.\n",
-      "Converted 010_callback.core.ipynb.\n",
-      "Converted 011_callback.noisy_student.ipynb.\n",
-      "Converted 012_callback.gblend.ipynb.\n",
-      "Converted 013_callback.TSBERT.ipynb.\n",
-      "Converted 100_models.layers.ipynb.\n",
-      "Converted 100b_models.utils.ipynb.\n",
-      "Converted 100c_models.explainability.ipynb.\n",
-      "Converted 101_models.ResNet.ipynb.\n",
-      "Converted 101b_models.ResNetPlus.ipynb.\n",
-      "Converted 102_models.InceptionTime.ipynb.\n",
-      "Converted 102b_models.InceptionTimePlus.ipynb.\n",
-      "Converted 103_models.MLP.ipynb.\n",
-      "Converted 103b_models.FCN.ipynb.\n",
-      "Converted 103c_models.FCNPlus.ipynb.\n",
-      "Converted 104_models.ResCNN.ipynb.\n",
-      "Converted 105_models.RNN.ipynb.\n",
-      "Converted 105_models.RNNPlus.ipynb.\n",
-      "Converted 106_models.XceptionTime.ipynb.\n",
-      "Converted 106b_models.XceptionTimePlus.ipynb.\n",
-      "Converted 107_models.RNN_FCN.ipynb.\n",
-      "Converted 107b_models.RNN_FCNPlus.ipynb.\n",
-      "Converted 108_models.TransformerModel.ipynb.\n",
-      "Converted 108b_models.TST.ipynb.\n",
-      "Converted 108c_models.TSTPlus.ipynb.\n",
-      "Converted 109_models.OmniScaleCNN.ipynb.\n",
-      "Converted 110_models.mWDN.ipynb.\n",
-      "Converted 111_models.ROCKET.ipynb.\n",
-      "Converted 111b_models.MINIROCKET.ipynb.\n",
-      "Converted 112_models.XResNet1d.ipynb.\n",
-      "Converted 112b_models.XResNet1dPlus.ipynb.\n",
-      "Converted 113_models.TCN.ipynb.\n",
-      "Converted 114_models.XCM.ipynb.\n",
-      "Converted 120_models.TabModel.ipynb.\n",
-      "Converted 130_models.MultiInputNet.ipynb.\n",
-      "Converted 140_models.misc.ipynb.\n",
-      "Converted 900_tutorials.ipynb.\n",
-      "Converted index.ipynb.\n",
-      "\n",
-      "\n",
-      "Checking folder: /Users/nacho/Documents/Machine_Learning/Jupyter_Notebooks/tsai/tsai\n",
-      "Correct conversion! 😃\n",
-      "Total time elapsed 166 s\n",
-      "Thursday 01/28/21 12:23:34 CET\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#hide\n",
     "out = create_scripts()\n",
@@ -280,7 +190,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   }

settings.ini

@@ -27,5 +27,4 @@ doc_baseurl = /
 git_url = https://github.com/timeseriesAI/tsai/tree/main/
 lib_path = tsai
 custom_sidebar = True
-recursive = True
-
+recursive = True

tsai/_nbdev.py

@@ -154,6 +154,7 @@
          "dummify": "001_utils.ipynb",
          "analyze_feature": "001_utils.ipynb",
          "analyze_array": "001_utils.ipynb",
+         "get_relpath": "001_utils.ipynb",
          "check_overlap": "010_data.validation.ipynb",
          "check_splits_overlap": "010_data.validation.ipynb",
          "leakage_finder": "010_data.validation.ipynb",

tsai/models/FCNPlus.py

@@ -8,13 +8,17 @@
 
 # Cell
 class FCNPlus(nn.Sequential):
-    def __init__(self, c_in, c_out, layers=[128, 256, 128], kss=[7, 5, 3], coord=False, separable=False,
+    def __init__(self, c_in, c_out, layers=[128, 256, 128], kss=[7, 5, 3], coord=False, separable=False, use_bn=False, fc_dropout=0.,
                  zero_norm=False, act=nn.ReLU, act_kwargs={}, residual=False):
         assert len(layers) == len(kss)
         backbone = _FCNBlockPlus(c_in, layers=layers, kss=kss, coord=coord, separable=separable,
                                  zero_norm=zero_norm, act=nn.ReLU, act_kwargs={}, residual=False)
         self.head_nf = layers[2]
-        head = nn.Sequential(nn.AdaptiveAvgPool1d(1), Squeeze(-1), nn.Linear(layers[-1], c_out))
+        head_layers = [nn.AdaptiveAvgPool1d(1), Squeeze(-1)]
+        if use_bn: head_layers += [nn.BatchNorm1d(layers[-1])]
+        if fc_dropout != 0: head_layers += [nn.Dropout(fc_dropout)]
+        head_layers += [nn.Linear(layers[-1], c_out)]
+        head = nn.Sequential(*head_layers)
         super().__init__(OrderedDict([('backbone', backbone), ('head', head)]))
 
 

tsai/utils.py

@@ -21,7 +21,7 @@
            'remove_dir', 'named_partial', 'yaml2dict', 'str2list', 'str2index', 'get_cont_cols', 'get_cat_cols',
            'alphabet', 'ALPHABET', 'get_mapping', 'map_array', 'log_tfm', 'to_sincos_time', 'plot_feature_dist',
            'rolling_moving_average', 'ffill_sequence', 'bfill_sequence', 'fbfill_sequence', 'dummify',
-           'analyze_feature', 'analyze_array']
+           'analyze_feature', 'analyze_array', 'get_relpath']
 
 # Cell
 from .imports import *
@@ -1252,4 +1252,15 @@ def analyze_array(o, bins=100, density=False, feature_names=None, clip_outliers_
             print(f"\n{f:3} {'feature':>{text_len - 4}}: {feature_name}\n")
             analyze_feature(o[:, f].flatten(), feature_name=feature_name)
     else:
-        analyze_feature(o.flatten(), feature_name=feature_names)
+        analyze_feature(o.flatten(), feature_name=feature_names)
+
+# Cell
+def get_relpath(path):
+    current_path = os.getcwd()
+    if is_listy(path):
+        relpaths = []
+        for p in path:
+            relpaths.append(os.path.relpath(p, current_path))
+        return relpaths
+    else:
+        return os.path.relpath(path, current_path)