数据基数是不明确的。确保所有数组包含相同数量的样本

2024-04-26 17:40:55 发布

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我正在Kaggle上运行以下代码。当它给出错误“数据基数不明确。确保所有数组包含相同数量的样本”时，它列出了一组两个不同的数字，80和10。80和10的数量是一样的。我不明白它在说什么是错的。任何帮助都将不胜感激。下面是代码和错误

import os
import caer
import canaro
import numpy as np
import cv2 as cv
import gc
import matplotlib.pyplot as plt
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import LearningRateScheduler

IMG_SIZE = (80,80)
channels = 1
char_path = r'../input/the-simpsons-characters-dataset/simpsons_dataset'

char_dict = {}
for char in os.listdir(char_path):
    char_dict[char] = len(os.listdir(os.path.join(char_path,char)))

#sort
char_dict = caer.sort_dict(char_dict, descending=True)

characters = []
count = 0
for i in char_dict:
    characters.append(i[0])
    count += 1
    if count >= 10:
        break

#training data
train = caer.preprocess_from_dir(char_path, characters, channels=channels, IMG_SIZE=IMG_SIZE, isShuffle=True)

plt.figure(figsize=(30,30))
plt.imshow(train[0][0], cmap='gray')
plt.show()

featureSet, labels = caer.sep_train(train, IMG_SIZE=IMG_SIZE)

# normalize feature sets
featureSet = caer.normalize(featureSet)
labels = to_categorical(labels, len(characters))

x_train, x_val, y_train, y_val = caer.train_val_split(featureSet, labels, val_ratio = 0.2)

del train
del featureSet
del labels
gc.collect()

BATCH_SIZE = 32
EPOCHS = 10

# image data generator
x_train = np.array(x_train)
y_train = np.array(y_train)
datagen = canaro.generators.imageDataGenerator()
train_gen = datagen.flow(x_train, y_train, batch_size=BATCH_SIZE)

# creating the model
model = canaro.models.createSimpsonsModel(IMG_SIZE=IMG_SIZE, channels=channels, output_dim=len(characters), loss='binary_crossentropy', decay=1e-6, learning_rate=0.001, momentum=0.9, nesterov=True)

callbacks_list = [LearningRateScheduler(canaro.lr_schedule)]

training = model.fit(train_gen,
                     steps_per_epoch=len(x_train2)//BATCH_SIZE,
                     epochs=EPOCHS,
                     validation_data=(x_val, y_val),
                     validation_steps=len(y_val)//BATCH_SIZE,
                     callbacks=callbacks_list)

错误：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-136-cbb5de211e8a> in <module>
      4                      validation_data=(x_val, y_val),
      5                      validation_steps=len(y_val)//BATCH_SIZE,
----> 6                      callbacks=callbacks_list)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
   1128                 use_multiprocessing=use_multiprocessing,
   1129                 model=self,
-> 1130                 steps_per_execution=self._steps_per_execution)
   1131           val_logs = self.evaluate(
   1132               x=val_x,

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution)
   1110         use_multiprocessing=use_multiprocessing,
   1111         distribution_strategy=ds_context.get_strategy(),
-> 1112         model=model)
   1113 
   1114     strategy = ds_context.get_strategy()

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
    272 
    273     num_samples = set(int(i.shape[0]) for i in nest.flatten(inputs)).pop()
--> 274     _check_data_cardinality(inputs)
    275 
    276     # If batch_size is not passed but steps is, calculate from the input data.

/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/data_adapter.py in _check_data_cardinality(data)
   1527           label, ", ".join(str(i.shape[0]) for i in nest.flatten(single_data)))
   1528     msg += "Make sure all arrays contain the same number of samples."
-> 1529     raise ValueError(msg)
   1530 
   1531 

ValueError: Data cardinality is ambiguous:
  x sizes: 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 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10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10
Make sure all arrays contain the same number of samples.

非常感谢您的帮助


Tags： inimportimgdatasizemodeltensorflowtrain
1条回答
网友
1楼 · 发布于 2024-04-26 17:40:55

谢谢你@Tim Roberts和@hlcodes01。为了社区的利益，在这里提供解决方案

import os
import caer
import canaro
import numpy as np
import cv2 as cv
import gc
import matplotlib.pyplot as plt
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.callbacks import LearningRateScheduler

IMG_SIZE = (80,80)
channels = 1
char_path = r'../input/the-simpsons-characters-dataset/simpsons_dataset'

char_dict = {}
for char in os.listdir(char_path):
    char_dict[char] = len(os.listdir(os.path.join(char_path,char)))

#sort
char_dict = caer.sort_dict(char_dict, descending=True)

characters = []
count = 0
for i in char_dict:
    characters.append(i[0])
    count += 1
    if count >= 10:
        break

#training data
train = caer.preprocess_from_dir(char_path, characters, channels=channels, IMG_SIZE=IMG_SIZE, isShuffle=True)

plt.figure(figsize=(30,30))
plt.imshow(train[0][0], cmap='gray')
plt.show()

featureSet, labels = caer.sep_train(train, IMG_SIZE=IMG_SIZE)

# normalize feature sets
featureSet = caer.normalize(featureSet)
labels = to_categorical(labels, len(characters))

x_train, x_val, y_train, y_val = caer.train_val_split(featureSet, labels, val_ratio = 0.2)

del train
del featureSet
del labels
gc.collect()

BATCH_SIZE = 32
EPOCHS = 10

# image data generator
x_train = np.array(x_train)
y_train = np.array(y_train)
x_val2  = np.array(x_val)
y_val2  = np.array(y_val)


datagen = canaro.generators.imageDataGenerator()
train_gen = datagen.flow(x_train, y_train, batch_size=BATCH_SIZE)

# creating the model
model = canaro.models.createSimpsonsModel(IMG_SIZE=IMG_SIZE, channels=channels, output_dim=len(characters), loss='binary_crossentropy', decay=1e-6, learning_rate=0.001, momentum=0.9, nesterov=True)

callbacks_list = [LearningRateScheduler(canaro.lr_schedule)]

training = model.fit(train_gen,
                     steps_per_epoch=len(x_train)//BATCH_SIZE,
                     epochs=EPOCHS,
                     validation_data=(x_val2, y_val2),
                     validation_steps=len(y_val2)//BATCH_SIZE,
                     callbacks=callbacks_list)

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