I am trying to implement a Siamese network in Keras and I want to apply image transformations to the 2 input images using Keras Image Data Generators. As per the example in the docs- https://keras.io/preprocessing/image/, I've tried to implement it like this-
datagen_args = dict(rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True)
in_gen1 = ImageDataGenerator(**datagen_args)
in_gen2 = ImageDataGenerator(**datagen_args)
train_generator = zip(in_gen1, in_gen2)
model.fit(train_generator.flow([pair_df[:, 0,::],pair_df[:, 1,::]],
y_train,batch_size=16), epochs, verbose = 1)
But this code throws this error:
TypeError: zip argument #1 must support iteration
I've tried using itertools.izip
as suggested in Keras - Generator for large dataset of Images and Masks but this throws the same error.
How do I resolve this?
EDIT: In case anyone is interested, this worked finally-
datagen_args = dict(
featurewise_center=False,
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True)
in_gen1 = ImageDataGenerator(**datagen_args)
in_gen2 = ImageDataGenerator(**datagen_args)
in_gen1 = in_gen1.flow(pair_df[:, 0,::], y_train, batch_size = 16, shuffle = False)
in_gen2 = in_gen2.flow(pair_df[:, 1,::], y_train, batch_size = 16, shuffle = False)
for e in range(epochs):
batches = 0
for x1, x2 in itertools.izip(in_gen1,in_gen2):
# x1, x2 are tuples returned by the generator, check whether targets match
assert sum(x1[1] != x2[1]) == 0
model.fit([x1[0], x2[0]], x1[1], verbose = 1)
batches +=1
if(batches >= len(pair_df)/16):
break
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