在Tensorflow神经网络中不正确的形状-形状中的批大小

Question

我正在遵循在Keras上实现EfficientNet的教程:

Tensorflow

我正在使用我自己的数据集，因此必须手动加载它。由于某些原因，批大小被包含在张量形状中，它抛出了一个错误。

from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense, Dropout, GlobalMaxPooling2D, Input
from tensorflow.keras import optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.applications import EfficientNetB0
import tensorflow as tf
import os, os.path
import glob
import shutil
import sys
import matplotlib.pyplot as plt
sys.path.insert(1, './efficientnet_keras_transfer_learning')

# Options: EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3 to B7
# Higher the number, the more complex the model is.
# EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3
# loading pretrained conv base model
# define input height and width
### This changes model weights name ###
model_type = 'B0'
if model_type == 'B0':
height, width = 224, 224
elif model_type == 'B1':
height, width = 240, 240
elif model_type == 'B2':
height, width = 260, 260
elif model_type == 'B3':
height, width = 300, 300
elif model_type == 'B4':
height, width = 380, 380
elif model_type == 'B5':
height, width = 456, 456
elif model_type == 'B6':
height, width = 528, 528
elif model_type == 'B7':
height, width = 600, 600
# input_shape = (height, width, 3)
# ### Change Model Type ###
# conv_base = EfficientNetB0(
#     include_top=True,
#     weights=None,
#     classes=3
#     # input_shape=input_shape,
# )
filepath = (os.path.expanduser(
'~') + 'dataset')
# Prepare Data
train_dir = filepath + 'Train'
test_dir = filepath + 'Test'
val_dir = filepath + 'Val'
batch_size = 32
train_ds = tf.keras.utils.image_dataset_from_directory(
train_dir,
labels='inferred',
seed=42,
image_size=(height, width),
batch_size=batch_size
)
val_ds = tf.keras.utils.image_dataset_from_directory(
val_dir,
labels='inferred',
seed=42,
image_size=(height, width),
batch_size=batch_size
)
class_names = train_ds.class_names
num_classes = len(class_names)
print('There are ' + str(num_classes) + ' classes:n' + str(class_names))

train_ds = train_ds.map(lambda image, label: (tf.image.resize(image, (height, width)), label))
val_ds = val_ds.map(lambda image, label: (tf.image.resize(image, (height, width)), label))
# plt.figure(figsize=(10, 10))
# for images, labels in train_ds.take(1):
#   for i in range(9):
#     ax = plt.subplot(3, 3, i + 1)
#     plt.imshow(images[i].numpy().astype("uint8"))
#     plt.title(class_names[labels[i]])
#     plt.axis("off")

img_augmentation = Sequential(
# [
# layers.RandomRotation(factor=0.15),
# layers.RandomTranslation(height_factor=0.1, width_factor=0.1),
# layers.RandomFlip(),
# layers.RandomContrast(factor=0.1),
# ],
name="img_augmentation",
)

# One-hot / categorical encoding
def input_preprocess(image, label):
label = tf.one_hot(label, num_classes)
return image, label
train_ds = train_ds.map(
input_preprocess, num_parallel_calls=tf.data.AUTOTUNE)
train_ds = train_ds.batch(batch_size=batch_size, drop_remainder=True)
train_ds = train_ds.prefetch(tf.data.AUTOTUNE)
val_ds = val_ds.map(input_preprocess)
val_ds = val_ds.batch(batch_size=batch_size, drop_remainder=True)
inputs = Input(shape=(height, width, 3))
x = img_augmentation(inputs)
outputs = EfficientNetB0(include_top=True, weights=None, classes=num_classes)(x)

model = tf.keras.Model(inputs, outputs)
model.compile(
optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]
)
model.summary()
epochs = 50
hist = model.fit(train_ds, epochs=epochs, validation_data=val_ds, verbose=2)

我一直得到一个错误:

ValueError: Input 0 is incompatible with layer model_4: expected shape=(None, 224, 224, 3), found shape=(32, None, 224, 224, 3)

前面的32显然是批量大小。但是我不知道为什么这个形状是这样的，也不知道怎么让它匹配。

Answer 1

正如@ frtera, tf.keras.utils所评论的。image_dataset_from_directory返回4Dimension的图像形状

样例代码

import pathlib
data_dir = pathlib.Path('/content/images/images')
import tensorflow as tf
batch_size = 16
img_height = 180
img_width = 180
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)

(7, 180, 180, 3)
(7,)