Erste Fehler beim Faltungsautoencoder in keras

Question

läuft ich Störung erhalte, während Sie den folgenden Code keras

Traceback (most recent call last): 
    File "my_conv_ae.py", line 74, in <module> 
validation_steps = nb_validation_samples // batch_size) 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\legacy\interfaces.py", line 88, in wrapper 
return func(*args, **kwargs) 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 1890, in fit_generator 
class_weight=class_weight) 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 1627, in train_on_batch 
check_batch_axis=True) 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 1309, in _standardize_user_data 
exception_prefix='target') 
    File "C:\Users\Administrator\AppData\Local\Programs\Python\Python35\lib\site-packages\keras\engine\training.py", line 127, in _standardize_input_data 
str(array.shape)) 
ValueError: Error when checking target: expected conv2d_transpose_8 to have 4 dimensions, but got array with shape (20, 1) 

Der Code in Laufen ist:

import keras 
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Conv2DTranspose 
from keras.models import Model 
from keras import backend as K 
from keras.preprocessing.image import ImageDataGenerator 
import numpy as np 

input_img = Input(shape=(512, 512, 1)) 
nb_train_samples = 1700 
nb_validation_samples = 420 
epochs = 10 
batch_size = 20 

x = Conv2D(64, (11, 11), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(input_img) 
x = Conv2D(64, (11, 11), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = MaxPooling2D((2, 2))(x) 
x = Conv2D(128, (7, 7), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2D(128, (5, 5), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = MaxPooling2D((2, 2))(x) 
x = Conv2D(256, (5, 5), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2D(256, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = MaxPooling2D((2, 2))(x) 
x = Conv2D(512, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2D(512, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
encoded = MaxPooling2D((2, 2))(x) 

print (K.int_shape(encoded)) 

an dieser Stelle ist die Darstellung (26, 26 , 512)

x = UpSampling2D((2, 2))(encoded) 
x = Conv2DTranspose(512, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2DTranspose(512, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = UpSampling2D((2, 2))(x) 
x = Conv2DTranspose(256, (3, 3), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2DTranspose(256, (5, 5), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = UpSampling2D((2, 2))(x) 
x = Conv2DTranspose(128, (5, 5), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = Conv2DTranspose(128, (7, 7), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
x = UpSampling2D((2, 2))(x) 
x = Conv2DTranspose(64, (11, 11), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 
decoded = Conv2DTranspose(1, (11, 11), activation='relu', strides= 1, padding='valid', kernel_initializer='glorot_uniform')(x) 

print (K.int_shape(decoded)) 

autoencoder = Model(input_img, decoded) 
autoencoder.compile(optimizer = 'adadelta', loss = 'sparse_categorical_crossentropy', metrics = ['accuracy']) 

train_datagen = ImageDataGenerator(
rescale=1./255, 
    shear_range=0.2, 
    zoom_range=0.2, 
    horizontal_flip=True) 

test_datagen = ImageDataGenerator(rescale=1./255) 

x_train = train_datagen.flow_from_directory(
    'data/train', 
    target_size = (512, 512), color_mode = 'grayscale', 
    batch_size = batch_size, 
    class_mode = 'binary') 

x_test = test_datagen.flow_from_directory(
    'data/validation', 
    target_size = (512, 512), color_mode = 'grayscale', 
    batch_size = batch_size, 
    class_mode = 'binary') 

autoencoder.fit_generator(
x_train, 
steps_per_epoch = nb_train_samples // batch_size, 
epochs = epochs, 
validation_data = x_test, 
validation_steps = nb_validation_samples // batch_size) 

decoded_imgs = autoencoder.predict(x_test) 

Zusammenfassung Modell ist wie folgt:

Layer (type)     Output Shape    Param # 
================================================================= 
input_1 (InputLayer)   (None, 512, 512, 1)  0 
_________________________________________________________________ 
conv2d_1 (Conv2D)   (None, 502, 502, 64)  7808 
_________________________________________________________________ 
conv2d_2 (Conv2D)   (None, 492, 492, 64)  495680 
_________________________________________________________________ 
max_pooling2d_1 (MaxPooling2 (None, 246, 246, 64)  0 
_________________________________________________________________ 
conv2d_3 (Conv2D)   (None, 240, 240, 128)  401536 
_________________________________________________________________ 
conv2d_4 (Conv2D)   (None, 236, 236, 128)  409728 
_________________________________________________________________ 
max_pooling2d_2 (MaxPooling2 (None, 118, 118, 128)  0 
_________________________________________________________________ 
conv2d_5 (Conv2D)   (None, 114, 114, 256)  819456 
_________________________________________________________________ 
conv2d_6 (Conv2D)   (None, 112, 112, 256)  590080 
_________________________________________________________________ 
max_pooling2d_3 (MaxPooling2 (None, 56, 56, 256)  0 
_________________________________________________________________ 
conv2d_7 (Conv2D)   (None, 54, 54, 512)  1180160 
_________________________________________________________________ 
conv2d_8 (Conv2D)   (None, 52, 52, 512)  2359808 
_________________________________________________________________ 
max_pooling2d_4 (MaxPooling2 (None, 26, 26, 512)  0 
_________________________________________________________________ 
up_sampling2d_1 (UpSampling2 (None, 52, 52, 512)  0 
_________________________________________________________________ 
conv2d_transpose_1 (Conv2DTr (None, 54, 54, 512)  2359808 
_________________________________________________________________ 
conv2d_transpose_2 (Conv2DTr (None, 56, 56, 512)  2359808 
_________________________________________________________________ 
up_sampling2d_2 (UpSampling2 (None, 112, 112, 512)  0 
_________________________________________________________________ 
conv2d_transpose_3 (Conv2DTr (None, 114, 114, 256)  1179904 
_________________________________________________________________ 
conv2d_transpose_4 (Conv2DTr (None, 118, 118, 256)  1638656 
_________________________________________________________________ 
up_sampling2d_3 (UpSampling2 (None, 236, 236, 256)  0 
_________________________________________________________________ 
conv2d_transpose_5 (Conv2DTr (None, 240, 240, 128)  819328 
_________________________________________________________________ 
conv2d_transpose_6 (Conv2DTr (None, 246, 246, 128)  802944 
_________________________________________________________________ 
up_sampling2d_4 (UpSampling2 (None, 492, 492, 128)  0 
_________________________________________________________________ 
conv2d_transpose_7 (Conv2DTr (None, 502, 502, 64)  991296 
_________________________________________________________________ 
conv2d_transpose_8 (Conv2DTr (None, 512, 512, 1)  7745 
================================================================= 
Total params: 16,423,745 
Trainable params: 16,423,745 
Non-trainable params: 0 
_________________________________________________________________ 

Bitte helfen Sie mir. Liegt das an Conv2DTranspose(), das ich für die Decodierung verwendet habe?

Answer 1

Es ist definitiv kein Problem mit der Modellarchitektur selbst (weil es auf meiner Seite arbeitet). Scheint wie ein Problem mit deinen Grundwahrheitsdaten. Er muss die gleichen Abmessungen wie Ihr Eingangsbild haben, aber flow_from_directory liefert keine solchen Daten zur Grundwahrheit. Ich denke, Sie müssen Ihren eigenen benutzerdefinierten Datengenerator verwenden.