我正在使用structureddatacclassifier来训练模型，经过几次尝试后，我遇到了以下错误:
Trial 3 Complete [00h 00m 23s]
val_accuracy: 0.9289383292198181
Best val_accuracy So Far: 0.9289383292198181
Total elapsed time: 00h 01m 02s
Search: Running Trial #4
Value |Best Value So Far |Hyperparameter
True |True |structured_data_block_1/normalize
False |False |structured_data_block_1/dense_block_1/use_batchnorm
2 |2 |structured_data_block_1/dense_block_1/num_layers
32 |32 |structured_data_block_1/dense_block_1/units_0
0 |0 |structured_data_block_1/dense_block_1/dropout
32 |32 |structured_data_block_1/dense_block_1/units_1
0 |0 |classification_head_1/dropout
adam |adam |optimizer
0.01 |0.001 |learning_rate
Epoch 1/1000
148/148 [==============================] - 2s 9ms/step - loss: 0.1917 - accuracy: 0.9576 - val_loss: 0.5483 - val_accuracy: 0.9289
Epoch 2/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1572 - accuracy: 0.9628 - val_loss: 0.3410 - val_accuracy: 0.9289
Epoch 3/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1434 - accuracy: 0.9628 - val_loss: 0.3330 - val_accuracy: 0.9289
Epoch 4/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1414 - accuracy: 0.9628 - val_loss: 0.3014 - val_accuracy: 0.9289
Epoch 5/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1395 - accuracy: 0.9628 - val_loss: 0.3012 - val_accuracy: 0.9289
Epoch 6/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1334 - accuracy: 0.9628 - val_loss: 0.4439 - val_accuracy: 0.9289
Epoch 7/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1370 - accuracy: 0.9628 - val_loss: 0.2964 - val_accuracy: 0.9289
Epoch 8/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1309 - accuracy: 0.9628 - val_loss: 0.2949 - val_accuracy: 0.9289
Epoch 9/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1282 - accuracy: 0.9628 - val_loss: 0.2927 - val_accuracy: 0.9289
Epoch 10/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1301 - accuracy: 0.9628 - val_loss: 0.2937 - val_accuracy: 0.9289
Epoch 11/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1278 - accuracy: 0.9628 - val_loss: 0.3152 - val_accuracy: 0.9289
Epoch 12/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1270 - accuracy: 0.9628 - val_loss: 0.3062 - val_accuracy: 0.9289
Epoch 13/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1286 - accuracy: 0.9628 - val_loss: 0.3198 - val_accuracy: 0.9289
Epoch 14/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1268 - accuracy: 0.9628 - val_loss: 0.3318 - val_accuracy: 0.9289
Epoch 15/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1244 - accuracy: 0.9628 - val_loss: 0.3038 - val_accuracy: 0.9289
Epoch 16/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1239 - accuracy: 0.9628 - val_loss: 0.3050 - val_accuracy: 0.9289
Epoch 17/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1222 - accuracy: 0.9628 - val_loss: 0.3180 - val_accuracy: 0.9289
Epoch 18/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1239 - accuracy: 0.9628 - val_loss: 0.3298 - val_accuracy: 0.9289
Epoch 19/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1220 - accuracy: 0.9628 - val_loss: 0.2916 - val_accuracy: 0.9289
Epoch 20/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1203 - accuracy: 0.9630 - val_loss: 0.3548 - val_accuracy: 0.9289
Epoch 21/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1243 - accuracy: 0.9628 - val_loss: 0.3047 - val_accuracy: 0.9289
Epoch 22/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1208 - accuracy: 0.9633 - val_loss: 0.4035 - val_accuracy: 0.9289
Epoch 23/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1242 - accuracy: 0.9628 - val_loss: 0.3383 - val_accuracy: 0.9289
Epoch 24/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1181 - accuracy: 0.9635 - val_loss: 0.3576 - val_accuracy: 0.9289
Epoch 25/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1171 - accuracy: 0.9641 - val_loss: 0.3221 - val_accuracy: 0.9289
Epoch 26/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1149 - accuracy: 0.9635 - val_loss: 0.3314 - val_accuracy: 0.9289
Epoch 27/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1136 - accuracy: 0.9635 - val_loss: 0.3554 - val_accuracy: 0.9289
Epoch 28/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1196 - accuracy: 0.9633 - val_loss: 0.3311 - val_accuracy: 0.9289
Epoch 29/1000
148/148 [==============================] - 1s 8ms/step - loss: 0.1176 - accuracy: 0.9635 - val_loss: 0.3684 - val_accuracy: 0.9289
Trial 4 Complete [00h 00m 36s]
val_accuracy: 0.9289383292198181
Best val_accuracy So Far: 0.9289383292198181
Total elapsed time: 00h 01m 37s
Search: Running Trial #5
Value |Best Value So Far |Hyperparameter
True |True |structured_data_block_1/normalize
False |False |structured_data_block_1/dense_block_1/use_batchnorm
2 |2 |structured_data_block_1/dense_block_1/num_layers
32 |32 |structured_data_block_1/dense_block_1/units_0
0 |0 |structured_data_block_1/dense_block_1/dropout
32 |32 |structured_data_block_1/dense_block_1/units_1
0 |0 |classification_head_1/dropout
adam_weight_decay |adam |optimizer
0.001 |0.001 |learning_rate
Epoch 1/1000
2022-12-11 16:22:23.607384: W tensorflow/core/framework/op_kernel.cc:1807] OP_REQUIRES failed at cast_op.cc:121 : UNIMPLEMENTED: Cast string to
float is not supported
2022-12-11 16:22:23.607506: W tensorflow/core/framework/op_kernel.cc:1807] OP_REQUIRES failed at cast_op.cc:121 : UNIMPLEMENTED: Cast string to
float is not supported
Traceback (most recent call last):
File "/home/anand/automl/automl.py", line 30, in <module>
clf.fit(x=X_train, y=y_train, use_multiprocessing=True, workers=8, verbose=True)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/tasks/structured_data.py", line 326, in fit
history = super().fit(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/tasks/structured_data.py", line 139, in fit
history = super().fit(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/auto_model.py", line 292, in fit
history = self.tuner.search(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/engine/tuner.py", line 193, in search
