Am probleme cu aplicarea Date Sanilor, atunci când de formare cu model. În special cu privire la utilizarea de fit_generator() metodă.
Am rula inițial modelul meu de succes, fără a sanilor folosind fit() metodă, cu toate acestea, potrivit altora , este recomandat să utilizați fit_generator(). Se pare ca ambele metode au nevoie de aceleași date de intrare, atunci când vine vorba de imagini și etichete, dar primesc următoarea EROARE când executați codul de mai jos:
---------------------------------------------------------------------------
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'
Am terminat google în încercarea de a stabili TypeError: float() argument trebuie să fie un șir de caractere sau un număr, nu 'BatchDataset' eroare, dar fără nici un rezultat. Are cineva sugestii cum să avanseze?
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)
Vă mulțumesc pentru uita la postul meu! :)