keras list of Numpy arrays not the size model expected

I am having trouble finding the correct way of passing multiple inputs to a model. The model has 2 inputs

(256, 256, 3)

(256, 256, 3)

and 1 output

(256, 256, 3)

I am producing the images via ImageDataGenerator:

ImageDataGenerator

x_data_gen = ImageDataGenerator(
horizontal_flip=True,
validation_split=0.2)


And I am producing the samples via a python generator:

def image_sampler(datagen, batch_size, subset="training"):

for imgs in datagen.flow_from_directory('data/r_cropped', batch_size=batch_size, class_mode=None, seed=1, subset=subset):

g_y =

noises =
bw_images =
for i in imgs:
# append to expected output the original image
g_y.append(i/255.0)

noises.append(generate_noise(1, 256, 3)[0])
bw_images.append(iu_rgb2gray(i))

yield(np.array([noises, bw_images]), np.array(g_y))


When trying to train the model with:

generator.fit_generator(
image_sampler(x_data_gen, 32),
validation_data=image_sampler(x_data_gen,32,"validation"),
epochs=EPOCHS,
steps_per_epoch= 540,
validation_steps=160 )


I receive an error stating:

Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays

while the message is quite clear, I do not understand how to fix the generation process to solve it.

I tried:

yield([noises, bw_images], np.array(g_y))


but this didn't work as it would reach a different error:

AttributeError: 'list' object has no attribute 'shape'

what am I missing?

1 Answer
1

When you have multiple inputs/outputs you should pass them as a list of numpy arrays. So your second approach is correct but you have forgotten to convert the lists to numpy arrays in your second approach:

yield ([np.array(noises), np.array(bw_images)], np.array(g_y))


A more verbose approach to make sure everything is correct, is to choose names for the input and output layers. Example:

input_1 = layers.Input(# other args, name='input_1')
input_2 = layers.Input(# other args, name='input_2')


Then, use those names like this in your generator function:

yield ({'input_1': np.array(noises), 'input_2': np.array(bw_images)}, {'output': np.array(g_y)})


By doing so, you are making sure that the mapping is done correctly.

thank you very much! now for some reason it seems that it saturates the memory before even starting, but at least this problem is solved!
– Stormsson
Jun 29 at 12:49

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