该问题在以下问题中得到解决，如果有任何答案，都没有提供明确的解决方案
- CTC损失Tensorflow，未找到有效路径
- ctc_loss 错误“未找到有效路径。”
- CTC 丢失错误：找不到有效路径？Tf.keras 中的 OCR 困难
- Tensorflow ctc_loss_calculator：找不到有效路径
这是我运行的代码...
from itertools import groupby
from pathlib import Path
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow.keras import Model
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import (LSTM, BatchNormalization, Bidirectional,
Conv2D, Dense, Input, Lambda, Layer,
MaxPool2D)
from tensorflow.keras.layers.experimental.preprocessing import StringLookup
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing.sequence import pad_sequences
class CTCLayer(Layer):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.loss_fn = tf.keras.backend.ctc_batch_cost
def call(self, y_true, *args, **kwargs):
y_pred = args[0]
batch_len = tf.cast(tf.shape(y_true)[0], dtype='int64')
input_length = tf.cast(tf.shape(y_pred)[1], dtype='int64')
label_length = tf.cast(tf.shape(y_true)[1], dtype='int64')
input_length = input_length * tf.ones(shape=(batch_len, 1), dtype='int64')
label_length = label_length * tf.ones(shape=(batch_len, 1), dtype='int64')
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
self.add_loss(loss)
return y_pred
class TrainingManager:
def __init__(
self, images, labels, batch_size=256, validation_size=0.1, resize=(32, 128)
):
self.images = images
self.labels = labels
self.batch_size = batch_size
self.validation_size = validation_size
self.resize = resize
self.vocabulary = sorted(set(''.join(self.labels)))
self.max_label_length = len(max(self.labels, key=len))
self.char_to_num = StringLookup(
vocabulary=self.vocabulary, num_oov_indices=0, mask_token=None
)
self.num_to_char = StringLookup(
vocabulary=self.char_to_num.get_vocabulary(), mask_token=None, invert=True
)
def process_example(self, img_path, label):
img = tf.io.read_file(img_path)
img = tf.io.decode_png(img, channels=1)
img = tf.image.convert_image_dtype(img, tf.float32)
img = tf.image.resize(img, self.resize)
return {'image': img, 'label': label}
def preview_dataset(self, dataset, n_rows, n_cols, fig_size=(15, 10)):
fig, ax = plt.subplots(n_rows, n_cols, figsize=fig_size)
for batch in dataset.take(1):
images = batch['image']
labels = batch['label']
for i in range(n_rows * n_cols):
img = (images[i] * 255).numpy().astype('uint8')
label = (
tf.strings.reduce_join(self.num_to_char(labels[i] + 1))
.numpy()
.decode('utf-8')
.replace('[UNK]', '')
)
ax[i // n_rows, i % n_cols].imshow(img[:, :, 0], cmap='gray')
ax[i // n_rows, i % n_cols].set_title(label)
ax[i // n_rows, i % n_cols].axis('off')
def decode_predictions(self, predictions):
text_list = []
prediction_indices = np.argmax(predictions, axis=2)
for i in range(prediction_indices.shape[0]):
text = ''
for p, _ in groupby(prediction_indices[i]):
if p != len(self.vocabulary):
text += self.vocabulary[p]
text_list.append(text)
return text_list
def create_dataset(self, x, y):
dataset = tf.data.Dataset.from_tensor_slices((x, y))
return (
dataset.map(
self.process_example, num_parallel_calls=tf.data.experimental.AUTOTUNE
)
.batch(self.batch_size)
.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
)
def split_data(self):
separation_idx = int(len(self.images) * (self.validation_size - 1))
train_images = self.images[:separation_idx]
valid_images = self.images[separation_idx:]
labels = [
self.char_to_num(tf.strings.unicode_split(label, input_encoding='UTF-8'))
for label in self.labels
]
labels = pad_sequences(labels, self.max_label_length, padding='post')
train_labels = labels[:separation_idx]
valid_labels = labels[separation_idx:]
return train_images, valid_images, train_labels, valid_labels
