Keras (R) error while executing fit generator - r

Question

I have an error using fit_generator in R...
here's my code..`
model <- keras_model_sequential()
model %>%
layer_conv_2d(32, c(3,3), input_shape = c(64, 64, 3)) %>%
layer_activation("relu") %>%
layer_max_pooling_2d(pool_size = c(2,2)) %>%
layer_conv_2d(32, c(3, 3)) %>%
layer_activation("relu") %>%
layer_max_pooling_2d(pool_size = c(2, 2)) %>%
layer_flatten() %>%
layer_dense(128) %>%
layer_activation("relu") %>%
layer_dense(128) %>%
layer_activation("relu") %>%
layer_dense(2) %>%
layer_activation("softmax")
opt <- optimizer_adam(lr = 0.001, decay = 1e-6)
model %>%
compile(loss = "categorical_crossentropy", optimizer = opt, metrics = "accuracy")
train_gen <- image_data_generator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = T)
test_gen <- image_data_generator(rescale = 1./255)
train_set = train_gen$flow_from_directory('dataset/training_set',
target_size = c(64, 64),
class_mode = "categorical")
test_set = test_gen$flow_from_directory('dataset/test_set',
target_size = c(64, 64),
batch_size = 32,
class_mode = 'categorical')
model$fit_generator(train_set,
steps_per_epoch = 50,
epochs = 10)
Error:
Error in py_call_impl(callable, dots$args, dots$keywords) :
StopIteration: 'float' object cannot be interpreted as an integer
If I put validation set it has another error too
bool(validation_data). Float error..

Answer 1

It is difficult help you without a minimal reproducible example.
I am guessing you get this error when you are trying to run
train_set = train_gen$flow_from_directory('dataset/training_set',
target_size = c(64, 64),
class_mode = "categorical")
Here you are calling the python function yourself using reticulate and not a keras (the R package) wrapper. That might work, but you have to be more explicit about the type and use target_size = as.integer(c(64, 64)), since python expects an integer.
Alternatively, I would suggest looking into the flow_images_from_directory() function included in the keras package.
The same goes for
model$fit_generator(train_set,
steps_per_epoch = 50,
epochs = 10)
I'd suggest looking into
model %>%
fit_generator()
instead, which is part of the keras package.