我正在 Coursera 上學習 CNN 課程，當我嘗試解決作業筆記本時，這是向我拋出的錯誤。
ValueError: `x` (images tensor) and `y` (labels) should have the same length. Found: x.shape = (27455, 28, 28, 1), y.shape = (7172, 28, 28, 1)
但是它們的長度不一樣嗎？（即維度。）以下是我創建問題的代碼塊：
# GRADED FUNCTION: train_val_generators
def train_val_generators(training_images, training_labels, validation_images, validation_labels):
"""
Creates the training and validation data generators
Args:
training_images (array): parsed images from the train CSV file
training_labels (array): parsed labels from the train CSV file
validation_images (array): parsed images from the test CSV file
validation_labels (array): parsed labels from the test CSV file
Returns:
train_generator, validation_generator - tuple containing the generators
"""
### START CODE HERE
# In this section, you will have to add another dimension to the data
# So, for example, if your array is (10000, 28, 28)
# You will need to make it (10000, 28, 28, 1)
# Hint: np.expand_dims
training_images = np.expand_dims(training_images, axis=3)
validation_images = np.expand_dims(validation_images, axis=3)
print(training_images.shape)
print(validation_images.shape)
# Instantiate the ImageDataGenerator class
# Don't forget to normalize pixel values
# and set arguments to augment the images (if desired)
train_datagen = ImageDataGenerator(
# Your Code Here
rescale = 1./255,
rotation_range = 40,
width_shift_range = 0.2,
height_shift_range = 0.2,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True,
fill_mode = 'nearest'
)
# Pass in the appropriate arguments to the flow method
train_generator = train_datagen.flow(x=training_images,
y=validation_images,
batch_size=32)
# Instantiate the ImageDataGenerator class (don't forget to set the rescale argument)
# Remember that validation data should not be augmented
validation_datagen = ImageDataGenerator(
rescale = 1./255
)
# Pass in the appropriate arguments to the flow method
validation_generator = validation_datagen.flow(x=training_images,
y=validation_images,
batch_size=32)
### END CODE HERE
return train_generator, validation_generator
運行此單元后，它作業正常并為我的影像添加了額外的維度。以下代碼單元引發了上述問題。
# Test your generators
train_generator, validation_generator = train_val_generators(training_images, training_labels, validation_images, validation_labels)
print(f"Images of training generator have shape: {train_generator.x.shape}")
print(f"Labels of training generator have shape: {train_generator.y.shape}")
print(f"Images of validation generator have shape: {validation_generator.x.shape}")
print(f"Labels of validation generator have shape: {validation_generator.y.shape}")
這是我的整個錯誤訊息：
(27455, 28, 28, 1)
(7172, 28, 28, 1)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-27-c93bf4854fbc> in <module>()
1 # Test your generators
----> 2 train_generator, validation_generator = train_val_generators(training_images, training_labels, validation_images, validation_labels)
3
4 print(f"Images of training generator have shape: {train_generator.x.shape}")
5 print(f"Labels of training generator have shape: {train_generator.y.shape}")
3 frames
/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/numpy_array_iterator.py in __init__(self, x, y, image_data_generator, batch_size, shuffle, sample_weight, seed, data_format, save_to_dir, save_prefix, save_format, subset, dtype)
87 'should have the same length. '
88 'Found: x.shape = %s, y.shape = %s' %
---> 89 (np.asarray(x).shape, np.asarray(y).shape))
90 if sample_weight is not None and len(x) != len(sample_weight):
91 raise ValueError('`x` (images tensor) and `sample_weight` '
ValueError: `x` (images tensor) and `y` (labels) should have the same length. Found: x.shape = (27455, 28, 28, 1), y.shape = (7172, 28, 28, 1)
我嘗試在 StackOverflow 中搜索許多類似的問題，其中談到了更改其尺寸，但我認為我的尺寸是正確的，因為更改它絕對沒有任何作用。對此有何見解？請幫忙。謝謝！:_)
uj5u.com熱心網友回復：
在train_datagen.flowandvalidation_datagen.flow中，你犯了兩個小錯誤。對于y引數，您傳遞validation_images，但您需要傳遞training_labelsand validation_labels。
我糾正了上述錯誤，并用隨機影像和一個簡單的 CNN 模型撰寫了完整的代碼并對其進行了擬合。
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import tensorflow as tf
import numpy as np
def train_val_generators(training_images, training_labels, validation_images, validation_labels):
training_images = np.expand_dims(training_images, axis=3)
validation_images = np.expand_dims(validation_images, axis=3)
print(training_images.shape)
print(validation_images.shape)
train_datagen = ImageDataGenerator(
rescale = 1./255,
rotation_range = 40,
width_shift_range = 0.2,
height_shift_range = 0.2,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True,
fill_mode = 'nearest'
)
train_generator = train_datagen.flow(x=training_images,
y=training_labels,
batch_size=32)
validation_datagen = ImageDataGenerator(
rescale = 1./255
)
validation_generator = validation_datagen.flow(x=validation_images,
y=validation_labels,
batch_size=32)
return train_generator, validation_generator
train_generator, validation_generator = train_val_generators(
training_images = np.random.rand(27455, 28,28),
training_labels = np.random.randint(0,2,27455),
validation_images = np.random.rand(7172, 28,28),
validation_labels = np.random.randint(0,2,7172),
)
model = tf.keras.Sequential([
tf.keras.layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(32, 3, padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dropout(0.4),
tf.keras.layers.Dense(2)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))
model.fit(train_generator,
epochs=2,
validation_data=validation_generator)
輸出：
(27455, 28, 28, 1)
(7172, 28, 28, 1)
Epoch 1/2
858/858 [==============================] - 25s 25ms/step - loss: 0.6933 - val_loss: 0.6931
Epoch 2/2
858/858 [==============================] - 18s 21ms/step - loss: 0.6932 - val_loss: 0.6930
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