python 3.7.3TensorFlow 2.0.0-Alpha0我正在尝试使用TensorFlow中的IMDB分类器，我在https://www.coursera.org/learn/natural-language-processing-tensorflow/lecture/q1ln5/notebook-for-lesson-1中坚持使用代码。
但是我在第一个时期后会出现以下错误。
Train on 25000 samples, validate on 25000 samples
Epoch 1/10
24256/25000 [============================>.] - ETA: 0s - loss: 0.4815 - accuracy: 0.7535Traceback (most recent call last):
File "tf2.py", line 78, in <module>
model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py", line 873, in fit
steps_name='steps_per_epoch')
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 398, in model_iteration
steps_name='validation_steps')
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/engine/training_arrays.py", line 352, in model_iteration
batch_outs = f(ins_batch)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/keras/backend.py", line 3211, in __call__
value = ops.convert_to_tensor(value, dtype=tensor.dtype)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1050, in convert_to_tensor
return convert_to_tensor_v2(value, dtype, preferred_dtype, name)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1108, in convert_to_tensor_v2
as_ref=False)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1186, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py", line 304, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py", line 245, in constant
allow_broadcast=True)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py", line 253, in _constant_impl
t = convert_to_eager_tensor(value, ctx, dtype)
File "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/tensorflow/python/framework/constant_op.py", line 114, in convert_to_eager_tensor
return ops.EagerTensor(value, handle, device, dtype)
TypeError: float() argument must be a string or a number, not 'method'
这是我的代码：
import tensorflow as tf
print(tf.__version__)
tf.compat.v1.enable_eager_execution()
import tensorflow_datasets as tfds
imdb, info = tfds.load("imdb_reviews", with_info = True, as_supervised = True)
import numpy as np
train_data, test_data = imdb['train'], imdb['test']
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy)
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)
vocab_size = 10000
embedding_dim =16
max_length = 120
trunc_type = 'post'
oov_tok = "<OOV>"
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words = vocab_size, oov_token = oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index #key-value dictionary on training_sentences
sequences = tokenizer.texts_to_sequences(training_sentences)
padded = pad_sequences(sequences, maxlen = max_length, truncating = trunc_type)
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen = max_length)
reverse_word_index = dict([(value, key) for (key,value) in word_index.items()])
def decode_review(text):
return ' '.join([reverse_word_index.get(i, '?') for i in text])
print(decode_review(padded[0]))
print(training_sentences[0])
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(6, activation='relu'),
tf.keras.layers.Dense(1,activation = 'sigmoid'),
])
model.compile(loss="binary_crossentropy", optimizer='adam', metrics=['accuracy'])
model.summary
num_epochs = 10
model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))
如何解决错误？
在此处提供解决方案(答案部分(，即使它在评论部分中存在(感谢Giser_yugang(，以造福社区。
从
修改代码时解决问题for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy)
to
for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy())
完成下面的工作代码
import tensorflow as tf
print(tf.__version__)
tf.compat.v1.enable_eager_execution()
import tensorflow_datasets as tfds
imdb, info = tfds.load("imdb_reviews", with_info = True, as_supervised = True)
import numpy as np
train_data, test_data = imdb['train'], imdb['test']
training_sentences = []
training_labels = []
testing_sentences = []
testing_labels = []
for s, l in train_data:
training_sentences.append(str(s.numpy()))
training_labels.append(l.numpy())
for s, l in test_data:
testing_sentences.append(str(s.numpy()))
testing_labels.append(l.numpy())
training_labels_final = np.array(training_labels)
testing_labels_final = np.array(testing_labels)
vocab_size = 10000
embedding_dim =16
max_length = 120
trunc_type = 'post'
oov_tok = "<OOV>"
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
tokenizer = Tokenizer(num_words = vocab_size, oov_token = oov_tok)
tokenizer.fit_on_texts(training_sentences)
word_index = tokenizer.word_index #key-value dictionary on training_sentences
sequences = tokenizer.texts_to_sequences(training_sentences)
padded = pad_sequences(sequences, maxlen = max_length, truncating = trunc_type)
testing_sequences = tokenizer.texts_to_sequences(testing_sentences)
testing_padded = pad_sequences(testing_sequences, maxlen = max_length)
reverse_word_index = dict([(value, key) for (key,value) in word_index.items()])
def decode_review(text):
return ' '.join([reverse_word_index.get(i, '?') for i in text])
print(decode_review(padded[0]))
print(training_sentences[0])
model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(6, activation='relu'),
tf.keras.layers.Dense(1,activation = 'sigmoid'),
])
model.compile(loss="binary_crossentropy", optimizer='adam', metrics=['accuracy'])
model.summary
num_epochs = 10
model.fit(padded, training_labels_final, epochs=num_epochs, validation_data=(testing_padded, testing_labels_final))
输出：
2.2.0
? ? b this was an absolutely terrible movie don't be <OOV> in by christopher walken or michael <OOV> both are great actors but this must simply be their worst role in history even their great acting could not redeem this movie's ridiculous storyline this movie is an early nineties us propaganda piece the most pathetic scenes were those when the <OOV> rebels were making their cases for <OOV> maria <OOV> <OOV> appeared phony and her pseudo love affair with walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning i am disappointed that there are movies like this ruining <OOV> like christopher <OOV> good name i could barely sit through it
b"This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it."
Epoch 1/10
782/782 [==============================] - 5s 6ms/step - loss: 0.4912 - accuracy: 0.7446 - val_loss: 0.3491 - val_accuracy: 0.8471
Epoch 2/10
782/782 [==============================] - 5s 6ms/step - loss: 0.2353 - accuracy: 0.9127 - val_loss: 0.3714 - val_accuracy: 0.8382
Epoch 3/10
782/782 [==============================] - 5s 6ms/step - loss: 0.0896 - accuracy: 0.9772 - val_loss: 0.4480 - val_accuracy: 0.8261
Epoch 4/10
782/782 [==============================] - 5s 6ms/step - loss: 0.0226 - accuracy: 0.9970 - val_loss: 0.5488 - val_accuracy: 0.8219
Epoch 5/10
782/782 [==============================] - 5s 6ms/step - loss: 0.0057 - accuracy: 0.9996 - val_loss: 0.5993 - val_accuracy: 0.8240
Epoch 6/10
782/782 [==============================] - 5s 6ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.6491 - val_accuracy: 0.8255
Epoch 7/10
782/782 [==============================] - 5s 7ms/step - loss: 8.2380e-04 - accuracy: 1.0000 - val_loss: 0.6869 - val_accuracy: 0.8262
Epoch 8/10
782/782 [==============================] - 5s 6ms/step - loss: 4.7165e-04 - accuracy: 1.0000 - val_loss: 0.7288 - val_accuracy: 0.8264
Epoch 9/10
782/782 [==============================] - 5s 6ms/step - loss: 2.6724e-04 - accuracy: 1.0000 - val_loss: 0.7653 - val_accuracy: 0.8261
Epoch 10/10
782/782 [==============================] - 5s 6ms/step - loss: 1.5851e-04 - accuracy: 1.0000 - val_loss: 0.8009 - val_accuracy: 0.8263