def create_datasets(self):
train_images, valid_images, train_labels, valid_labels = self.split_data()
train_dataset = self.create_dataset(train_images, train_labels)
valid_dataset = self.create_dataset(valid_images, valid_labels)
return train_dataset, valid_dataset
def create_model(self, training=True):
x0 = Input(shape=(32, 128, 1), name='image')
x = Conv2D(32, (3, 3), activation='selu', padding='same')(x0)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Conv2D(64, (3, 3), activation='selu', padding='same')(x)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Conv2D(128, (3, 3), activation='selu', padding='same')(x)
x = Conv2D(128, (3, 3), activation='selu', padding='same')(x)
x = MaxPool2D(pool_size=(2, 1))(x)
x = Conv2D(256, (3, 3), activation='selu', padding='same')(x)
x = BatchNormalization()(x)
x = Conv2D(256, (3, 3), activation='selu', padding='same')(x)
x = BatchNormalization()(x)
x = MaxPool2D(pool_size=(2, 1))(x)
x = Conv2D(64, (2, 2), activation='selu')(x)
x = Lambda(lambda i: tf.squeeze(i, 1))(x)
x = Bidirectional(LSTM(128, return_sequences=True))(x)
x = Bidirectional(LSTM(128, return_sequences=True))(x)
output = Dense(len(self.vocabulary) + 1, activation='softmax', name='dense')(x)
if not training:
return Model(x0, output)
labels = Input(name='label', shape=(None,), dtype='float32')
output = CTCLayer(name='ctc_loss')(labels, output)
return Model(inputs=[x0, labels], outputs=output)
if __name__ == '__main__':
photos, texts = [], []
for line in open('labels.txt'):
photo_path, photo_text = line.split(',')
photos.append((Path('examples') / photo_path).as_posix())
texts.append(photo_text.strip())
manager = TrainingManager(photos, texts, batch_size=32)
optimizer = Adam()
model = manager.create_model()
model.compile(optimizer=optimizer, metrics=[tf.keras.metrics.Accuracy()])
model.summary()
tds, vds = manager.create_datasets()
manager.preview_dataset(tds, 2, 2)
plt.show()
history = model.fit(
tds,
epochs=100,
validation_data=vds,
verbose=1,
callbacks=[EarlyStopping(verbose=1, patience=3, restore_best_weights=True)],
shuffle=True,
)
examples+ labels.txt（在示例文件夹中）
结果如下：
2021-10-14 05:22:31.610 Python[18731:595755] ApplePersistenceIgnoreState: Existing state will not be touched. New state will be written to /var/folders/hr/61r_7jcx2r3cnklwrr2zwbqw0000gn/T/org.python.python.savedState
2021-10-14 05:22:31.746089: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)
Epoch 1/100
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1/1 [==============================] - ETA: 0s - loss: inf - accuracy: 0.0000e+002021-10-14 05:22:44.004554: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - 9s 9s/step - loss: inf - accuracy: 0.0000e+00 - val_loss: inf - val_accuracy: 0.0000e+00
Epoch 2/100
2021-10-14 05:22:44.622235: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - ETA: 0s - loss: inf - accuracy: 0.0000e+002021-10-14 05:22:45.075456: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - 1s 1s/step - loss: inf - accuracy: 0.0000e+00 - val_loss: inf - val_accuracy: 0.0000e+00
Epoch 3/100
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1/1 [==============================] - ETA: 0s - loss: inf - accuracy: 0.0000e+002021-10-14 05:22:46.127968: W ./tensorflow/core/util/ctc/ctc_loss_calculator.h:499] No valid path found.
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1/1 [==============================] - 1s 1s/step - loss: inf - accuracy: 0.0000e+00 - val_loss: inf - val_accuracy: 0.0000e+00
Restoring model weights from the end of the best epoch.
Epoch 00003: early stopping
注意：该代码适用于长度为 10 个字符或更短的标签。上例中的标签和相应照片各有 1-20 个字符。给定长度为 n 的标签，我究竟需要修改什么才能使其工作？