TypeError: float() argument doit être une chaîne de caractères ou un nombre, et non pas "BatchDataset' lorsque les données augmentant l'aide de fit_generator()

Je suis ont des problèmes avec l'application de Données d'Augmentation lors de la formation du modèle. Spécifiquement sur l'utilisation de la fit_generator() méthode.

J'ai à l'origine de mon modèle avec succès, sans augmentation de l'aide de l' ajustement() la méthode, cependant, selon d'autres, il est recommandé d'utiliser fit_generator(). Il semble que les deux méthodes ont besoin de la même entrée quand il s'agit des images et des étiquettes, mais je suis arriver à la suite d'ERREUR lors de l'exécution du code ci-dessous:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/tmp/ipykernel_35/139227558.py in <module>
    105 
    106 # train the network
--> 107 model.fit_generator(aug.flow(train_ds,  batch_size=batch_size),
    108         validation_data=val_ds, steps_per_epoch=len(train_ds[0]) // batch_size,
    109     epochs=epochs)

/opt/conda/lib/python3.7/site-packages/keras/preprocessing/image.py in flow(self, x, y, batch_size, shuffle, sample_weight, seed, save_to_dir, save_prefix, save_format, subset)
    894         save_prefix=save_prefix,
    895         save_format=save_format,
--> 896         subset=subset)
    897 
    898   def flow_from_directory(self,

/opt/conda/lib/python3.7/site-packages/keras/preprocessing/image.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)
    472         save_format=save_format,
    473         subset=subset,
--> 474         **kwargs)
    475 
    476 

/opt/conda/lib/python3.7/site-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)
    119                     y = y[split_idx:]
    120 
--> 121         self.x = np.asarray(x, dtype=self.dtype)
    122         self.x_misc = x_misc
    123         if self.x.ndim != 4:

/opt/conda/lib/python3.7/site-packages/numpy/core/_asarray.py in asarray(a, dtype, order)
     81 
     82     """
---> 83     return array(a, dtype, copy=False, order=order)
     84 
     85 

TypeError: float() argument must be a string or a number, not 'BatchDataset'

J'ai terminé google en essayant de réparer l' erreur TypeError: float() argument doit être une chaîne de caractères ou un nombre, et non pas "BatchDataset' erreur, mais en vain. Quelqu'un aurait-il des suggestions pour aller de l'avant?

import pathlib
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt

# Set data directory
data_dir = pathlib.Path("../input/validatedweaponsv6/images/")

# Set image size
img_height = 120
img_width = 120

# Hyperparameters
batch_size = 128
epochs = 50
learning_rate = 0.001

# Create the training dataset
train_ds = tf.keras.utils.image_dataset_from_directory(
    data_dir,
    label_mode='categorical',
    validation_split=0.2,
    subset="training",
    shuffle=True,
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

# Create the validation dataset
val_ds = tf.keras.utils.image_dataset_from_directory(
    data_dir,
    label_mode='categorical',
    validation_split=0.2,
    subset="validation",
    shuffle=True,
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

# Create sequential model
model = Sequential([

    # Preprocessing
    layers.Rescaling(1./127.5, offset=-1,
                     input_shape=(img_height, img_width, 3)),

    # Encoder
    layers.Conv2D(8, 3, activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(16, 3, activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(32, 3, activation='relu'),
    # layers.Conv2D(2, 3, activation='relu'), ???
    layers.Flatten(),

    # Decoder
    layers.Dense(64, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(2, activation='softmax')
])

# Print the model to see the different output shapes
print(model.summary())

# Compile model
model.compile(loss='categorical_crossentropy',
              optimizer=keras.optimizers.SGD(learning_rate=learning_rate), metrics=['accuracy'])

# construct the training image generator for data augmentation
aug = tf.keras.preprocessing.image.ImageDataGenerator(rotation_range=20, zoom_range=0.15,
    width_shift_range=0.2, height_shift_range=0.2, shear_range=0.15,
    horizontal_flip=True, fill_mode="nearest")

# train the network
model.fit_generator(aug.flow(train_ds,  batch_size=batch_size),
validation_data=val_ds, steps_per_epoch=len(train_ds[0]) // batch_size,
epochs=epochs)

# Print scores
score = model.evaluate(train_ds, verbose=0)
print('Validation loss:', score[0])
print('Validation accuracy:', score[1])

# Show loss and accuracy models
show_history(history)

Merci de regarder mon post! :)