Hi Ricardo,
On mer., 08 mai 2024 at 12:11, Ricardo Wurmus <rek...@elephly.net> wrote:
> This appears to be a common problem, but we don't know why. It's
> probably related to the bazel-build-system. You'll get substitutes only
> if you use `--no-grafts'.
Ah, weird… I miss how the build system could impact the content-address.
Another story. :-)
>> Then I get this:
>>
>> --8<---------------cut here---------------start------------->8---
>> $ guix time-machine -C channels.scm -- shell -C r r-keras -C
>> python-minimal r-reticulate tensorflow@2.13.1
>
> You need python-tensorflow (also from guix-science), not just the
> tensorflow library.
Cool! It just works!
Thank you.
Cheers,
simon
--8<---------------cut here---------------start------------->8---
$ guix time-machine -C channels.scm \
-- shell -C r r-keras -C python-minimal r-reticulate tensorflow@2.13.1
python-tensorflow
[env]$ R
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-unknown-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(keras)
> model <- keras_model_sequential()
Would you like to create a default python environment for the reticulate
package? (Yes/no/cancel) no
2024-05-08 12:30:26.110266: I tensorflow/core/util/port.cc:110] oneDNN custom
operations are on. You may see slightly different numerical results due to
floating-point round-off errors from different computation orders. To turn them
off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-05-08 12:30:26.137568: I
tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is
optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 AVX512F
AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the
appropriate compiler flags.
WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard
installation.
WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard
installation.
WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard
installation.
WARNING:root:Limited tf.summary API due to missing TensorBoard installation.
WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard
installation.
WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard
installation.
WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard
installation.
> model %>%
# Adds a densely-connected layer with 64 units to the model:
layer_dense(units = 64, activation = 'relu') %>%
# Add another:
layer_dense(units = 64, activation = 'relu') %>%
# Add a softmax layer with 10 output units:
layer_dense(units = 10, activation = 'softmax')
+ + + + + + + + + >
> model %>% compile(
optimizer = 'adam',
loss = 'categorical_crossentropy',
metrics = list('accuracy')
)
+ + + + >
> data <- matrix(rnorm(1000 * 32), nrow = 1000, ncol = 32)
labels <- matrix(rnorm(1000 * 10), nrow = 1000, ncol = 10)
model %>% fit(
data,
labels,
epochs = 10,
batch_size = 32
)
> > > + + + + + Epoch 1/10
32/32 [==============================] - 0s 837us/step - loss: -0.2669 -
accuracy: 0.1060
32/32 [==============================] - 0s 951us/step - loss: -0.2669 -
accuracy: 0.1060
Epoch 2/10
32/32 [==============================] - 0s 743us/step - loss: -0.4499 -
accuracy: 0.1120
32/32 [==============================] - 0s 1ms/step - loss: -0.4499 -
accuracy: 0.1120
Epoch 3/10
32/32 [==============================] - 0s 682us/step - loss: -0.6262 -
accuracy: 0.1050
32/32 [==============================] - 0s 736us/step - loss: -0.6262 -
accuracy: 0.1050
Epoch 4/10
32/32 [==============================] - 0s 643us/step - loss: -0.8110 -
accuracy: 0.1150
32/32 [==============================] - 0s 695us/step - loss: -0.8110 -
accuracy: 0.1150
Epoch 5/10
32/32 [==============================] - 0s 614us/step - loss: -1.0271 -
accuracy: 0.1270
32/32 [==============================] - 0s 659us/step - loss: -1.0271 -
accuracy: 0.1270
Epoch 6/10
32/32 [==============================] - 0s 535us/step - loss: -1.1987 -
accuracy: 0.1480
32/32 [==============================] - 0s 599us/step - loss: -1.1987 -
accuracy: 0.1480
Epoch 7/10
32/32 [==============================] - 0s 637us/step - loss: -1.4685 -
accuracy: 0.1230
32/32 [==============================] - 0s 739us/step - loss: -1.4685 -
accuracy: 0.1230
Epoch 8/10
32/32 [==============================] - 0s 608us/step - loss: -1.7238 -
accuracy: 0.1380
32/32 [==============================] - 0s 654us/step - loss: -1.7238 -
accuracy: 0.1380
Epoch 9/10
32/32 [==============================] - 0s 552us/step - loss: -1.9540 -
accuracy: 0.1270
32/32 [==============================] - 0s 592us/step - loss: -1.9540 -
accuracy: 0.1270
Epoch 10/10
32/32 [==============================] - 0s 705us/step - loss: -2.2367 -
accuracy: 0.1160
32/32 [==============================] - 0s 773us/step - loss: -2.2367 -
accuracy: 0.1160
>
--8<---------------cut here---------------end--------------->8---
