Discussion :

[s2a07]

bonjour j'éspere trouver de l'aide voici le code

Code python :

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import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, SimpleRNN, Dense
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
 
# Charger vos données et les étiquettes correspondantes
spam_df = pd.read_csv('C:/Users/User/Documents/spambase/spambase.txt', sep="\t")
 
columns = ["Column_"+str(i+1) for i in range(spam_df.shape[1]-1)] + ['Spam']
spam_df.columns = columns
print(spam_df['Spam'])
spam_df2 = spam_df.drop(columns =['Spam'])
display(spam_df.head())
display(spam_df2.head())
display(spam_df2.head())
# Prétraitement des données (nettoyage, mise en minuscules, etc.)
 
# Diviser les données en ensembles d'entraînement et de test
X_train, X_test, y_train, y_test = train_test_split(spam_df2, spam_df['Spam'], test_size=0.2, random_state=42)
print(train_test_split(spam_df2, spam_df['Spam'], test_size=0.2, random_state=42))
print(y_train)
# Tokenization et padding
tokenizer = Tokenizer()
tokenizer.fit_on_texts(X_train)
X_train_seq = tokenizer.texts_to_sequences(X_train)
X_test_seq = tokenizer.texts_to_sequences(X_test)
vocab_size = len(tokenizer.word_index) + 1
max_sequence_length = max(len(seq) for seq in X_train_seq)
X_train_padded = pad_sequences(X_train_seq, maxlen=max_sequence_length, padding='post')
X_test_padded = pad_sequences(X_test_seq, maxlen=max_sequence_length, padding='post')
print(X_train_padded)
 
# Créer le modèle RNN
model = Sequential()
model.add(Embedding(input_dim=vocab_size, output_dim=64, input_length=max_sequence_length))
model.add(SimpleRNN(units=128, activation='tanh', return_sequences=True))
model.add(SimpleRNN(units=64, activation='tanh'))
model.add(Dense(units=1, activation='sigmoid'))
 
# Compiler le modèle
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
 
# Entraîner le modèle
model.fit(X_train_padded, y_train, epochs=10, batch_size=32, validation_split=0.2)
 
# Évaluer le modèle
y_pred = model.predict(X_test_padded)
y_pred_classes = (y_pred > 0.5).astype(int)
print(classification_report(y_test, y_pred_classes))

et les logs obtenu que je n'arrive pas a comprendre

Code x :

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UnimplementedError                        Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_15308/2652462471.py in <module>
     47 
     48 # Entraîner le modèle
---> 49 model.fit(X_train_padded, y_train, epochs=10, batch_size=32, validation_split=0.2)
     50 
     51 # Évaluer le modèle
 
~\anaconda\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb
 
~\anaconda\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     52   try:
     53     ctx.ensure_initialized()
---> 54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
     55                                         inputs, attrs, num_outputs)
     56   except core._NotOkStatusException as e:
 
UnimplementedError: Graph execution error:
 
