我看到了这些问题：Tensorflow Keras错误：未知的图像文件格式。需要其中之一：JPEG，PNG，GIF，BMP 和 未知的图像文件格式。 需要其中之一：JPEG，PNG，GIF，BMP，但它们并没有完全帮助我。
我正在Google Colab中构建一个简单的CNN。
我是一个有用的助手，可以为您进行文本翻译。下面是需要翻译的内容：
我正在Google Colab中构建一个简单的CNN。
Epoch 1/5
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
<ipython-input-29-a98bc2c91ee1> in <module>
----> 1 history = model_1.fit(train_data, epochs=5, steps_per_epoch=len(train_data), validation_data=test_data, validation_steps=int(0.25 * len(test_data)))
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
53 ctx.ensure_initialized()
54 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55 inputs, attrs, num_outputs)
56 except core._NotOkStatusException as e:
57 if name is not None:
InvalidArgumentError: Graph execution error:
2 root error(s) found.
(0) INVALID_ARGUMENT: Unknown image file format. One of JPEG, PNG, GIF, BMP required.
[[{{node decode_image/DecodeImage}}]]
[[IteratorGetNext]]
[[categorical_crossentropy/softmax_cross_entropy_with_logits/Shape_2/_10]]
(1) INVALID_ARGUMENT: Unknown image file format. One of JPEG, PNG, GIF, BMP required.
[[{{node decode_image/DecodeImage}}]]
[[IteratorGetNext]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_31356]
我是一个有用的助手，可以为您进行文本翻译。下面是需要翻译的内容：
我遇到了上述错误。这个错误出现在我尝试拟合模型时。 通过我链接的以前的答案，我已经确认我的文件夹中没有不适当的图像。所有的图像都是jpeg格式。
我的代码:
import tensorflow as tf
# Create training and test directory paths
train_dir = 'Dataset/train'
test_dir = 'Dataset/test'
IMG_SIZE = (224,224)
BATCH_SIZE=32
# Set up data loaders
import tensorflow as tf
IMG_SIZE = (224,224)
BATCH_SIZE=32
train_data = tf.keras.preprocessing.image_dataset_from_directory(directory=train_dir,
image_size=IMG_SIZE,
label_mode='categorical',
batch_size=BATCH_SIZE)
test_data = tf.keras.preprocessing.image_dataset_from_directory(directory=test_dir, image_size=IMG_SIZE, batch_size=BATCH_SIZE, label_mode='categorical')
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
data_augmentation = keras.Sequential([
preprocessing.RandomFlip('horizontal'),
preprocessing.RandomRotation(0.2),
preprocessing.RandomZoom(0.2),
preprocessing.RandomHeight(0.2),
preprocessing.RandomWidth(0.2),
# preprocessing.Rescale(1/255.) Keep this model for ResNet. Efficient Net has rescaling buit in
], name='data_augmentation')
input_shape = (224,224,3)
base_model = tf.keras.applications.EfficientNetB0(include_top=False)
base_model.trainable=False
# Create the input layer
inputs = layers.Input(shape=input_shape, name='input_layer')
x=data_augmentation(inputs)
# Give base model the inputs after augmentation.. Dont train it
x = base_model(x,training=False)
x = layers.GlobalAveragePooling2D()(x)
# Add a dense layer for output
outputs = layers.Dense(9, activation='softmax', name='output_layer')(x)
# Make a model using the inputs and outputs
model_1 = keras.Model(inputs,outputs)
# Compile the model
model_1.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
history = model_1.fit(train_data, epochs=5, steps_per_epoch=len(train_data), validation_data=test_data, validation_steps=int(0.25 * len(test_data)))
我只从Google搜索下载了所有图像。
数据集链接： https://drive.google.com/file/d/1dKgzyq2lUF87ggZQ80KUhINhmtVrC_p-/view?usp=sharing