とValueError：形状を持つことが期待convolution2d_input_8（なし、3、150、150）が、形状を持つ配列（32、150、150、3）

Question

を得た私は、私はそのがあるためであると考え、次のエラー

ValueError: Error when checking model input: expected convolution2d_input_8 to have shape (None, 3, 150, 150) but got array with shape (32, 150, 150, 3)

を得ましたのdim_orderingの。テンソルフローバックエンドを使用しています。 しかし、私はそれを削除した場合、私は別のエラーが出る：

ValueError: Negative dimension size caused by subtracting 3 from 1 for 'Conv2D_17' (op: 'Conv2D') with input shapes: [?,1,74,16], [3,3,16,32].

import keras 
import numpy as np 
np.random.seed(123) # reproducibility 

from keras.preprocessing.image import ImageDataGenerator 
from keras.models import Sequential 
from keras.layers import Convolution2D, MaxPooling2D 
from keras.layers import Activation, Dropout, Flatten, Dense 


# expected image size 
img_width, img_height = 150, 150 
# folder containing the images on which 
# the network will train. The train folder 
# has two sub folders, dogs and cats. 
train_data_dir = 'C:/Users/Ishan Sohony/Downloads/Compressed/train' 
# folder containing the validation samples 
# folder structure is same as the training folder 
validation_data_dir = 'C:/Users/Ishan Sohony/Downloads/Compressed/test1' 
# how many images to be considered for training 
train_samples = 2000 
# how many images to be used for validation 
validation_samples = 800 
# how many runs will the network make 
# over the training set before starting on 
# validation 
epoch = 50 
# this is the augmentation configuration we will use for training 
# we are generating a lot of transformed images so that the model 
# can handle variety in the real world scenario 
train_datagen = ImageDataGenerator(
     rescale=1./255, 
     shear_range=0.2, 
     zoom_range=0.2, 
     horizontal_flip=True) 

# this is the augmentation configuration we will use for testing: 
# only rescaling 
test_datagen = ImageDataGenerator(rescale=1./255) 
# this section is actually taking images from the folder 
# and passing on to the ImageGenerator which then 
# creates a lot of transformed versions 

train_generator = train_datagen.flow_from_directory(
     train_data_dir, 
     target_size=(img_width, img_height), 
     batch_size=32, 
     class_mode='binary') 

validation_generator = test_datagen.flow_from_directory(
     validation_data_dir, 
     target_size=(img_width, img_height), 
     batch_size=32, 
     class_mode='binary') 
# ** Model Begins ** 
model = Sequential() 
model.add(Convolution2D(32, 3, 3, input_shape=(3, img_width, img_height),dim_ordering='th')) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2),dim_ordering='th')) 

model.add(Convolution2D(32, 3, 3)) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2),dim_ordering='th')) 

model.add(Convolution2D(64, 3, 3)) 
model.add(Activation('relu')) 
model.add(MaxPooling2D(pool_size=(2, 2),dim_ordering='th')) 

model.add(Flatten()) 
model.add(Dense(64)) 
model.add(Activation('relu')) 
model.add(Dropout(0.5)) 
model.add(Dense(1)) 
model.add(Activation('sigmoid')) 
# ** Model Ends ** 
model.compile(loss='binary_crossentropy', 
       optimizer='rmsprop', 
       metrics=['accuracy']) 


# this is where the actual processing happens 
# it will take some time to run this step. 
model.fit_generator(
     train_generator, 
     samples_per_epoch=train_samples, 
     nb_epoch=epoch, 
     validation_data=validation_generator, 
     nb_val_samples=validation_samples) 

model.save_weights('trial.h5') 

Answer 1

Input shape

4D tensor with shape: (samples, channels, rows, cols) if dim_ordering='th' or 4D tensor with shape: (samples, rows, cols, channels) if dim_ordering='tf'.

Output shape

4D tensor with shape: (samples, nb_filter, new_rows, new_cols) if dim_ordering='th' or 4D tensor with shape: (samples, new_rows, new_cols, nb_filter) if dim_ordering='tf'. rows and cols values might have changed due to padding. source

確かにそれは、ディメンションの順序から来ています。 TheanoまたはTensorflowを使用しているときに入力が異なるだけでなく、出力も同じです。

私が正しく理解していれば、いつものようにtfバックエンドを使うべきですが、入力の形を（150,150,3）に変更してください。 実際にTheanoに変更したい場合は、dim_orderingをのすべてののtheanoのtheanoに設定して、出力と入力の形状について互いに話すことができるようにしてください。

これは役に立ちますか？