出现unrecognized data stream contents when reading image file怎么解决，有没有大神解决一下

[qq_54195568]

import os
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import RMSprop
import tensorflow as tf
from kerastuner.tuners import Hyperband
from kerastuner.engine.hyperparameters import HyperParameters


#创建两个生成器，指定scaling范围为0~1
train_datagen = ImageDataGenerator(rescale=1/255)
validation_datagen = ImageDataGenerator(rescale=1/255)

#指向训练数据的文件夹
train_generator = train_datagen.flow_from_directory(
    './tmp/horse-or-human',     #训练数据的文件夹
    target_size=(150,150),      #指定输出尺寸
    batch_size=32,
    class_mode='binary'          #指定二分类
)

#指向测试数据文件夹
validation_generator = validation_datagen.flow_from_directory(
    './tmp/validation-horse-or-human',
    target_size=(150,150),
    batch_size=32,
    class_mode='binary'
)

hp = HyperParameters()
def build_model(hp):
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Conv2D(hp.Choice('num_filters_layer0',values=[16,64],default=16),(3,3),activation='relu',input_shape=(150,150,3)))
    model.add(tf.keras.layers.MaxPooling2D(2,2))
    for i in range(hp.Int('num_conv_layers',1,3)):
        model.add(tf.keras.layers.Conv2D(hp.Choice(f'num_filters_layer{i}',values=[16,64],default=16),(3,3),activation='relu'))
        model.add(tf.keras.layers.MaxPooling2D(2,2))
    # model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))
    # model.add(tf.keras.layers.MaxPooling2D(2,2))

    model.add(tf.keras.layers.Flatten())
    model.add(tf.keras.layers.Dense(hp.Int('hidden_units',128,512,step=32),activation='relu'))
    model.add(tf.keras.layers.Dense(1,activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer=RMSprop(lr=0.001), metrics=['acc'])
    return model
tuner = Hyperband(
    build_model,
    objective = 'val_acc',
    max_epochs = 15,
    directory = 'horse_human_params',
    hyperparameters = hp,
    project_name = 'my_horse_human_project'
)

tuner.search(train_generator,epochs = 10,validation_data = validation_generator)

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