Error in py_call_impl(callable, call_args$unnamed, call_args$named) : ValueError: Input 2 of layer "functional_4" is incompatible with the layer:

[yuriy-babenko]

https://github.com/t-kalinowski/deep-learning-with-R-2nd-edition-code/blob/5d666f93d52446511a8a8e4eb739eba1c0ffd199/ch07.R#L146C1-L150C2

Executed code:

`install.packages("remotes") remotes::install_github("rstudio/tensorflow") reticulate::install_python() install.packages("keras3") keras3::install_keras(envname = "r-reticulate") library(keras3)

model <- keras_model_sequential() %>% layer_dense(units = 64, activation = "relu") %>% layer_dense(units = 10, activation = "softmax")

Not build yet, not weights

model$weights

model$build(input_shape = shape(NA, 3)) str(model$weights)

model

model <- keras_model_sequential(input_shape = c(3), name = "my model" ) %>% layer_dense(units = 64, activation = "relu") %>% layer_dense(units = 10, activation = "softmax")

model

Functional API

inputs <- layer_input(shape = c(3), name = "my_input") features <- inputs %>% layer_dense(64, activation = "relu") outputs <- features %>% layer_dense(10, activation = "softmax") model <- keras_model(inputs = inputs, outputs = outputs)

model

multi-input, multi-output

vocabulary_size <- 10000 num_tags <- 100 num_departments <- 4

title <- layer_input(shape = c(vocabulary_size), name = "title") text_body <- layer_input(shape = c(vocabulary_size), name = "text_body") tags <- layer_input(shape = c(vocabulary_size), name = "tags")

features <- layer_concatenate(list(title, text_body, tags)) %>% layer_dense(64, activation = "relu")

priority <- features %>%
layer_dense(1, activation = "sigmoid", name = "priority")

department <- features %>%
layer_dense(num_departments, activation = "softmax", name = "departments")

model <- keras_model( inputs = list(title, text_body, tags), outputs = list(priority, department) )

num_samples <- 1280 random_uniform_array <- function(dim) array(runif(prod(dim)), dim)

random_vectorized_array <- function(dim) array(sample(0:1, prod(dim), replace = TRUE), dim)

title_data <- random_vectorized_array(c(num_samples, vocabulary_size)) text_body_data <- random_vectorized_array(c(num_samples, vocabulary_size)) tags_data <- random_vectorized_array(c(num_samples, num_tags))

priority_data <- random_vectorized_array(c(num_samples, 1)) department_data <- random_vectorized_array(c(num_samples, num_departments))

model %>% compile( optimizer = "rmsprop", loss = c("mean_squared_error", "categorical_crossentropy"), metrics = c("mean_absolute_error", "accuracy") )

model %>% fit( x = list(title_data, text_body_data, tags_data), y = list(priority_data, department_data), epochs = 1 )`

Error message:

Error in py_call_impl(callable, call_args$unnamed, call_args$named) : ValueError: Input 2 of layer "functional_4" is incompatible with the layer: expected shape=(None, 10000), found shape=(32, 100) Runreticulate::py_last_error()for details.

Output of detailed log:

`── R Traceback ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ▆

1. ├─model %>% ...
2. ├─generics::fit(...)
3. └─keras3:::fit.keras.src.models.model.Model(...)
4. ├─base::do.call(object$fit, args)
5. └─reticulate (local) <python.builtin.method>(...)
6. └─reticulate:::py_call_impl(callable, call_args$unnamed, call_args$named) See reticulate::py_last_error()$r_trace$full_call for more details.`

If I understand the issue correctly this is related to the move to Keras3 mentioned here: https://github.com/rstudio/keras3/issues/1427#issuecomment-2041491532

Looking at keras3: https://keras.io/api/layers/core_layers/embedding/, there is indeed no "input_length" any longer but what I still do not understand is where do we use embedding in the above code? We are accessing the "Functional API" and nothing in code suggests we call "Embedding Layer".