Related
I've been receiving the following error after running the following line:
transformer = preprocessing.FunctionTransformer(func=np.log1p, inverse_func=np.expm1)
scaler = preprocessing.StandardScaler()
X1_t = transformer.fit_transform(X_t)
Error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In [103], line 3
1 transformer = preprocessing.FunctionTransformer(func=np.log1p, inverse_func=np.expm1)
2 scaler = preprocessing.StandardScaler()
----> 3 X1_t = transformer.fit_transform(X_t)
4 X2_t = scaler.fit_transform(X1_t)
5 print(X2_t.shape)
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/base.py:867, in TransformerMixin.fit_transform(self, X, y, **fit_params)
863 # non-optimized default implementation; override when a better
864 # method is possible for a given clustering algorithm
865 if y is None:
866 # fit method of arity 1 (unsupervised transformation)
--> 867 return self.fit(X, **fit_params).transform(X)
868 else:
869 # fit method of arity 2 (supervised transformation)
870 return self.fit(X, y, **fit_params).transform(X)
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/preprocessing/_function_transformer.py:195, in FunctionTransformer.fit(self, X, y)
193 X = self._check_input(X, reset=True)
194 if self.check_inverse and not (self.func is None or self.inverse_func is None):
--> 195 self._check_inverse_transform(X)
196 return self
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/preprocessing/_function_transformer.py:160, in FunctionTransformer._check_inverse_transform(self, X)
157 idx_selected = slice(None, None, max(1, X.shape[0] // 100))
158 X_round_trip = self.inverse_transform(self.transform(X[idx_selected]))
--> 160 if not np.issubdtype(X.dtype, np.number):
161 raise ValueError(
162 "'check_inverse' is only supported when all the elements in `X` is"
163 " numerical."
164 )
166 if not _allclose_dense_sparse(X[idx_selected], X_round_trip):
File /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/pandas/core/generic.py:5575, in NDFrame.__getattr__(self, name)
5568 if (
5569 name not in self._internal_names_set
5570 and name not in self._metadata
5571 and name not in self._accessors
5572 and self._info_axis._can_hold_identifiers_and_holds_name(name)
5573 ):
5574 return self[name]
-> 5575 return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute 'dtype'
I was able to run this code before, but had to reinstall Jupyter notebook and when reinstalling and downloading all libraries, started getting this issue. My hypothesis is that it is related to combinations of versions of Jupyter + libraries (pandas, sklearn), but don't remember the versions I previously had.
Any idea?
I want to develop a sequential model using optimal hyperparameters derived from Keras Tuner. My code raised "Invalid argument: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:]" error.
I tried adding +1 or +10 to the entire X_train but still get the same error.
Build the sequential model:
def model_builder(hp):
model = Sequential()
# Tune the number of units in the first Dense layer
# Choose an optimal value between 32-512
hp_units1 = hp.Int('units1', min_value=32, max_value=512, step=32)
hp_units2 = hp.Int('units2', min_value=32, max_value=512, step=32)
hp_units3 = hp.Int('units3', min_value=32, max_value=512, step=32)
model.add(Dense(units=hp_units1, activation='relu'))
model.add(tf.keras.layers.Dense(units=hp_units2, activation='relu'))
model.add(tf.keras.layers.Dense(units=hp_units3, activation='relu'))
model.add(Dense(1, kernel_initializer='normal', activation='linear')) # output layer
# Tune the learning rate for the optimizer
# Choose an optimal value from 0.01, 0.001, or 0.0001
hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])
# Performance visualization callback
performance_viz_cbk = PerformanceVisualizationCallback(
model=model,
test_data=X_test,
image_dir='c:\perorfmance_charts')
# Model summary
model.compile(optimizer=Adam(learning_rate=hp_learning_rate),
loss=SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy', tf.keras.metrics.AUC(), MulticlassTruePositives()])
return model
Hyperparameter tuning
tuner = kt.Hyperband(model_builder,
objective='val_accuracy',
max_epochs=10,
factor=3,
directory='my_dir',
project_name='intro_to_kt')
tuner.search(X_train, y_train, epochs=10, validation_split=0.2)
Traceback:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/tmp/ipykernel_17/1976310635.py in <module>
----> 1 tuner.search(X_train, y_train, epochs=10, validation_split=0.2)
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/base_tuner.py in search(self, *fit_args, **fit_kwargs)
177
178 self.on_trial_begin(trial)
--> 179 results = self.run_trial(trial, *fit_args, **fit_kwargs)
180 # `results` is None indicates user updated oracle in `run_trial()`.
