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lars-reimannlars-reimann
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feat: infer input size of forward and LSTM layers (#808)
### Summary of Changes * Users no longer need to specify input sizes of forward and LSTM layers. * The constructor parameter `output_size` of these two layers has been renamed to `neuron_count`.
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‎docs/tutorials/classification.ipynb‎

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‎docs/tutorials/convolutional_neural_network_for_image_classification.ipynb‎

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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from safeds.data.image.containers import ImageList\n",
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"\n",
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"images, filepaths = ImageList.from_files(\"data/shapes\", return_filenames=True)"
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]
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],
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"outputs": []
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{
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"cell_type": "code",
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"source": [
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"import re\n",
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"from safeds.data.tabular.containers import Column\n",
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"id": "66dcf95a3fa51f23",
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{
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"from safeds.data.labeled.containers import ImageDataset\n",
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"\n",
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"cell_type": "code",
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"source": [
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"from safeds.ml.nn.layers import (Convolutional2DLayer, FlattenLayer,\n",
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" ForwardLayer, MaxPooling2DLayer)\n",
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" Convolutional2DLayer(output_channel=32, kernel_size=3, padding=1),\n",
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" MaxPooling2DLayer(kernel_size=2, stride=2),\n",
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" FlattenLayer(),\n",
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" ForwardLayer(output_size=128),\n",
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" ForwardLayer(output_size=3),\n",
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" ForwardLayer(neuron_count=128),\n",
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" ForwardLayer(neuron_count=3),\n",
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"]"
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"cell_type": "code",
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"source": [
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"from safeds.ml.nn import NeuralNetworkClassifier\n",
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"from safeds.ml.nn.converters import InputConversionImageToColumn\n",
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"source": [
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"cnn_fitted = cnn.fit(dataset, epoch_size=32, batch_size=16)"
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"source": [
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"prediction = cnn_fitted.predict(dataset.get_input())"
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"source": [
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"shuffled_prediction = prediction.shuffle()"
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"shuffled_prediction.get_input().remove_image_by_index(list(range(9, len(prediction))))"
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"shuffled_prediction.get_output().to_list()[0:9]"
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‎docs/tutorials/machine_learning.ipynb‎

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‎src/safeds/data/labeled/containers/_tabular_dataset.py‎

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torch.Tensor(self.features._data_frame.to_numpy()).to(_get_device()),
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torch.Tensor(self.target._series.to_numpy()).to(_get_device()).unsqueeze(dim=-1),
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self.features._data_frame.to_torch().to(_get_device()),
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self.target._series.to_torch().to(_get_device()).unsqueeze(dim=-1),
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self.target._series.to_torch().to(_get_device()),
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‎src/safeds/data/tabular/transformation/__init__.py‎

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__name__,
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"Discretizer": "._discretizer:Discretizer",
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"InvertibleTableTransformer": "._table_transformer:InvertibleTableTransformer",
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"InvertibleTableTransformer": "._invertible_table_transformer:InvertibleTableTransformer",
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"LabelEncoder": "._label_encoder:LabelEncoder",
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"OneHotEncoder": "._one_hot_encoder:OneHotEncoder",
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"RangeScaler": "._range_scaler:RangeScaler",

‎src/safeds/data/tabular/transformation/_discretizer.py‎

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"""Whether the transformer is fitted."""
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"""The number of bins to be created."""
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# ------------------------------------------------------------------------------------------------------------------

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