Mám problémy s použitím Dat Prsou při školení modelu. Konkrétně o použití fit_generator() metoda.
Já jsem původně běžet můj model úspěšně bez prsou pomocí fit() metoda, nicméně podle jiné je doporučeno používat fit_generator(). Zdá se, jako by obě metody potřebují stejné vstupní když přijde na obrázky a popisky, ale já jsem dostat následující CHYBU při spuštění následující kód:
---------------------------------------------------------------------------
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'
Dokončil jsem google se snaží opravit TypeError: float() argument musí být řetězec nebo číslo, ne BatchDataset' chyba, ale bezvýsledně. Máte někdo návrhy, jak se pohnout kupředu?
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)
Děkujeme vám za pohledu na můj příspěvek! :)