[人工智能]from tensorflow.keras.preprocessing import image_dataset_from_directory

方法简单 但是日志怎么不能读

from tensorflow.keras.preprocessing import image_dataset_from_directory
from tensorflow.keras import models,layers,metrics,optimizers,losses,callbacks

# open("cats_and_dogs_filtered/validation")
train_data=image_dataset_from_directory("cats_and_dogs_filtered/train",label_mode='categorical',image_size=(32,32),shuffle=True,seed=123,batch_size=128)
test_data=image_dataset_from_directory("cats_and_dogs_filtered/validation",label_mode='categorical',image_size=(32,32),shuffle=True,seed=123,batch_size=128)

def my_model():
    model=models.Sequential([
        layers.InputLayer((32,32,3)),
        layers.Conv2D(32,(3,3),padding="same",activation="relu"),
        layers.MaxPooling2D((2,2),2),
        layers.Conv2D(64, (3, 3), padding="same", activation="relu"),
        layers.MaxPooling2D((2, 2), 2),
        layers.Conv2D(128, (3, 3), padding="same", activation="relu"),
        layers.MaxPooling2D((2, 2), 2),
        layers.BatchNormalization(),
        layers.Flatten(),
        layers.Dense(128,activation="relu"),
        layers.Dense(2,activation="softmax")
    ])
    model.summary()
    model.compile(optimizer=optimizers.Adam(),
                  loss=losses.categorical_crossentropy,
                  metrics=['accuracy'])
    return model

if __name__ == '__main__':
    model=my_model()
    callbacks = [
        callbacks.TensorBoard(log_dir="log")
    ]

    model.fit_generator(train_data,epochs=10,validation_data=test_data)

    # model.save("model.h5")
    # tensorboard --logdir  ../m_tensorflow/log