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Meezo ML

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دورات

أجوبة بواسطة Meezo ML

  2. ظهور الخطأ التالي ImportError: cannot import name 'VGG16' from 'keras.applications' (/usr/local/lib/python3.7/dist-packages/keras/applications/__init__.py) أثناء محاولة استيراد VGG16

    في بايثون

    نشر

    أريد استخدام  VGG16 ولكن لا أعرف ما المشكلة في عملية استدعاء الموديل على Google Colab: 

    import keras
import numpy as np
from keras.applications import VGG16
import pandas as pd
import matplotlib.pyplot
---------------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)

<ipython-input-5-46922636139b> in <module>()
      1 import keras
  	  2 import numpy as np
----> 3 from keras.applications import VGG16

ImportError: cannot import name 'VGG16' from 'keras.applications' (/usr/local/lib/python3.7/dist-packages/keras/applications/__init__.py)

     

  3. ظهور الخطأ ValueError: Input 0 of layer simple_rnn_20 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 16) أثناء تدريب شبكة RNNs

    في أسئلة البرمجة

    نشر

    أحاول تدريب شبكة عصبية (هذه الشبكة عبارة عن تجميع طبقات RNN) لكن يظهر لي الخطأ في الكود التالي: 

    from keras.layers import Dense,Embedding,SimpleRNN
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras.models import Sequential
max_features = 1000
maxlen = 20
batch_size = 64
print('Loading data...')
(input_train, y_train), (input_test, y_test) = imdb.load_data(
num_words=max_features)
print(len(input_train), 'train sequences')
print(len(input_test), 'test sequences')
print('Pad sequences (samples x time)')
input_train = sequence.pad_sequences(input_train, maxlen=maxlen)
input_test = sequence.pad_sequences(input_test, maxlen=maxlen)
print('input_train shape:', input_train.shape)
model = Sequential()
Loading data...
25000 train sequences
25000 test sequences
Pad sequences (samples x time)
input_train shape: (25000, 20)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-45-586f07f93ca3> in <module>
     18 model.add(Embedding(max_features, 16))
     19 model.add(SimpleRNN(16))
---> 20 model.add(SimpleRNN(16))
     21 model.add(Dense(1, activation='sigmoid'))
     22 model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])

~\anaconda3\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs)
    515     self._self_setattr_tracking = False  # pylint: disable=protected-access
    516     try:
--> 517       result = method(self, *args, **kwargs)
    518     finally:
    519       self._self_setattr_tracking = previous_value  # pylint: disable=protected-access

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\sequential.py in add(self, layer)
    221       # If the model is being built continuously on top of an input layer:
    222       # refresh its output.
--> 223       output_tensor = layer(self.outputs[0])
    224       if len(nest.flatten(output_tensor)) != 1:
    225         raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG)

~\anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
    658 
    659     if initial_state is None and constants is None:
--> 660       return super(RNN, self).__call__(inputs, **kwargs)
    661 
    662     # If any of `initial_state` or `constants` are specified and are Keras

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs)
    950     if _in_functional_construction_mode(self, inputs, args, kwargs, input_list):
    951       return self._functional_construction_call(inputs, args, kwargs,
--> 952                                                 input_list)
    953 
    954     # Maintains info about the `Layer.call` stack.

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list)
   1089         # Check input assumptions set after layer building, e.g. input shape.
   1090         outputs = self._keras_tensor_symbolic_call(
-> 1091             inputs, input_masks, args, kwargs)
   1092 
   1093         if outputs is None:

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs)
    820       return nest.map_structure(keras_tensor.KerasTensor, output_signature)
    821     else:
--> 822       return self._infer_output_signature(inputs, args, kwargs, input_masks)
    823 
    824   def _infer_output_signature(self, inputs, args, kwargs, input_masks):

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks)
    860           # overridden).
    861           # TODO(kaftan): do we maybe_build here, or have we already done it?
--> 862           self._maybe_build(inputs)
    863           outputs = call_fn(inputs, *args, **kwargs)
    864 

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _maybe_build(self, inputs)
   2683     if not self.built:
   2684       input_spec.assert_input_compatibility(
-> 2685           self.input_spec, inputs, self.name)
   2686       input_list = nest.flatten(inputs)
   2687       if input_list and self._dtype_policy.compute_dtype is None:

~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
    221                          'expected ndim=' + str(spec.ndim) + ', found ndim=' +
    222                          str(ndim) + '. Full shape received: ' +
--> 223                          str(tuple(shape)))
    224     if spec.max_ndim is not None:
    225       ndim = x.shape.rank

ValueError: Input 0 of layer simple_rnn_20 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 16)


model.add(Embedding(max_features, 16))
model.add(SimpleRNN(16))
model.add(SimpleRNN(16))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(input_train, y_train,
epochs=2,
batch_size=128,
validation_split=0.2)

     

  5. ظهور الخطأ ValueError: Input 0 of layer dense_19 is incompatible with the layer: expected axis -1 of input shape to have value 128 but received input with shape (4, 32) أثناء محاولة تدريب شبكة عصبية في Keeas