Detected at node 'binary_crossentropy/Cast' defined at (most recent call last):
    File "C:\Users\User\anaconda\lib\runpy.py", line 197, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "C:\Users\User\anaconda\lib\runpy.py", line 87, in _run_code
      exec(code, run_globals)
    File "C:\Users\User\anaconda\lib\site-packages\ipykernel_launcher.py", line 16, in <module>
      app.launch_new_instance()
    File "C:\Users\User\anaconda\lib\site-packages\traitlets\config\application.py", line 846, in launch_instance
      app.start()
    File "C:\Users\User\anaconda\lib\site-packages\ipykernel\kernelapp.py", line 677, in start
      self.io_loop.start()
    File "C:\Users\User\anaconda\lib\site-packages\tornado\platform\asyncio.py", line 199, in start
      self.asyncio_loop.run_forever()
    File "C:\Users\User\anaconda\lib\asyncio\base_events.py", line 596, in run_forever
      self._run_once()
    File "C:\Users\User\anaconda\lib\asyncio\base_events.py", line 1890, in _run_once
      handle._run()
    File "C:\Users\User\anaconda\lib\asyncio\events.py", line 80, in _run
      self._context.run(self._callback, *self._args)
    File "C:\Users\User\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 457, in dispatch_queue
      await self.process_one()
    File "C:\Users\User\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 446, in process_one
      await dispatch(*args)
    File "C:\Users\User\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 353, in dispatch_shell
      await result
    File "C:\Users\User\anaconda\lib\site-packages\ipykernel\kernelbase.py", line 648, in execute_request
      reply_content = await reply_content
    File "C:\Users\User\anaconda\lib\site-packages\ipykernel\ipkernel.py", line 353, in do_execute
      res = shell.run_cell(code, store_history=store_history, silent=silent)
    File "C:\Users\User\anaconda\lib\site-packages\ipykernel\zmqshell.py", line 533, in run_cell
      return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
    File "C:\Users\User\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2901, in run_cell
      result = self._run_cell(
    File "C:\Users\User\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 2947, in _run_cell
      return runner(coro)
    File "C:\Users\User\anaconda\lib\site-packages\IPython\core\async_helpers.py", line 68, in _pseudo_sync_runner
      coro.send(None)
    File "C:\Users\User\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3172, in run_cell_async
      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
    File "C:\Users\User\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3364, in run_ast_nodes
      if (await self.run_code(code, result,  async_=asy)):
    File "C:\Users\User\anaconda\lib\site-packages\IPython\core\interactiveshell.py", line 3444, in run_code
      exec(code_obj, self.user_global_ns, self.user_ns)
    File "C:\Users\User\AppData\Local\Temp/ipykernel_15308/2652462471.py", line 49, in <module>
      model.fit(X_train_padded, y_train, epochs=10, batch_size=32, validation_split=0.2)
    File "C:\Users\User\anaconda\lib\site-packages\keras\utils\traceback_utils.py", line 64, in error_handler
      return fn(*args, **kwargs)
    File "C:\Users\User\anaconda\lib\site-packages\keras\engine\training.py", line 1409, in fit
      tmp_logs = self.train_function(iterator)
    File "C:\Users\User\anaconda\lib\site-packages\keras\engine\training.py", line 1051, in train_function
      return step_function(self, iterator)
    File "C:\Users\User\anaconda\lib\site-packages\keras\engine\training.py", line 1040, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "C:\Users\User\anaconda\lib\site-packages\keras\engine\training.py", line 1030, in run_step
      outputs = model.train_step(data)
    File "C:\Users\User\anaconda\lib\site-packages\keras\engine\training.py", line 890, in train_step
      loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "C:\Users\User\anaconda\lib\site-packages\keras\engine\training.py", line 948, in compute_loss
      return self.compiled_loss(
    File "C:\Users\User\anaconda\lib\site-packages\keras\engine\compile_utils.py", line 201, in __call__
      loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "C:\Users\User\anaconda\lib\site-packages\keras\losses.py", line 139, in __call__
      losses = call_fn(y_true, y_pred)
    File "C:\Users\User\anaconda\lib\site-packages\keras\losses.py", line 243, in call
      return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "C:\Users\User\anaconda\lib\site-packages\keras\losses.py", line 1920, in binary_crossentropy
      y_true = tf.cast(y_true, y_pred.dtype)
Node: 'binary_crossentropy/Cast'
Cast string to float is not supported
	 [[{{node binary_crossentropy/Cast}}]] [Op:__inference_train_function_9785]

merci d'avance pour une ombre d'explication

[dourouc05]

Voici l'erreur : Cast string to float is not supported. Un problème dans tes données ? Ou une étape de conversion manquante ?

[s2a07]

bonjour le bug est dans le fit et ytrain qui est j'ai l'impression de type csv mais a quel niveau du code dois je agir es ce que c'est dans la lecture du fichier spambase
merci d'avance pour votre aide