super().search(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras_tuner/engine/base_tuner.py", line 183, in search
results = self.run_trial(trial, *fit_args, **fit_kwargs)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras_tuner/engine/tuner.py", line 295, in run_trial
obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/engine/tuner.py", line 101, in _build_and_fit_model
_, history = utils.fit_with_adaptive_batch_size(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/utils/utils.py", line 88, in fit_with_adaptive_batch_size
history = run_with_adaptive_batch_size(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/utils/utils.py", line 101, in run_with_adaptive_batch_size
history = func(x=x, validation_data=validation_data, **fit_kwargs)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/utils/utils.py", line 89, in <lambda>
batch_size, lambda **kwargs: model.fit(**kwargs), **fit_kwargs
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "/home/anand/automl/.venv/lib/python3.10/site-packages/tensorflow/python/eager/execute.py", line 52, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.UnimplementedError: Graph execution error:
Detected at node 'Cast_1' defined at (most recent call last):
File "/home/anand/automl/automl.py", line 30, in <module>
clf.fit(x=X_train, y=y_train, use_multiprocessing=True, workers=8, verbose=True)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/tasks/structured_data.py", line 326, in fit
history = super().fit(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/tasks/structured_data.py", line 139, in fit
history = super().fit(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/auto_model.py", line 292, in fit
history = self.tuner.search(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/engine/tuner.py", line 193, in search
super().search(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras_tuner/engine/base_tuner.py", line 183, in search
results = self.run_trial(trial, *fit_args, **fit_kwargs)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras_tuner/engine/tuner.py", line 295, in run_trial
obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/engine/tuner.py", line 101, in _build_and_fit_model
_, history = utils.fit_with_adaptive_batch_size(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/utils/utils.py", line 88, in fit_with_adaptive_batch_size
history = run_with_adaptive_batch_size(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/utils/utils.py", line 101, in run_with_adaptive_batch_size
history = func(x=x, validation_data=validation_data, **fit_kwargs)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/utils/utils.py", line 89, in <lambda>
batch_size, lambda **kwargs: model.fit(**kwargs), **fit_kwargs
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/utils/traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/engine/training.py", line 1650, in fit
tmp_logs = self.train_function(iterator)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/engine/training.py", line 1249, in train_function
return step_function(self, iterator)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/engine/training.py", line 1233, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/engine/training.py", line 1222, in run_step
outputs = model.train_step(data)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/engine/training.py", line 1027, in train_step
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 527, in minimize
self.apply_gradients(grads_and_vars)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/autokeras/keras_layers.py", line 360, in apply_gradients
return super(AdamWeightDecay, self).apply_gradients(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1140, in apply_grad
ients
return super().apply_gradients(grads_and_vars, name=name)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 632, in apply_gradi
ents
self._apply_weight_decay(trainable_variables)
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1159, in _apply_wei
ght_decay
tf.__internal__.distribute.interim.maybe_merge_call(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1155, in distribute
d_apply_weight_decay
distribution.extended.update(
File "/home/anand/automl/.venv/lib/python3.10/site-packages/keras/optimizers/optimizer_experimental/optimizer.py", line 1151, in weight_dec
ay_fn
wd = tf.cast(self.weight_decay, variable.dtype)
Node: 'Cast_1'
2 root error(s) found.
(0) UNIMPLEMENTED: Cast string to float is not supported
[[{{node Cast_1}}]]
(1) CANCELLED: Function was cancelled before it was started
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_70943]
import tensorflow as tf
import pandas as pd
import numpy as np
import autokeras as ak
from sklearn.model_selection import LeaveOneGroupOut
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.preprocessing import LabelEncoder
data = pd.read_csv("p_feature_df.csv")
y = data.pop('is_p')
y = y.astype(np.int32)
data.pop('idx')
groups = data.pop('owner')
data = data.astype(np.float32)
X = data.to_numpy()
lb = LabelEncoder()
y = lb.fit_transform(y)
logo = LeaveOneGroupOut()
logo.get_n_splits(X,y,groups)
results = []
models = []
for train_index, test_index in logo.split(X,y,groups):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
clf = ak.StructuredDataClassifier(overwrite=True)
clf.fit(x=X_train, y=y_train, use_multiprocessing=True, workers=8, verbose=True)
loss, acc = clf.evaluate(x=X_test, y=y_test, verbose=True)
results.append( (loss, acc))
models.append(clf)
print( (loss, acc) )`
使用adam_weight_decay时代码失败。
这里也是一样我认为这与autokeras在colab和本地pc上制作的一些下载有关文件是:
https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50v2_weights_tf_dim_ordering_tf_kernels_notop.h5
我也遇到了同样的问题。我通过创建一个autommodel (basecclass)来解决这个问题，如:
input_node = ak.StructuredDataInput()
output_node = ak.DenseBlock(use_batchnorm=True)(input_node)
output_node = ak.DenseBlock(dropout=0.1)(output_node)
output_node = ak.DenseBlock(use_batchnorm=True)(input_node)
output_node = ak.ClassificationHead()(output_node)
clf = ak.AutoModel(
inputs=input_node, outputs=output_node, overwrite=True )
这似乎是structureddatacclassifier中的一个错误