The fastai library
simplifies training fast and accurate neural nets using modern best
practices. See the fastai website to get started. The library is based
on research into deep learning best practices undertaken at
fast.ai, and includes “out of the box” support for
vision, text, tabular, and
collab (collaborative filtering) models.
Get dataset and build cnn_learner.
URLs_MNIST_SAMPLE()
tfms = aug_transforms(do_flip = FALSE)
path = 'mnist_sample'
bs = 20
data = ImageDataLoaders_from_folder(path, batch_tfms = tfms, size = 26, bs = bs)
learn = cnn_learner(data, xresnet50_deep(), metrics = accuracy)Modify channels to 1:
init = learn$model[0][0][0][['in_channels']]
print(init)
# 3
learn$model[0][0][0][['in_channels']] %f% 1L
print(learn$model[0][0][0][['in_channels']])
# 1Here, one can observe a special assignment %f%. It helps
for safe modification of layer parameters.
How to see and modify other parameters of the layer? First see names:
names(learn$model[0][0][0])Cnn layer parameters:
[1] "add_module" "apply" "bfloat16"
[4] "bias" "buffers" "children"
[7] "cpu" "cuda" "dilation"
[10] "double" "dump_patches" "eval"
[13] "extra_repr" "float" "forward"
[16] "groups" "half" "has_children"
[19] "in_channels" "kernel_size" "load_state_dict"
[22] "modules" "named_buffers" "named_children"
[25] "named_modules" "named_parameters" "out_channels"
[28] "output_padding" "padding" "padding_mode"
[31] "parameters" "register_backward_hook" "register_buffer"
[34] "register_forward_hook" "register_forward_pre_hook" "register_parameter"
[37] "requires_grad_" "reset_parameters" "share_memory"
[40] "state_dict" "stride" "T_destination"
[43] "to" "train" "training"
[46] "transposed" "type" "weight"
[49] "zero_grad" Kernel size from (3, 3) to 9.
In addition, one could replace values inside tensors with the same assignment.
For single in-place value modification:
x = tensor(c(1,2), c(3,4))
# tensor([[1., 2.],
# [3., 4.]])
print(x[0][0])
# tensor(1.)
# Now change it to 99.
x[0][0] %f% 99
print(x[0][0])
# tensor(99.)
print(x)
# tensor([[99., 2.],
# [ 3., 4.]])Modify 2 or more values:
How to slice tensors? For less confusion, the slicing is the same as
in python. narrow function requires a tensor and its
dimensions. Let’s see an example:
We can extract and play with tensor dimensions:
First lets understand the tensor:
a %>% narrow('[:,:,:,:]')tensor([[[[ 1, 28, 55],
[10, 37, 64],
[19, 46, 73]],
[[ 4, 31, 58],
[13, 40, 67],
[22, 49, 76]],
[[ 7, 34, 61],
[16, 43, 70],
[25, 52, 79]]],
[[[ 2, 29, 56],
[11, 38, 65],
[20, 47, 74]],
[[ 5, 32, 59],
[14, 41, 68],
[23, 50, 77]],
[[ 8, 35, 62],
[17, 44, 71],
[26, 53, 80]]],
[[[ 3, 30, 57],
[12, 39, 66],
[21, 48, 75]],
[[ 6, 33, 60],
[15, 42, 69],
[24, 51, 78]],
[[ 9, 36, 63],
[18, 45, 72],
[27, 54, 81]]]], dtype=torch.int32)We could imagine that the tensor contains 3 R lists and each list contain 3 matrices with 3 rows and 3 columns.
: without any indicated value before or after
: will not modify the tensor.
How to extract 1st list from the tensor? Very simple:
tensor([[[[ 1, 28, 55],
[10, 37, 64],
[19, 46, 73]],
[[ 4, 31, 58],
[13, 40, 67],
[22, 49, 76]],
[[ 7, 34, 61],
[16, 43, 70],
[25, 52, 79]]]], dtype=torch.int32)Why from 0? Because indexing starts from 0 not from
1.
We could extract 1st matrix from all 3 lists.
tensor([[[ 1, 28, 55],
[10, 37, 64],
[19, 46, 73]],
[[ 2, 29, 56],
[11, 38, 65],
[20, 47, 74]],
[[ 3, 30, 57],
[12, 39, 66],
[21, 48, 75]]], dtype=torch.int32)How to build a model with fastai Module class?
Simple!
Prepare data:
library(magrittr)
library(fastai)
library(zeallot)
if(!file.exists('mnist.pkl.gz')) {
download.file('http://deeplearning.net/data/mnist/mnist.pkl.gz','mnist.pkl.gz')
R.utils::gunzip("mnist.pkl.gz", remove=FALSE)
}
c(c(x_train, y_train), c(x_valid, y_valid), res) %<-%
reticulate::py_load_object('mnist.pkl', encoding = 'latin-1')
x_train = x_train[1:500,1:784]
x_valid = x_valid[1:500,1:784]
y_train = as.integer(y_train)[1:500]
y_valid = as.integer(y_valid)[1:500]Plot example:
example = array_reshape(x_train[1,], c(28,28))
example %>% show_image(cmap = 'gray') %>% plot()Prepare data loaders and build a model:
TensorDataset = torch()$utils$data$TensorDataset
bs = 32
train_ds = TensorDataset(tensor(x_train), tensor(y_train))
valid_ds = TensorDataset(tensor(x_valid), tensor(y_valid))
train_dl = TfmdDL(train_ds, bs = bs, shuffle = TRUE)
valid_dl = TfmdDL(valid_ds, bs = 2 * bs)
dls = Data_Loaders(train_dl, valid_dl)
one = one_batch(dls)
x = one[[1]]
y = one[[2]]
x$shape; y$shape
nn = nn()
Functional = torch()$nn$functionalPut your model into nn_module function:
model = nn_module(function(self) {
self$lin1 = nn$Linear(784L, 50L, bias=TRUE)
self$lin2 = nn$Linear(50L, 10L, bias=TRUE)
forward = function(y) {
x = self$lin1(y)
x = Functional$relu(x)
self$lin2(x)
}
})Fit the model:
learn = Learner(dls, model, loss_func=nn$CrossEntropyLoss(), metrics=accuracy)
learn %>% summary()
learn %>% fit_one_cycle(1, 1e-2)Note: if CUDA is available then the model will be automatically trained on GPU.