Rename Cheminfo workspaces to have pretty names.

examples/Cheminformatics/Neural QSAR Model.lynxkite.json

@@ -1647,7 +1647,7 @@
           "type": "basic"
         },
         "params": {
-          "model_workspace": "Cheminformatics/Model_NN_SAR",
+          "model_workspace": "Cheminformatics/Model NN SAR",
           "save_as": "model"
         },
         "status": "done",
@@ -3088,7 +3088,7 @@
             }
           },
           "other": {
-            "model": "ModelConfig(model=Sequential(\n  (0) - Linear(1024, 512, bias=True): Input__tensor_1_output -> Linear_1_output\n  (1) - <function relu at 0x7f2e936854e0>: Linear_1_output -> Activation_1_output\n  (2) - Linear(512, 256, bias=True): Activation_1_output -> Linear_2_output\n  (3) - <function relu at 0x7f2e936854e0>: Linear_2_output -> Activation_2_output\n  (4) - Linear(256, 1, bias=True): Activation_2_output -> Linear_3_output\n  (5) - Identity(): Linear_3_output -> Output_1_x\n  (6) - Identity(): Output_1_x -> Output_1_x\n), model_inputs=['Input__tensor_1_output'], model_outputs=['Output_1_x'], loss_inputs=['Input__tensor_3_output', 'Output_1_x'], loss=Sequential(\n  (0) - <function mse_loss at 0x7f2e93687420>: Output_1_x, Input__tensor_3_output -> MSE_loss_2_output\n  (1) - Identity(): MSE_loss_2_output -> loss\n), optimizer_parameters={'lr': 0.0001, 'type': <OptionsFor_type.AdamW: 1>}, optimizer=AdamW (\nParameter Group 0\n    amsgrad: False\n    betas: (0.9, 0.999)\n    capturable: False\n    decoupled_weight_decay: True\n    differentiable: False\n    eps: 1e-08\n    foreach: None\n    fused: None\n    lr: 0.0001\n    maximize: False\n    weight_decay: 0.01\n), source_workspace='Cheminformatics/Model_NN_SAR', trained=True)"
+            "model": "ModelConfig(model=Sequential(\n  (0) - Linear(1024, 512, bias=True): Input__tensor_1_output -> Linear_1_output\n  (1) - <function relu at 0x7f2e936854e0>: Linear_1_output -> Activation_1_output\n  (2) - Linear(512, 256, bias=True): Activation_1_output -> Linear_2_output\n  (3) - <function relu at 0x7f2e936854e0>: Linear_2_output -> Activation_2_output\n  (4) - Linear(256, 1, bias=True): Activation_2_output -> Linear_3_output\n  (5) - Identity(): Linear_3_output -> Output_1_x\n  (6) - Identity(): Output_1_x -> Output_1_x\n), model_inputs=['Input__tensor_1_output'], model_outputs=['Output_1_x'], loss_inputs=['Input__tensor_3_output', 'Output_1_x'], loss=Sequential(\n  (0) - <function mse_loss at 0x7f2e93687420>: Output_1_x, Input__tensor_3_output -> MSE_loss_2_output\n  (1) - Identity(): MSE_loss_2_output -> loss\n), optimizer_parameters={'lr': 0.0001, 'type': <OptionsFor_type.AdamW: 1>}, optimizer=AdamW (\nParameter Group 0\n    amsgrad: False\n    betas: (0.9, 0.999)\n    capturable: False\n    decoupled_weight_decay: True\n    differentiable: False\n    eps: 1e-08\n    foreach: None\n    fused: None\n    lr: 0.0001\n    maximize: False\n    weight_decay: 0.01\n), source_workspace='Cheminformatics/Model NN SAR', trained=True)"
           },
           "relations": []
         },