181 if results is None:
/opt/conda/lib/python3.7/site-packages/keras_tuner/tuners/hyperband.py in run_trial(self, trial, *fit_args, **fit_kwargs)
382 fit_kwargs["epochs"] = hp.values["tuner/epochs"]
383 fit_kwargs["initial_epoch"] = hp.values["tuner/initial_epoch"]
--> 384 return super(Hyperband, self).run_trial(trial, *fit_args, **fit_kwargs)
385
386 def _build_model(self, hp):
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/tuner.py in run_trial(self, trial, *args, **kwargs)
292 callbacks.append(model_checkpoint)
293 copied_kwargs["callbacks"] = callbacks
--> 294 obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
295
296 histories.append(obj_value)
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/tuner.py in _build_and_fit_model(self, trial, *args, **kwargs)
220 hp = trial.hyperparameters
221 model = self._try_build(hp)
--> 222 results = self.hypermodel.fit(hp, model, *args, **kwargs)
223 return tuner_utils.convert_to_metrics_dict(
224 results, self.oracle.objective, "HyperModel.fit()"
/opt/conda/lib/python3.7/site-packages/keras_tuner/engine/hypermodel.py in fit(self, hp, model, *args, **kwargs)
135 If return a float, it should be the `objective` value.
136 """
--> 137 return model.fit(*args, **kwargs)
138
139
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1182 _r=1):
1183 callbacks.on_train_batch_begin(step)
-> 1184 tmp_logs = self.train_function(iterator)
1185 if data_handler.should_sync:
1186 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
948 # Lifting succeeded, so variables are initialized and we can run the
949 # stateless function.
--> 950 return self._stateless_fn(*args, **kwds)
951 else:
952 _, _, _, filtered_flat_args = \
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
3038 filtered_flat_args) = self._maybe_define_function(args, kwargs)
3039 return graph_function._call_flat(
-> 3040 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
3041
3042 #property
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1962 # No tape is watching; skip to running the function.
1963 return self._build_call_outputs(self._inference_function.call(
-> 1964 ctx, args, cancellation_manager=cancellation_manager))
1965 forward_backward = self._select_forward_and_backward_functions(
1966 args,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
594 inputs=args,
595 attrs=attrs,
--> 596 ctx=ctx)
597 else:
598 outputs = execute.execute_with_cancellation(
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (sequential/dense_3/BiasAdd:0) = ] [[-1.17921221][-1.64039445][-1.71617472]...] [y (Cast_8/x:0) = ] [0]
[[{{node assert_greater_equal/Assert/AssertGuard/else/_1/assert_greater_equal/Assert/AssertGuard/Assert}}]]
(1) Invalid argument: assertion failed: [predictions must be >= 0] [Condition x >= y did not hold element-wise:] [x (sequential/dense_3/BiasAdd:0) = ] [[-1.17921221][-1.64039445][-1.71617472]...] [y (Cast_8/x:0) = ] [0]
[[{{node assert_greater_equal/Assert/AssertGuard/else/_1/assert_greater_equal/Assert/AssertGuard/Assert}}]]
[[assert_less_equal/Assert/AssertGuard/pivot_f/_13/_41]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_7509]
Function call stack:
train_function -> train_function
Sample data:
X_train
array([[7.97469882, 3.5471509 , 5.67233186, ..., 5.40341065, 3.16356072,
4.62657322],
[4.5390077 , 5.19247562, 5.21946796, ..., 4.12139197, 5.62828788,
4.31792717],
[5.83576307, 6.42946189, 4.85759085, ..., 5.26563348, 5.48022682,
5.29933021],
[4.58240518, 5.02457724, 6.1146384 , ..., 5.47847468, 6.62666587,
5.77328054],
[4.51602713, 5.17875946, 4.57102455, ..., 4.15622882, 5.48888824,
4.88704 ]])
y_train
array([[1],
[3],
[1],
[0],
[3]])
I try to transfer learn a LightningModule. The relevant part of the code is this:
class DeepFilteringTransferLearning(pl.LightningModule):
def __init__(self, chk_path = None):
super().__init__()
#init class members
self.prediction = []
self.label = []
self.loss = MSELoss()
#init pretrained model
self.chk_path = chk_path
model = DeepFiltering.load_from_checkpoint(chk_path)
backbone = model.sequential
layers = list(backbone.children())[:-1]
self.groundModel = Sequential(*layers)
#use the pretrained modell the same way to regress Lshall and neq
self.regressor = nn.Linear(64,2)
def forward(self, x):
self.groundModel.eval()
with torch.no_grad():
groundOut = self.groundModel(x)
yPred = self.regressor(groundOut)
return yPred
I save my model in a different, main file which relevant part is:
