|
137 | 137 | }, |
138 | 138 | { |
139 | 139 | "cell_type": "code", |
140 | | - "execution_count": 1, |
| 140 | + "execution_count": null, |
141 | 141 | "metadata": {}, |
142 | | - "outputs": [ |
143 | | - { |
144 | | - "name": "stdout", |
145 | | - "output_type": "stream", |
146 | | - "text": [ |
147 | | - "Loading data from /Users/bartz/miniforge3/envs/spotCondaEnv/lib/python3.11/site-packages/spotPython/data/data.csv\n", |
148 | | - "torch.Size([11, 64])\n", |
149 | | - "torch.Size([11])\n" |
150 | | - ] |
151 | | - } |
152 | | - ], |
| 142 | + "outputs": [], |
153 | 143 | "source": [ |
154 | 144 | "from spotPython.data.csvdataset import CSVDataset\n", |
155 | 145 | "# dataset = CSVDataset(csv_file='./data/spotPython/data.csv', target_column='prognosis')\n", |
|
160 | 150 | }, |
161 | 151 | { |
162 | 152 | "cell_type": "code", |
163 | | - "execution_count": 5, |
| 153 | + "execution_count": null, |
164 | 154 | "metadata": {}, |
165 | | - "outputs": [ |
166 | | - { |
167 | | - "data": { |
168 | | - "text/plain": [ |
169 | | - "'Split: Train'" |
170 | | - ] |
171 | | - }, |
172 | | - "execution_count": 5, |
173 | | - "metadata": {}, |
174 | | - "output_type": "execute_result" |
175 | | - } |
176 | | - ], |
| 155 | + "outputs": [], |
177 | 156 | "source": [ |
178 | 157 | "dataset.extra_repr()" |
179 | 158 | ] |
180 | 159 | }, |
181 | 160 | { |
182 | 161 | "cell_type": "code", |
183 | | - "execution_count": 6, |
| 162 | + "execution_count": null, |
184 | 163 | "metadata": {}, |
185 | | - "outputs": [ |
186 | | - { |
187 | | - "name": "stdout", |
188 | | - "output_type": "stream", |
189 | | - "text": [ |
190 | | - "Batch Size: 3\n", |
191 | | - "---------------\n", |
192 | | - "Inputs: tensor([[1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n", |
193 | | - " 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n", |
194 | | - " 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0],\n", |
195 | | - " [0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0,\n", |
196 | | - " 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1,\n", |
197 | | - " 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", |
198 | | - " [1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n", |
199 | | - " 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0,\n", |
200 | | - " 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1]])\n", |
201 | | - "Targets: tensor([6, 8, 3])\n" |
202 | | - ] |
203 | | - } |
204 | | - ], |
| 164 | + "outputs": [], |
205 | 165 | "source": [ |
206 | 166 | "from torch.utils.data import DataLoader\n", |
207 | 167 | "# Set batch size for DataLoader\n", |
|
394 | 354 | }, |
395 | 355 | { |
396 | 356 | "cell_type": "code", |
397 | | - "execution_count": 4, |
| 357 | + "execution_count": 1, |
398 | 358 | "metadata": {}, |
399 | 359 | "outputs": [], |
400 | 360 | "source": [ |
|
405 | 365 | }, |
406 | 366 | { |
407 | 367 | "cell_type": "code", |
408 | | - "execution_count": 3, |
| 368 | + "execution_count": 2, |
409 | 369 | "metadata": {}, |
410 | 370 | "outputs": [ |
411 | 371 | { |
|
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