    في أسئلة البرمجة

    نشر

    أقوم باستخدام keras مع ال Embedding على نموذج بسيط ولكن يظهر لدي الخطأ التالي: 

    inputx= np.random.randint(0,40, 4)
model = Sequential()
model.add(layers.Embedding(input_dim=40, output_dim=32, input_length=4))
model.add(layers.Flatten())
model.add(layers.Dense(units=5, activation='sigmoid'))
print(model(inputx))
---------------------------------------------------------------------------------------
ValueError: Input 0 of layer dense_19 is incompatible with the layer: expected axis -1 of input shape to have value 128 but received input with shape (4, 32)

     

  7. ظهور الخطأ التالي ValueError: Shapes (None, 1) and (None, 46) are incompatible أثناء محاولة تدريب شبكة عصبية في Keras

    في بايثون

    نشر · تم التعديل في بواسطة Meezo ML

    أقوم ببناء نموذج تصنيف متعدد لكن تظهر لي المشكلة التالية عندما أحاول تدريب النموذج، فما هي المشكلة: 

    from keras.datasets import reuters
(train_data, train_labels), (test_data, test_labels) = reuters.load_data(
num_words=1000)
import numpy as np
def vectorize_sequences(sequences, dimension=1000):
    results = np.zeros((len(sequences), dimension))
    for i, sequence in enumerate(sequences):
        results[i, sequence] = 1.
    return results
x_train = vectorize_sequences(train_data)
x_test = vectorize_sequences(test_data)
from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Dense(64, activation='relu', input_shape=(1000,)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(46, activation='softmax'))
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(x_train,
train_labels,
epochs=8,
batch_size=512,
validation_split=0.2)
Epoch 1/8

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-15-00a9a197f4b0> in <module>
     24 epochs=8,
     25 batch_size=512,
---> 26 validation_split=0.2)

~\anaconda3\lib\site-packages\tensorflow\python\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)
   1098                 _r=1):
   1099               callbacks.on_train_batch_begin(step)
-> 1100               tmp_logs = self.train_function(iterator)
   1101               if data_handler.should_sync:
   1102                 context.async_wait()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds)
    826     tracing_count = self.experimental_get_tracing_count()
    827     with trace.Trace(self._name) as tm:
--> 828       result = self._call(*args, **kwds)
    829       compiler = "xla" if self._experimental_compile else "nonXla"
    830       new_tracing_count = self.experimental_get_tracing_count()

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
    869       # This is the first call of __call__, so we have to initialize.
    870       initializers = []
--> 871       self._initialize(args, kwds, add_initializers_to=initializers)
    872     finally:
    873       # At this point we know that the initialization is complete (or less

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to)
    724     self._concrete_stateful_fn = (
    725         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
--> 726             *args, **kwds))
    727 
    728     def invalid_creator_scope(*unused_args, **unused_kwds):

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   2967       args, kwargs = None, None
   2968     with self._lock:
-> 2969       graph_function, _ = self._maybe_define_function(args, kwargs)
   2970     return graph_function
   2971 

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
   3359 
   3360           self._function_cache.missed.add(call_context_key)
-> 3361           graph_function = self._create_graph_function(args, kwargs)
   3362           self._function_cache.primary[cache_key] = graph_function
   3363 

~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3204             arg_names=arg_names,
   3205             override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206             capture_by_value=self._capture_by_value),
   3207         self._function_attributes,
   3208         function_spec=self.function_spec,

~\anaconda3\lib\site-packages\tensorflow\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)
    988         _, original_func = tf_decorator.unwrap(python_func)
    989 
--> 990       func_outputs = python_func(*func_args, **func_kwargs)
    991 
    992       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
    632             xla_context.Exit()
    633         else:
--> 634           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
    635         return out
    636 

~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
    975           except Exception as e:  # pylint:disable=broad-except
    976             if hasattr(e, "ag_error_metadata"):
--> 977               raise e.ag_error_metadata.to_exception(e)
    978             else:
    979               raise

ValueError: in user code:

    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function  *
        return step_function(self, iterator)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step  **
        outputs = model.train_step(data)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:756 train_step
        y, y_pred, sample_weight, regularization_losses=self.losses)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:203 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:152 __call__
        losses = call_fn(y_true, y_pred)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:256 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:1537 categorical_crossentropy
        return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper
        return target(*args, **kwargs)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py:4833 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (None, 1) and (None, 46) are incompatible

     

  9. ال input_shape و ظهور الخطأ ValueError: Shapes are incompatible في Keras

    في بايثون

    نشر · تم التعديل في بواسطة Meezo ML

    أقوم بتدريب موديل في keras على minst ولكن يظهر لدي الخطأ التالي خلال تنفيذ الكود: 

    from keras.models import Sequential
from keras import layers
import tensorflow as tf
from tensorflow.keras.datasets import mnist

(X_train,y_train),(X_test,y_test)=mnist.load_data()
print(X_train.shape,y_train.shape)
model = models.Sequential()
model.add(layers.Dense(512, activation='relu', input_shape=(60000,28, 28)))
model.add(layers.Dense(1, activation='softmax'))

model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])

model.fit(X_train,y_train)
(60000, 28, 28) (60000,)