#callbacks
callbacks = [
ModelCheckpoint(
dirpath = "checkpoints/maxPooling16StandardizedL2RegularizedReproduceableSeeded42Ampl1ConvTransferLearned",
save_top_k=5,
monitor="val_loss",
),
]
#trainer
trainer = pl.Trainer(gpus=[1,2],strategy="dp",max_epochs=150,logger=wandb_logger,callbacks=callbacks,precision=32,deterministic=True)
trainer.fit(model,train_dataloaders=trainDl,val_dataloaders=valDl)
After try to load the modell from checkpoint like this:
chk_patH = "path/to/transfer_learned/model"
standardizedL2RegularizedL1 = DeepFilteringTransferLearning("path/to/model/trying/to/use/for/transfer_learning").load_from_checkpoint(chk_patH)
I got the following error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/torch/serialization.py in _check_seekable(f)
307 try:
--> 308 f.seek(f.tell())
309 return True
AttributeError: 'NoneType' object has no attribute 'seek'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-6-13f5fd0c7b85> in <module>
1 chk_patH = "checkpoints/maxPooling16StandardizedL2RegularizedReproduceableSeeded42Ampl1/epoch=4-step=349.ckpt"
----> 2 standardizedL2RegularizedL1 = DeepFilteringTransferLearning("checkpoints/maxPooling16StandardizedL2RegularizedReproduceableSeeded42Ampl2/epoch=145-step=10219.ckpt").load_from_checkpoint(chk_patH)
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/pytorch_lightning/core/saving.py in load_from_checkpoint(cls, checkpoint_path, map_location, hparams_file, strict, **kwargs)
154 checkpoint[cls.CHECKPOINT_HYPER_PARAMS_KEY].update(kwargs)
155
--> 156 model = cls._load_model_state(checkpoint, strict=strict, **kwargs)
157 return model
158
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/pytorch_lightning/core/saving.py in _load_model_state(cls, checkpoint, strict, **cls_kwargs_new)
196 _cls_kwargs = {k: v for k, v in _cls_kwargs.items() if k in cls_init_args_name}
197
--> 198 model = cls(**_cls_kwargs)
199
200 # give model a chance to load something
~/whistlerProject/gitHub/whistler/mathe/gwInspired/deepFilteringTransferLearning.py in __init__(self, chk_path)
34 #init pretrained model
35 self.chk_path = chk_path
---> 36 model = DeepFiltering.load_from_checkpoint(chk_path)
37 backbone = model.sequential
38 layers = list(backbone.children())[:-1]
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/pytorch_lightning/core/saving.py in load_from_checkpoint(cls, checkpoint_path, map_location, hparams_file, strict, **kwargs)
132 checkpoint = pl_load(checkpoint_path, map_location=map_location)
133 else:
--> 134 checkpoint = pl_load(checkpoint_path, map_location=lambda storage, loc: storage)
135
136 if hparams_file is not None:
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/pytorch_lightning/utilities/cloud_io.py in load(path_or_url, map_location)
31 if not isinstance(path_or_url, (str, Path)):
32 # any sort of BytesIO or similiar
---> 33 return torch.load(path_or_url, map_location=map_location)
34 if str(path_or_url).startswith("http"):
35 return torch.hub.load_state_dict_from_url(str(path_or_url), map_location=map_location)
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/torch/serialization.py in load(f, map_location, pickle_module, **pickle_load_args)
579 pickle_load_args['encoding'] = 'utf-8'
580
--> 581 with _open_file_like(f, 'rb') as opened_file:
582 if _is_zipfile(opened_file):
583 # The zipfile reader is going to advance the current file position.
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/torch/serialization.py in _open_file_like(name_or_buffer, mode)
233 return _open_buffer_writer(name_or_buffer)
234 elif 'r' in mode:
--> 235 return _open_buffer_reader(name_or_buffer)
236 else:
237 raise RuntimeError(f"Expected 'r' or 'w' in mode but got {mode}")
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/torch/serialization.py in __init__(self, buffer)
218 def __init__(self, buffer):
219 super(_open_buffer_reader, self).__init__(buffer)
--> 220 _check_seekable(buffer)
221
222
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/torch/serialization.py in _check_seekable(f)
309 return True
310 except (io.UnsupportedOperation, AttributeError) as e:
--> 311 raise_err_msg(["seek", "tell"], e)
312 return False
313
~/anaconda3/envs/skimageTrial/lib/python3.6/site-packages/torch/serialization.py in raise_err_msg(patterns, e)
302 + " Please pre-load the data into a buffer like io.BytesIO and"
303 + " try to load from it instead.")
