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[英]Input 0 of layer max_pooling2d is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: [None, 4, 10, 8, 32]
[英]Keras Dimension error - (Input 0 of layer "max_pooling2d" is incompatible with the layer: expected ndim=4, found ndim=6.)
我想在 github 上工作这个项目: https://github.com/kesaroid/Glaucoma-Detection
这是一个用于创建 keras h5 文件的 CNN.py 文件:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
from os import path, environ
from imgaug import augmenters as iaa
from keras import backend as K
from keras import optimizers
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from keras.layers import BatchNormalization, Activation
from keras.layers import Input, Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
img_width, img_height = 256, 256
channels = 3
input_shape = channels, img_width, img_height if K.image_data_format() == 'channels_first' \
else img_width, img_height, channels
train_data_dir = path.join('data', 'train')
validation_data_dir = path.join('data', 'validation')
nb_train_samples = int(environ.get('TRAINING_SAMPLES', 20))
nb_validation_samples = int(environ.get('VALIDATION_SAMPLES', 20))
batch_size = 16
epochs = 100
input_tensor = Input(shape=input_shape)
block1 = BatchNormalization(name='norm_0')(input_tensor)
# Block 1
block1 = Conv2D(8, (3, 3), name='conv_11', activation='relu')(block1)
block1 = Conv2D(16, (3, 3), name='conv_12', activation='relu')(block1)
block1 = Conv2D(32, (3, 3), name='conv_13', activation='relu')(block1)
block1 = Conv2D(64, (3, 3), name='conv_14', activation='relu')(block1)
block1 = MaxPooling2D(pool_size=(2, 2))(block1)
block1 = BatchNormalization(name='norm_1')(block1)
block1 = Conv2D(16, 1)(block1)
# Block 2
block2 = Conv2D(32, (3, 3), name='conv_21', activation='relu')(block1)
block2 = Conv2D(64, (3, 3), name='conv_22', activation='relu')(block2)
block2 = Conv2D(64, (3, 3), name='conv_23', activation='relu')(block2)
block2 = Conv2D(128, (3, 3), name='conv_24', activation='relu')(block2)
block2 = MaxPooling2D(pool_size=(2, 2))(block2)
block2 = BatchNormalization(name='norm_2')(block2)
block2 = Conv2D(64, 1)(block2)
# Block 3
block3 = Conv2D(64, (3, 3), name='conv_31', activation='relu')(block2)
block3 = Conv2D(128, (3, 3), name='conv_32', activation='relu')(block3)
block3 = Conv2D(128, (3, 3), name='conv_33', activation='relu')(block3)
block3 = Conv2D(64, (3, 3), name='conv_34', activation='relu')(block3)
block3 = MaxPooling2D(pool_size=(2, 2))(block3)
block3 = BatchNormalization(name='norm_3')(block3)
# Block 4
block4 = Conv2D(64, (3, 3), name='conv_41', activation='relu')(block3)
block4 = Conv2D(32, (3, 3), name='conv_42', activation='relu')(block4)
block4 = Conv2D(16, (3, 3), name='conv_43', activation='relu')(block4)
block4 = Conv2D(8, (2, 2), name='conv_44', activation='relu')(block4)
block4 = MaxPooling2D(pool_size=(2, 2))(block4)
block4 = BatchNormalization(name='norm_4')(block4)
block4 = Conv2D(2, 1)(block4)
block5 = GlobalAveragePooling2D()(block4)
output = Activation('softmax')(block5)
model = Model(inputs=[input_tensor], outputs=[output])
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False),
metrics=['accuracy'])
# Initiate the train and test generators with data Augmentation
sometimes = lambda aug: iaa.Sometimes(0.6, aug)
seq = iaa.Sequential([
iaa.GaussianBlur(sigma=(0, 1.0)),
iaa.Sharpen(alpha=1, lightness=0),
iaa.CoarseDropout(p=0.1, size_percent=0.15),
sometimes(iaa.Affine(
scale={'x': (0.8, 1.2), 'y': (0.8, 1.2)},
translate_percent={'x': (-0.2, 0.2), 'y': (-0.2, 0.2)},
rotate=(-30, 30),
shear=(-16, 16)))
])
train_datagen = ImageDataGenerator(
rescale=1. / 255,
preprocessing_function=seq.augment_image,
horizontal_flip=True,
vertical_flip=True)
test_datagen = ImageDataGenerator(
rescale=1. / 255,
horizontal_flip=True,
vertical_flip=True)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_height, img_width),
class_mode='categorical')
checkpoint = ModelCheckpoint('f1.h5', monitor='acc', verbose=1, save_best_only=True, save_weights_only=False,
mode='auto', period=1)
reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.1, patience=2, verbose=0, mode='auto', cooldown=0, min_lr=0)
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size,
callbacks=[checkpoint, reduce_lr]
)
但我收到错误消息:
C:\anaconda3\envs\tf2.7\python.exe
C:/Users/yigit/Documents/GitHub/Glaucoma-Detection/CNN.py
回溯(最近一次通话最后):
文件“C:\Users\yigit\Documents\GitHub\Glaucoma-Detection\CNN.py”,第 38 行,在 block1 = MaxPooling2D(pool_size=(2, 2))(block1)
error_handler 中的文件“C:\anaconda3\envs\tf2.7\lib\site-packages\keras\utils\traceback_utils.py”,第 67 行,从 None 提升 e.with_traceback(filtered_tb)
文件“C:\anaconda3\envs\tf2.7\lib\site-packages\keras\engine\input_spec.py”,第 213 行,在 assert_input_compatibility
raise ValueError(f'Input {input_index} of layer "{layer_name}" '
ValueError:层“max_pooling2d”的输入 0 与层不兼容:预期 ndim=4,发现 ndim=6。 收到的完整形状:(无、3、256、248、248、64)
进程以退出代码 1 结束
我该怎么办?
tf.keras.layers.MaxPool2D
期望输入形状为形状为(batch_size, rows, cols, channels)
的 4D 张量。 在这种情况下,您正在添加额外的维度。 确保您的输入是 4D。
工作示例代码
import tensorflow as tf
input_shape = (4, 28, 28, 3)
x = tf.random.normal(input_shape)
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.InputLayer(input_shape=input_shape[1:]))
model.add(tf.keras.layers.Conv2D(filters=32,
kernel_size=2,
activation='relu'))
model.add(tf.keras.layers.MaxPool2D(pool_size=2))
output = model(x)
Output
TensorShape([4, 13, 13, 32])
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