我正在嘗試訓練我的模型并在呼叫該fit()方法時收到此錯誤。關于為什么這無法運行的任何想法？
import pandas as pd
import tensorflow as tf
df = pd.read_csv('labeled_tweets_processed.csv')
labels = df.pop('class')
dataset = tf.data.Dataset.from_tensor_slices((df, labels))
for x, y in dataset.take(1):
print(x, y)
import numpy as np
import tensorflow_datasets as tfds
import tensorflow as tf
tfds.disable_progress_bar()
import matplotlib.pyplot as plt
train_dataset = dataset.take(10000)
test_dataset = dataset.skip(10000)
BUFFER_SIZE = 10000
BATCH_SIZE = 32
train_dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
test_dataset = test_dataset.batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
VOCAB_SIZE = 1000
encoder = tf.keras.layers.TextVectorization(
max_tokens=VOCAB_SIZE)
encoder.adapt(train_dataset.map(lambda text, label: text))
vocab = np.array(encoder.get_vocabulary()).flatten()
vocab[:20]
model = tf.keras.Sequential([
encoder,
tf.keras.layers.Embedding(
input_dim=len(encoder.get_vocabulary()),
output_dim=64,
# Use masking to handle the variable sequence lengths
mask_zero=True),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
])
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['accuracy'])
history = model.fit(train_dataset, validation_data=test_dataset, epochs=10)
在最后一個命令中，我收到以下錯誤：
ValueError: logits and labels must have the same shape ((None, 1) vs ())
任何想法為什么會發生這種情況？
非常感謝您！
uj5u.com熱心網友回復：
也許嘗試這樣的事情：
import pandas as pd
import tensorflow as tf
df = pd.DataFrame(data = {'texts': ['Some text', 'Some text', 'Some text', 'Some text', 'Some text'],
'class': [0, 0, 1, 1, 1]})
labels = df.pop('class')
dataset = tf.data.Dataset.from_tensor_slices((df, labels))
VOCAB_SIZE = 1000
encoder = tf.keras.layers.TextVectorization(
max_tokens=VOCAB_SIZE)
encoder.adapt(dataset.map(lambda text, label: text))
BUFFER_SIZE = 2
BATCH_SIZE = 2
train_dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)
model = tf.keras.Sequential([
encoder,
tf.keras.layers.Embedding(
input_dim=len(encoder.get_vocabulary()),
output_dim=64,
# Use masking to handle the variable sequence lengths
mask_zero=True),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(1)
])
model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['accuracy'])
history = model.fit(train_dataset, epochs=10)
轉載請註明出處，本文鏈接：https://www.uj5u.com/qukuanlian/432301.html