--> 304 raise type(e)(msg)
305 raise e
306
AttributeError: 'NoneType' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead.
which I can't resolve.
I try to this according to the available tutorials on the official page of pytorch lightning here. I can't figure it out what I miss.
Could somebody point me in the right direction?
I'm trying to clean my data in jupyterlab by watching several tutorials, but I keep getting one or the other error every time. So I thought I'd come on stack overflow and ask if someone can help me.
This is the csv file I want to clean: https://1drv.ms/u/s!AvOXB8kb-IHBgjaveis044GVoPpk
I'm building a machine learning model so I want to convert all the object values, but I don't know how to.
EDIT: I tried cleaning the data from scratch.
My code input:
import pandas as pd
from sklearn.tree import DecisionTreeClassifier
criminal_data = pd.read_csv('database2.csv')
X = criminal_data.drop(columns=['Agency Type', 'City', 'State',
'Crime Solved'])
y = criminal_data['City']
model = DecisionTreeClassifier()
model.fit(X, y)
criminal_data
The error message:
ValueError Traceback (most recent call
last)
<ipython-input-117-4b6968f9994f> in <module>
6 y = criminal_data['City']
7 model = DecisionTreeClassifier()
----> 8 model.fit(X, y)
9 criminal_data
~\anaconda3\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
896 """
897
--> 898 super().fit(
899 X, y,
900 sample_weight=sample_weight,
~\anaconda3\lib\site-packages\sklearn\tree\_classes.py in fit(self, X, y, sample_weight, check_input, X_idx_sorted)
154 check_X_params = dict(dtype=DTYPE, accept_sparse="csc")
155 check_y_params = dict(ensure_2d=False, dtype=None)
--> 156 X, y = self._validate_data(X, y,
157 validate_separately=(check_X_params,
158 check_y_params))
~\anaconda3\lib\site-packages\sklearn\base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
428 # :(
429 check_X_params, check_y_params =
validate_separately
--> 430 X = check_array(X, **check_X_params)
431 y = check_array(y, **check_y_params)
432 else:
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in
check_array(array, accept_sparse, accept_large_sparse, dtype, order,
copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
614 array = array.astype(dtype, casting="unsafe",
copy=False)
615 else:
--> 616 array = np.asarray(array, order=order, dtype=dtype)
617 except ComplexWarning as complex_warning:
618 raise ValueError("Complex data not supported\n"
~\anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype, order, like)
100 return _asarray_with_like(a, dtype=dtype, order=order,
like=like)
101
--> 102 return array(a, dtype, copy=False, order=order)
103
104
~\anaconda3\lib\site-packages\pandas\core\generic.py in __array__(self, dtype)
1897
1898 def __array__(self, dtype=None) -> np.ndarray:
-> 1899 return np.asarray(self._values, dtype=dtype)
1900
1901 def __array_wrap__(
~\anaconda3\lib\site-packages\numpy\core\_asarray.py in asarray(a, dtype,
order, like)
100 return _asarray_with_like(a, dtype=dtype, order=order,
like=like)
101
--> 102 return array(a, dtype, copy=False, order=order)
103
104
ValueError: could not convert string to float: 'Anchorage'
You are trying to train your model with some data that is not numerical. Before using the model, you need to do encoding. You can try LabelEncoder for that.
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
for column_name in X.columns:
if X[column_name].dtype == object:
X[column_name] = le.fit_transform(X[column_name])
else:
pass
If you have a combination of different data types in a row. Try below:
from sklearn import preprocessing
le = preprocessing.LabelEncoder()
for column_name in X.columns:
X[column_name] = X[column_name].replace(np.nan, 'none', regex=True)
X[column_name] = le.fit_transform(X[column_name].astype(str))
I am a beginner to GNNs and I was trying out a code for predicting drug toxicity using DeepChem's Tox21 dataset. It is a dataset with a training set of 12 thousand compounds and test set of 650 compounds. I need in help in debugging and rectifying this error:"TypeError: 'NoneType' object is not subscriptable", which I get at the end.
Here is the code snippet:
model = GraphConvModel(len(tox21_tasks),
batch_size=32,
mode='classification')
print("Fitting the model")
model.fit(train_dataset, nb_epoch=10)
And here is my error:
TypeError Traceback (most recent call last)
<ipython-input-5-8088249b7fd6> in <module>
4 mode='classification')
5 print("Fitting the model")
----> 6 model.fit(train_dataset, nb_epoch=10)
~\anaconda3\lib\site-packages\deepchem\models\keras_model.py in fit(self, dataset, nb_epoch, max_checkpoints_to_keep, checkpoint_interval, deterministic, restore, variables, loss, callbacks, all_losses)
322 dataset, epochs=nb_epoch,
323 deterministic=deterministic), max_checkpoints_to_keep,
--> 324 checkpoint_interval, restore, variables, loss, callbacks, all_losses)
325
326 def fit_generator(self,
~\anaconda3\lib\site-packages\deepchem\models\keras_model.py in fit_generator(self, generator, max_checkpoints_to_keep, checkpoint_interval, restore, variables, loss, callbacks, all_losses)
407 inputs = inputs[0]
408
--> 409 batch_loss = apply_gradient_for_batch(inputs, labels, weights, loss)
410 current_step = self._global_step.numpy()
411
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in __call__(self, *args, **kwds)
455
456 tracing_count = self._get_tracing_count()
--> 457 result = self._call(*args, **kwds)
458 if tracing_count == self._get_tracing_count():
459 self._call_counter.called_without_tracing()
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in _call(self, *args, **kwds)
501 # This is the first call of __call__, so we have to initialize.
502 initializer_map = object_identity.ObjectIdentityDictionary()
--> 503 self._initialize(args, kwds, add_initializers_to=initializer_map)
504 finally:
505 # At this point we know that the initialization is complete (or less
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
406 self._concrete_stateful_fn = (
407 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 408 *args, **kwds))
409
410 def invalid_creator_scope(*unused_args, **unused_kwds):
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
1846 if self.input_signature:
1847 args, kwargs = None, None
-> 1848 graph_function, _, _ = self._maybe_define_function(args, kwargs)
1849 return graph_function
1850
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _maybe_define_function(self, args, kwargs)
2148 graph_function = self._function_cache.primary.get(cache_key, None)
2149 if graph_function is None:
-> 2150 graph_function = self._create_graph_function(args, kwargs)
2151 self._function_cache.primary[cache_key] = graph_function
2152 return graph_function, args, kwargs
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
2039 arg_names=arg_names,
2040 override_flat_arg_shapes=override_flat_arg_shapes,
-> 2041 capture_by_value=self._capture_by_value),
2042 self._function_attributes,
2043 # Tell the ConcreteFunction to clean up its graph once it goes out of
~\anaconda3\lib\site-packages\tensorflow_core\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
913 converted_func)
914
--> 915 func_outputs = python_func(*func_args, **func_kwargs)
916
917 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
~\anaconda3\lib\site-packages\tensorflow_core\python\eager\def_function.py in wrapped_fn(*args, **kwds)
356 # __wrapped__ allows AutoGraph to swap in a converted function. We give
357 # the function a weak reference to itself to avoid a reference cycle.
--> 358 return weak_wrapped_fn().__wrapped__(*args, **kwds)
359 weak_wrapped_fn = weakref.ref(wrapped_fn)
360
~\anaconda3\lib\site-packages\tensorflow_core\python\framework\func_graph.py in wrapper(*args, **kwargs)
903 except Exception as e: # pylint:disable=broad-except
904 if hasattr(e, "ag_error_metadata"):
--> 905 raise e.ag_error_metadata.to_exception(e)
906 else:
907 raise
TypeError: in converted code:
relative to C:\Users\Madiha\anaconda3\lib\site-packages:
deepchem\models\keras_model.py:474 apply_gradient_for_batch *
grads = tape.gradient(batch_loss, vars)
tensorflow_core\python\eager\backprop.py:1014 gradient
unconnected_gradients=unconnected_gradients)
tensorflow_core\python\eager\imperative_grad.py:76 imperative_grad
compat.as_str(unconnected_gradients.value))
tensorflow_core\python\eager\backprop.py:138 _gradient_function
return grad_fn(mock_op, *out_grads)
tensorflow_core\python\ops\math_grad.py:455 _UnsortedSegmentMaxGrad
return _UnsortedSegmentMinOrMaxGrad(op, grad)
tensorflow_core\python\ops\math_grad.py:432 _UnsortedSegmentMinOrMaxGrad
_GatherDropNegatives(op.outputs[0], op.inputs[1])
TypeError: 'NoneType' object is not subscriptable
As an advise, check some examples on the DeepChem website. Here is a code which will work:
tasks, datasets, transformers = dc.molnet.load_tox21(featurizer='GraphConv')
train_dataset, valid_dataset, test_dataset = datasets
model = dc.models.GraphConvModel(len(tasks),
batch_size=32,
mode='classification')
print("Fitting the model")
model.fit(train_dataset)
Hope is work for you!