How to adapt input_shape parameter to table dimensions in R

So far, I have a net looking like this:

data <- data2018

# Splitting data into train and test
set.seed(101)
sample <- sample.int(n = nrow(data), size = floor(.75 * nrow(data)), replace = F)
train <- as.data.table(data[sample, ])
test <- as.data.table(data[-sample, ])

# Preparing the training set
cols <- c(names(train))
train %<>% mutate_at(cols, as.factor)
train %<>% mutate_at(cols, as.numeric)
train %<>% mutate_at(cols,~(scale(.) %>% as.vector))
train[is.na(train) ] <- 0

# Splitting train into x (parameters) and y (prediction values)
x_train <- data.matrix(train[,-"1"])
y_train <- data.matrix(train[, "1"])

# Designing model
model <- keras_model_sequential() %>%
layer_dense(units = 128, activation = 'tanh', input_shape = dim(x_train)[-1]) %>%
layer_dropout(rate = 0.4) %>%
layer_dense(units = 64, activation = 'tanh') %>%
layer_dropout(rate = 0.3) %>%
layer_dense(units = 32, activation = 'tanh') %>%
layer_dropout(rate = 0.2) %>%
layer_dense(units = 16, activation = 'tanh') %>%
layer_dropout(rate = 0.1) %>%
layer_dense(units = length(unique(data$`1`)), activation = 'softmax')

# Summarize model layers and units
summary(model)

# Compile model
model %>% compile(
loss = 'categorical_crossentropy',
optimizer = optimizer_rmsprop(),
metrics = c('accuracy')
)

# Train model
history <- model %>% fit(
x_train, y_train,
epochs = 100, batch_size = 32,
validation_split = 0.2
)

Due to secrecy issues, I cannot show the original datatable but it is a simple table consisting of 7 rows and 34 columns (only a small simulation chunk).

The input (x_train) is already normalized and vectorized. It is a simple table with rows and columns.

dim(x_train)

Result:

> dim(x_train)
[1]  5 33

However, if I try to train the model, keras prints out this cryptic error message.

Error in py_call_impl(callable, dots$args, dots$keywords) : 
ValueError: in user code:

/home/seschlbeck/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-   packages/tensorflow/python/keras/engine/training.py:855 train_function  *
    return step_function(self, iterator)
/home/seschlbeck/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:845 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
/home/seschlbeck/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:1285 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/home/seschlbeck/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
/home/seschlbeck/.local/share/r-miniconda/envs/r-reticulate/lib/python3

This has something to do with my input shape, I guess. But I just can't figure out why... I have already tried out the following ones (all not working)

dim(x_train)
c(5, 33)
c(None, 5, 33)
dim(x_train)[1]

Does anyone have an idea what I am doing wrong here? Any help is appreciated...