Styjun

I am using newest version of R studio, 1.2.1335. I ma trying to use LSTM model using reticulate inside R script or PYthon inside Rmarkdown document, but both return an error.

First, my try with reticulate:

library(reticulate)
use_condaenv('my_env')


SAMPLES=10000
A = 0.7
B = 10000.0
AMPU = 0.2
AMPN = 0.08
LAG = 5

tseq <- seq(0,0.1*pi,,(SAMPLES+LAG))
set.seed(1)
c.unif <- runif(SAMPLES+LAG)
c.norm <- rnorm(SAMPLES+LAG)
y1 <- A*sin(B*tseq)+c.unif*AMPU 
y2 <- A*sin(B*tseq)+c.norm*AMPN 

data <- cbind(y1[1:SAMPLES],y2[1:SAMPLES])
trgt <- cbind(y1[LAG:(SAMPLES+LAG-1)],y2[LAG:(SAMPLES+LAG-1)])

LEN = 8
SRATE = 1
STRIDE = 1
BATCH = 16

### TEST
np <- import("numpy")
keraspy <- import("keras")
pybuiltin <- import_builtins(convert = TRUE)

train_gen <- keraspy$preprocessing$sequence$TimeseriesGenerator(
 data=data,
 targets=trgt,
 length=LEN,
 sampling_rate = SRATE,
 stride = STRIDE,
 start_index = 1,
 end_index = 9000,
 shuffle = FALSE,
 reverse = FALSE,
 batch_size = BATCH
)

val_gen = keraspy$preprocessing$sequence$TimeseriesGenerator(
 data=data,
 targets=trgt,
 length=LEN,
 sampling_rate = SRATE,
 stride= STRIDE,
 start_index = 9001,
 end_index = 10000,
 shuffle = FALSE,
 reverse = FALSE,
 batch_size = BATCH
)


model = keraspy$models$Sequential()
model$add(keraspy$layers$Flatten(input_shape = c(pybuiltin$int(LEN), 2L)))
model$add(keraspy$layers$Dense(units = 32L, activation = "relu"))
model$add(keraspy$layers$Dense(units = 2L))

model$compile(optimizer = "rmsprop", loss = "mae")

stepsPerEpoch <- floor((train_gen$end_index - train_gen$start_index)/BATCH)
validationSteps <- floor((val_gen$end_index - val_gen$start_index)/BATCH)
model$fit_generator(
 train_gen,
 steps_per_epoch = pybuiltin$int(stepsPerEpoch),
 epochs = pybuiltin$int(100),
 validation_data = val_gen,
 validation_steps = pybuiltin$int(validationSteps)
)

This return error mentioned in the title:

Error in py_call_impl(callable, dots$args, dots$keywords) : 
 TypeError: 'float' object cannot be interpreted as an integer 

I have also tried to estimate same model inside R notebook:

---
title: "R Notebook"
output: html_notebook
---

```r
SAMPLES=10000
A = 0.7
B = 10000.0
AMPU = 0.2
AMPN = 0.08
LAG = 5

tseq <- seq(0,0.1*pi,,(SAMPLES+LAG))
set.seed(1)
c.unif <- runif(SAMPLES+LAG)
c.norm <- rnorm(SAMPLES+LAG)
y1 <- A*sin(B*tseq)+c.unif*AMPU 
y2 <- A*sin(B*tseq)+c.norm*AMPN 

data <- cbind(y1[1:SAMPLES],y2[1:SAMPLES])
trgt <- cbind(y1[LAG:(SAMPLES+LAG-1)],y2[LAG:(SAMPLES+LAG-1)])

LEN = 8
SRATE = 1
STRIDE = 1
BATCH = 16

```

```python
import numpy as np
from keras.preprocessing.sequence import TimeseriesGenerator
from keras.models import Sequential
from keras.layers import Dense
import keras
import math

train_gen = TimeseriesGenerator(
 data=r.data,
 targets=r.trgt,
 length=r.LEN,
 sampling_rate = r.SRATE,
 stride = r.STRIDE,
 start_index = 1,
 end_index = 9000,
 shuffle = False,
 reverse = False,
 batch_size = r.BATCH
)

val_gen = TimeseriesGenerator(
 data= r.data,
 targets = r.trgt,
 length= r.LEN,
 sampling_rate = r.SRATE,
 stride= r.STRIDE,
 start_index = 9001,
 end_index = 10000,
 shuffle = False,
 reverse = False,
 batch_size = r.BATCH
)

math.floor((train_gen.end_index - train_gen.start_index)/int(r.BATCH))
train_gen.end_index

model = Sequential()
model.add(keras.layers.Flatten(input_shape = (int(r.LEN), 2)))
model.add(Dense(32, activation = "relu"))
model.add(Dense(2))
model.compile(optimizer = 'rmsprop', loss = 'mae')
model.fit_generator(
 train_gen,
 steps_per_epoch = math.floor((train_gen.end_index - train_gen.start_index)/float(r.BATCH)),
 epochs = 100,
 validation_data = val_gen,
 validation_steps = math.floor((val_gen.end_index - val_gen.start_index)/float(r.BATCH)) 
)
```

but I got the same error.

If I define numbers as integers in the last step, I get smae error:

model.fit_generator(
 train_gen,
 steps_per_epoch = int(561),
 epochs = int(100),
 validation_data = val_gen,
 validation_steps = int(61) 
)

This is my first time using python inside Rstudio. Not very promising...