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Я следую вместе с Лоуренсом Морони в tensorflow, кодируя по ходу. Вот видео:https://www.youtube.com/watch?v=Y_hzMnRXjhI

Я получаю это сообщение об ошибке:

 Traceback (most recent call last):
  File "tensorTest.py", line 66, in <module>
    validation_data=(testing_padded, testing_labels), verbose=2)
  File "/Users/elliot/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 108, in _method_wrapper
    return method(self, *args, **kwargs)
  File "/Users/elliot/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 1063, in fit
    steps_per_execution=self._steps_per_execution)
  File "/Users/elliot/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 1117, in __init__
    model=model)
  File "/Users/elliot/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py", line 282, in __init__
    raise ValueError(msg)
ValueError: Data cardinality is ambiguous:
  x sizes: 28618
  y sizes: 14309
Please provide data which shares the same first dimension.
  

Я могу получить сводку модели просто отлично, но когда она пытается соответствовать, я получаю ошибку. Полный код ниже:

 import tensorflow as tf from tensorflow import keras from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences import pandas as pd

data = pd.read_json("Sarcasm_Headlines_Dataset_v2.json", lines=True)

sentences = data['headline'].to_list() labels = data['is_sarcastic'].to_list() urls = data['article_link'].to_list()

vocab_size = 10000 embedding_dim = 16 max_length = 100 trunc_type='post' padding_type='post' oov_tok = "<OOV>" training_size
= 14309

training_sentences = sentences[0:training_size] testing_sentences = sentences[:training_size] training_labels = labels[0:training_size] testing_labels = labels[training_size:]



tokenizer = Tokenizer(oov_token = oov_tok) tokenizer.fit_on_texts(training_sentences)

word_index = tokenizer.word_index

training_sequences = tokenizer.texts_to_sequences(sentences) training_padded = pad_sequences(training_sequences, padding=padding_type, maxlen=max_length, truncating=trunc_type)

testing_sequences = tokenizer.texts_to_sequences(testing_sentences) testing_padded = pad_sequences(testing_sequences, padding=padding_type, maxlen=max_length, truncating=trunc_type)

import numpy as np training_padded = np.array(training_padded) training_labels = np.array(training_labels) testing_padded = np.array(testing_padded) testing_labels = np.array(testing_labels)

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(24, activation='relu'),
    tf.keras.layers.Dense(1, activation='sigmoid') ])

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.summary()


num_epochs = 30

history = model.fit(training_padded, training_labels, epochs=num_epochs, validation_data=(testing_padded, testing_labels), verbose=2)

print(history)
  

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