gabrieldemarmiesse
commented
6 years ago Hi! I think I can help you.
When using neural networks, if you want your model to handle multiple images sizes, the layers used in the network must be able to handle variables size images. But Dense layers need to know the exact shapes of the tensor just before to know the shape of the kernel to optimize. Long story short, if you want your network to handle multiple image sizes, I advise you to use the pattern convolutions-> globalAveragePooling - > Dense if you want to do classification. Or only convolutional layers if you work on segmentation/artifact removals/ hyperresolution.
I hope it helps.
gabartas
mshaikh2
Pranav-Rastogi
imohitch
Hello, and sorry if a similar question have been asked, but I am reaching a deadend in my research for a solution for this problem.
I am trying to train a keras CNN on 2d datasets, but those datasets have a highly varying size, and padding them would have little sense. I understood that it was possible to train a CNN with variable training data size using batches of size 1, but the model.fit still won't work. As I am starting with keras, I could have missed something, but I keep getting this message :
Error when checking input: expected conv2d_1_input to have 4 dimensions, but got array with shape (52, 1)
yet, here is a simplified version of my code :
from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils import numpy as np
Load dataset
dataset = np.load("openml\datasets.npy") labels = np.load("openml\labels.npy")
Y = []
input and output
X = dataset Y = np_utils.to_categorical(Y, 5)
create model
model = Sequential() model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(None,None,1))) model.add(Convolution2D(32, 3, 3, activation='relu')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Dense(128, activation='relu')) model.add(Dense(5, activation='softmax'))
Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
Fit the model
model.fit(X, Y, epochs=50, batch_size=1)
model.save('meddling_model.h5')
From what I understood, the input_shape=(None,None,1) should allow the CNN to manage inputs of varying sizes. I think the problem is caused by the fact that my dataset variable is a list of arrays, with a "shape" of ([number of datasets],1) and each arrays having a very different size as they are quite dissimilar 2d datasets.
I know that 52 training samples seems to not be enough, I was using these ones in order to check if it was possible to do what I'm trying to do before using my full training data.
Sorry for the long question, and thank you if you read all that through.