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add docs & examples for debug_model
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@ -147,8 +147,8 @@ config from being resolved. This means that you may not see all validation
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errors at once and some issues are only shown once previous errors have been
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fixed.
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Instead of specifying all required settings in the config file, you can rely
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on an auto-fill functionality that uses spaCy's built-in defaults. The resulting
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Instead of specifying all required settings in the config file, you can rely on
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an auto-fill functionality that uses spaCy's built-in defaults. The resulting
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full config can be written to file and used in downstream training tasks.
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```bash
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@ -381,7 +381,135 @@ will not be available.
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| `--help`, `-h` | flag | Show help message and available arguments. |
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| overrides | | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--training.use_gpu 1`. |
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<!-- TODO: document debug profile and debug model? -->
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<!-- TODO: document debug profile?-->
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### debug model {#debug-model}
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Debug a Thinc [`Model`](https://thinc.ai/docs/api-model) by running it on a
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sample text and checking how it updates its internal weights and parameters.
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```bash
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$ python -m spacy debug model [config_path] [component] [--layers] [-DIM] [-PAR] [-GRAD] [-ATTR] [-P0] [-P1] [-P2] [P3] [--gpu_id]
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```
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> #### Example 1
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>
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> ```bash
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> $ python -m spacy debug model ./config.cfg tagger -P0
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> ```
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<Accordion title="Example 1 output" spaced>
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```
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ℹ Using CPU
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ℹ Fixing random seed: 0
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ℹ Analysing model with ID 62
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========================== STEP 0 - before training ==========================
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ℹ Layer 0: model ID 62:
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'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list>>with_array-residual>>residual>>residual>>residual>>with_array-softmax'
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ℹ Layer 1: model ID 59:
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'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list>>with_array-residual>>residual>>residual>>residual'
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ℹ Layer 2: model ID 61: 'with_array-softmax'
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ℹ Layer 3: model ID 24:
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'extract_features>>list2ragged>>with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed>>with_array-maxout>>layernorm>>dropout>>ragged2list'
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ℹ Layer 4: model ID 58: 'with_array-residual>>residual>>residual>>residual'
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ℹ Layer 5: model ID 60: 'softmax'
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ℹ Layer 6: model ID 13: 'extract_features'
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ℹ Layer 7: model ID 14: 'list2ragged'
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ℹ Layer 8: model ID 16:
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'with_array-ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed'
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ℹ Layer 9: model ID 22: 'with_array-maxout>>layernorm>>dropout'
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ℹ Layer 10: model ID 23: 'ragged2list'
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ℹ Layer 11: model ID 57: 'residual>>residual>>residual>>residual'
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ℹ Layer 12: model ID 15:
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'ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed|ints-getitem>>hashembed'
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ℹ Layer 13: model ID 21: 'maxout>>layernorm>>dropout'
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ℹ Layer 14: model ID 32: 'residual'
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ℹ Layer 15: model ID 40: 'residual'
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ℹ Layer 16: model ID 48: 'residual'
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ℹ Layer 17: model ID 56: 'residual'
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ℹ Layer 18: model ID 3: 'ints-getitem>>hashembed'
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ℹ Layer 19: model ID 6: 'ints-getitem>>hashembed'
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ℹ Layer 20: model ID 9: 'ints-getitem>>hashembed'
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...
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```
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</Accordion>
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In this example log, we just print the name of each layer after creation of the
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model ("Step 0"), which helps us to understand the internal structure of the
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Neural Network, and to focus on specific layers that we want to inspect further
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(see next example).
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> #### Example 2
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>
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> ```bash
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> $ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P2
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> ```
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<Accordion title="Example 2 output" spaced>
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```
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ℹ Using CPU
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ℹ Fixing random seed: 0
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ℹ Analysing model with ID 62
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========================= STEP 0 - before training =========================
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ℹ Layer 5: model ID 60: 'softmax'
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ℹ - dim nO: None
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ℹ - dim nI: 96
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ℹ - param W: None
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ℹ - param b: None
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ℹ Layer 15: model ID 40: 'residual'
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ℹ - dim nO: None
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ℹ - dim nI: None
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======================= STEP 1 - after initialization =======================
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ℹ Layer 5: model ID 60: 'softmax'
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ℹ - dim nO: 4
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ℹ - dim nI: 96
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ℹ - param W: (4, 96) - sample: [0. 0. 0. 0. 0.]
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ℹ - param b: (4,) - sample: [0. 0. 0. 0.]
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ℹ Layer 15: model ID 40: 'residual'
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ℹ - dim nO: 96
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ℹ - dim nI: None
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========================== STEP 2 - after training ==========================
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ℹ Layer 5: model ID 60: 'softmax'
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ℹ - dim nO: 4
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ℹ - dim nI: 96
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ℹ - param W: (4, 96) - sample: [ 0.00283958 -0.00294119 0.00268396 -0.00296219
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-0.00297141]
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ℹ - param b: (4,) - sample: [0.00300002 0.00300002 0.00300002 0.00300002]
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ℹ Layer 15: model ID 40: 'residual'
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ℹ - dim nO: 96
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ℹ - dim nI: None
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```
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</Accordion>
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In this example log, we see how initialization of the model (Step 1) propagates
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the correct values for the `nI` (input) and `nO` (output) dimensions of the
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various layers. In the `softmax` layer, this step also defines the `W` matrix as
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an all-zero matrix determined by the `nO` and `nI` dimensions. After a first
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training step (Step 2), this matrix has clearly updated its values through the
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training feedback loop.
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| Argument | Type | Default | Description |
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| ----------------------- | ---------- | ------- | ---------------------------------------------------------------------------------------------------- |
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| `config_path` | positional | | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. |
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| `component` | positional | | Name of the pipeline component of which the model should be analysed. |
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| `--layers`, `-l` | option | | Comma-separated names of layer IDs to print. |
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| `--dimensions`, `-DIM` | option | `False` | Show dimensions of each layer. |
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| `--parameters`, `-PAR` | option | `False` | Show parameters of each layer. |
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| `--gradients`, `-GRAD` | option | `False` | Show gradients of each layer. |
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| `--attributes`, `-ATTR` | option | `False` | Show attributes of each layer. |
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| `--print-step0`, `-P0` | option | `False` | Print model before training. |
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| `--print-step1`, `-P1` | option | `False` | Print model after initialization. |
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| `--print-step2`, `-P2` | option | `False` | Print model after training. |
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| `--print-step3`, `-P3` | option | `False` | Print final predictions. |
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| `--help`, `-h` | flag | | Show help message and available arguments. |
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## Train {#train}
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