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-41-23ec20f8146c> in <module>()
     11 model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])
     12 
---> 13 model.fit(X_train,y_train)

9 frames

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, kwargs)
    984           except Exception as e:  # pylint:disable=broad-except
    985             if hasattr(e, "ag_error_metadata"):
--> 986               raise e.ag_error_metadata.to_exception(e)
    987             else:
    988               raise

ValueError: in user code:

    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:855 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:845 step_function  
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica
        return fn(*args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:838 run_step  
        outputs = model.train_step(data)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:797 train_step
        y, y_pred, sample_weight, regularization_losses=self.losses)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:204 call
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:155 call
        losses = call_fn(y_true, y_pred)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:259 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:1644 categorical_crossentropy
        y_true, y_pred, from_logits=from_logits)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py:4862 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (32, 1) and (32, 28) are incompatible

    ما الحل؟

  10. ظهور الخطأ التالي ImportError: cannot import name 'to_categorical' from 'keras.utils' (/usr/local/lib/python3.7/dist-packages/keras/utils/__init__.py) أثناء محاولة استيراد الدالة to_categorical في كيراس

    في بايثون

    نشر

    أحاول استخدام الدالة to_categorical على غوغل كولاب لكن يظهر لي الخطأ التالي عندما أحاول استيرادها: 

    from keras.utils import to_categorical
y_train=to_categorical(y_train)
---------------------------------------------------------------------------

ImportError                               Traceback (most recent call last)

<ipython-input-1-812bfb11e6e7> in <module>()
----> 1 from keras.utils import to_categorical
	  2 y_train=to_categorical(y_train)

ImportError: cannot import name 'to_categorical' from 'keras.utils' (/usr/local/lib/python3.7/dist-packages/keras/utils/__init__.py)


---------------------------------------------------------------------------
NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.

To view examples of installing some common dependencies, click the
"Open Examples" button below.
---------------------------------------------------------------------------

    علماً أنني أقوم باستيرادها بنفس الشكل ضمن ال IDE على جهازي؟ما الحل؟

  13. ظهور خطأ ImportError عند محاولة استيراد مكتبة Keras

    في بايثون

    نشر · تم التعديل في بواسطة Meezo ML

    أريد بناء شبكة عصبية متكررة RNN باستخدام كيراس، لكن عند محاولتي استيراد مكتبة Keras يظهر لي الخطأ التالي؟!  

    >>> import keras
Using TensorFlow backend.
ModuleNotFoundError: No module named 'numpy.core._multiarray_umath'
ImportError: numpy.core.multiarray failed to import

    بالرغم من أنني قمت بتثبيت المكتبة ولدي أحدث إصدار من بايثون 

  15. ظهور الخطأ ImportError: Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work أثناء محاولة رسم نموذج في كيراس

    في بايثون

    نشر · تم التعديل في بواسطة Meezo ML

    قمت بإنشاء نموذج باستخدام إطار العمل Keras، لكن عندما حاولت رسم بنية النموذج أعطاني خطأ، رغم أنني قمت بتثبيت الحزم المشار إليها لكن لم ينجح الأمر.
    الكود: 

    from tensorflow.keras.utils import plot_model
plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)

     الخطأ:  

    ImportError: Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work

  18. ظهور الخطأ "ImportError: cannot import name 'IterativeImputer أثناء محاولة استخدام أداة IterativeImputer في مكتبة Sklearn

    في أسئلة البرمجة

    نشر · تم التعديل في بواسطة Meezo ML

    أحاول استخدام الأداة IterativeImputer من مكتبة Sklearn لكي أعالج القيم المفقودة بكن يظهر لي الخطأ التالي دوماً: 

    ImportError: cannot import name 'IterativeImputer'

    رغم أني جربت أحدث إصدار من Sklearn لكن الأمر لم ينجح! ما الحل؟

  20. ظهور الخطأ التالي ValueError: C <= 0 أثناء استخدام نموذج SVR في مكتبة Sklearn

    في بايثون

    نشر

    أحاول تطبيق أداة GridSearchCV لاختيار أفضل قيم المعاملات لل SVR لكن تظهر لي المشكلة التالية، ما الخطأ: 

    from sklearn.svm import SVR
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
import pandas as pd
BostonData = load_boston()
X = BostonData.data
y = BostonData.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, shuffle =True) 
SelectedModel = SVR()
Selected = {'kernel':('linear', 'rbf'),'C': [0, 0.7, 8]}
GridSearchModel = GridSearchCV(SelectedModel,Selected, cv = 2,return_train_score=True)
GridSearchModel.fit(X_train, y_train)
C:\Users\Windows.10\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
ValueError: C <= 0

  FitFailedWarning)
C:\Users\Windows.10\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
ValueError: C <= 0

  FitFailedWarning)
C:\Users\Windows.10\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
ValueError: C <= 0

  FitFailedWarning)
C:\Users\Windows.10\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
ValueError: C <= 0

  FitFailedWarning)

     

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