Question

详情如下：

import os
import matplotlib.pyplot
os.environ["TF_CPP_MIN_LOG_LEVEL"]='2'
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist
import tensorflow_hub as hub
import tensorflow_datasets as tfds
import sys
import numpy
(ds_train,ds_test),ds_info=tfds.load(
    "imdb_reviews",
    split=["train","test"],
    shuffle_files=True,
    as_supervised=True,
    with_info=True,
)
tokenizer=tfds.deprecated.text.Tokenizer()
def buildvoca():
    voca=set()
    for text , _ in ds_train:
        voca.update(tokenizer.tokenize(text.numpy().lower()))
    return voca
vocabulary=buildvoca()
encoder=tfds.deprecated.text.TokenTextEncoder(
    vocabulary,oov_token="<UNK>",lowercase=True,tokenizer=tokenizer
)
def myencoding(text_tensor,label):
    return encoder.encode(text_tensor.numpy()),label
def encodemap(text,label):
    encoded_text,label=tf.py_function(myencoding,inp=[text,label],Tout=(tf.int64,tf.int64))
    encoded_text.set_shape([None])
    label.set_shape([])
    return encoded_text,label
AUTOTUNE=tf.data.experimental.AUTOTUNE
ds_train=ds_train.map(encodemap,num_parallel_calls=AUTOTUNE)
ds_train=ds_train.cache()
ds_train=ds_train.shuffle(10000)
ds_train=ds_train.padded_batch(32,padded_shapes=([None],()))
ds_train=ds_train.prefetch(AUTOTUNE)
ds_test=ds_test.map(encodemap)
ds_test=ds_test.padded_batch(32,padded_shapes=([None],()))
model=keras.Sequential(
    [
        layers.Masking(mask_value=0),
        layers.Embedding(input_dim=len(vocabulary)+2,output_dim=32),
        layers.GlobalAveragePooling1D(),
        layers.Dense(64,activation="relu"),
        layers.Dense(1),
    ]
)
model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.Adam(lr=0.001),
    metrics=["accuracy"]
)
model.fit(ds_train,epochs=10,verbose=2)
model.evaluate(ds_test)

我收到以下错误：

tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError:  Received a label value of 1 which is outside the valid range of [0, 1).  Label values: 1 1 0 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 0 1 0 1 1 1 1 1
     [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at \Desktop\modelsaving\model.py:58) ]] [Op:__inference_train_function_50876]

Function call stack:
train_function

Answer 1

您没有指定正确的损失函数。

只需将其更改为二元交叉熵：

model.compile(
    loss=keras.losses.BinaryCrossentropy(from_logits=True), # use BCE
    optimizer=keras.optimizers.Adam(learning_rate=0.001),
    metrics=["accuracy"]
)

我收到以下错误：tensorflow.python.framework.errors_impl.InvalidArgumentError

1 个答案: