Introduction || Quick Start || Tensor || Data Loading and Processing || Model || Autograd || Train || Save and load || Accelerating with Static Graphs || Mixed Precision
Quick Start
This section quickly implements a simple deep learning model through MindSpore APIs. For a deeper understanding of how to use MindSpore, see the reference links provided at the end of each section.
import mindspore
from mindspore import nn
from mindspore.dataset import vision, transforms
from mindspore.dataset import MnistDataset
Processing a Dataset
MindSpore provides Pipeline-based Data Engine and achieves efficient data preprocessing through Data Loading and Processing. In this tutorial, we use the Mnist dataset and pre-process dataset by using the data transformations provided by mindspore.dataset, after automatically downloaded.
The sample code in this chapter relies on
download, which can be installed by using the commandpip install download. If this document is run as Notebook, you need to restart the kernel after installation to execute subsequent code.
# Download data from open datasets\n",
from download import download
url = "https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/" \
"notebook/datasets/MNIST_Data.zip"
path = download(url, "./", kind="zip", replace=True)
Downloading data from https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/notebook/datasets/MNIST_Data.zip (10.3 MB)
file_sizes: 100%|██████████████████████████| 10.8M/10.8M [00:01<00:00, 6.73MB/s]
Extracting zip file...
Successfully downloaded / unzipped to ./
The directory structure of MNIST Dataset is as following：
MNIST_Data
└── train
├── train-images-idx3-ubyte (60000 training images)
├── train-labels-idx1-ubyte (60000 training labels)
└── test
├── t10k-images-idx3-ubyte (10000 test images)
├── t10k-labels-idx1-ubyte (10000 test labels)
After the data is downloaded, the dataset object is obtained.
train_dataset = MnistDataset('MNIST_Data/train')
test_dataset = MnistDataset('MNIST_Data/test')
Print the names of the data columns contained in the dataset for dataset pre-processing.
print(train_dataset.get_col_names())
['image', 'label']
Dataset in MindSpore uses the Data Processing Pipeline, which requires specifying operations such as map, batch, and shuffle. Here we use map to transform the image data and labels by scaling the input image to 1/255 and normalizing it according to the mean value of 0.1307 and the standard deviation value of 0.3081, and then the processed dataset is packed into a batch of size 64.
def datapipe(dataset, batch_size):
image_transforms = [
vision.Rescale(1.0 / 255.0, 0),
vision.Normalize(mean=(0.1307,), std=(0.3081,)),
vision.HWC2CHW()
]
label_transform = transforms.TypeCast(mindspore.int32)
dataset = dataset.map(image_transforms, 'image')
dataset = dataset.map(label_transform, 'label')
dataset = dataset.batch(batch_size)
return dataset
train_dataset = datapipe(train_dataset, 64)
test_dataset = datapipe(test_dataset, 64)
create_tuple_iterator or create_dict_iterator could be used to iterate over the dataset, printing the shape and dtype for image and label.
for image, label in test_dataset.create_tuple_iterator():
print(f"Shape of image [N, C, H, W]: {image.shape} {image.dtype}")
print(f"Shape of label: {label.shape} {label.dtype}")
break
Shape of image [N, C, H, W]: (64, 1, 28, 28) Float32
Shape of label: (64,) Int32
for data in test_dataset.create_dict_iterator():
print(f"Shape of image [N, C, H, W]: {data['image'].shape} {data['image'].dtype}")
print(f"Shape of label: {data['label'].shape} {data['label'].dtype}")
break
Shape of image [N, C, H, W]: (64, 1, 28, 28) Float32
Shape of label: (64,) Int32
For more detailed information, see Data Loading and Processing.
Building Network
mindspore.nn class is the base class for building all networks and is the basic unit of the network. When the user needs to customize the network, you can inherit the nn.Cell class and override the __init__ method and the construct method. __init__ contains the definitions of all network layers, and construct contains the transformation process of the data (Tensor).
# Define model
class Network(nn.Cell):
def __init__(self):
super().__init__()
self.flatten = nn.Flatten()
self.dense_relu_sequential = nn.SequentialCell(
nn.Dense(28*28, 512),
nn.ReLU(),
nn.Dense(512, 512),
nn.ReLU(),
nn.Dense(512, 10)
)
def construct(self, x):
x = self.flatten(x)
logits = self.dense_relu_sequential(x)
return logits
model = Network()
print(model)
Network<
(flatten): Flatten<>
(dense_relu_sequential): SequentialCell<
(0): Dense<input_channels=784, output_channels=512, has_bias=True>
(1): ReLU<>
(2): Dense<input_channels=512, output_channels=512, has_bias=True>
(3): ReLU<>
(4): Dense<input_channels=512, output_channels=10, has_bias=True>
>
>
For more detailed information, see Model.
Training Model
In model training, a complete training process (step) requires the following three steps:
Forward calculation: model predicts results (logits) and finds the prediction loss (loss) with the correct label (label).
Backpropagation: Using an automatic differentiation mechanism, the gradients of the model parameters (parameters) with respect to the loss are automatically found.
Parameter optimization: update the gradient to the parameter.
MindSpore uses a functional automatic differentiation mechanism, implemented through the steps above:
Define forward calculation function.
Obtain the gradient calculation function by function transformation, calling value_and_grad for details.
Define training functions, set to training mode by calling set_train for setting of training mode, and perform forward computation, back propagation and parameter optimization.
# Instantiate loss function and optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = nn.SGD(model.trainable_params(), 1e-2)
# 1. Define forward function
def forward_fn(data, label):
logits = model(data)
loss = loss_fn(logits, label)
return loss, logits
# 2. Get gradient function
grad_fn = mindspore.value_and_grad(forward_fn, None, optimizer.parameters, has_aux=True)
# 3. Define function of one-step training
def train_step(data, label):
(loss, _), grads = grad_fn(data, label)
optimizer(grads)
return loss
def train(model, dataset):
size = dataset.get_dataset_size()
model.set_train()
for batch, (data, label) in enumerate(dataset.create_tuple_iterator()):
loss = train_step(data, label)
if batch % 100 == 0:
loss, current = loss.asnumpy(), batch
print(f"loss: {loss:>7f} [{current:>3d}/{size:>3d}]")
In addition to training, we define test functions that are used to evaluate the performance of the model.
def test(model, dataset, loss_fn):
num_batches = dataset.get_dataset_size()
model.set_train(False)
total, test_loss, correct = 0, 0, 0
for data, label in dataset.create_tuple_iterator():
pred = model(data)
total += len(data)
test_loss += loss_fn(pred, label).asnumpy()
correct += (pred.argmax(1) == label).asnumpy().sum()
test_loss /= num_batches
correct /= total
print(f"Test: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
The training process requires several iterations of the dataset, and one complete iteration is called an epoch. In each round, the training set is traversed for training and the test set is used for prediction at the end. The loss value and prediction accuracy (Accuracy) of each round are printed, and it can be seen that the loss is decreasing and Accuracy is increasing.
epochs = 3
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train(model, train_dataset)
test(model, test_dataset, loss_fn)
print("Done!")
Epoch 1
-------------------------------
loss: 2.302088 [ 0/938]
loss: 2.290692 [100/938]
loss: 2.266338 [200/938]
loss: 2.205240 [300/938]
loss: 1.907198 [400/938]
loss: 1.455603 [500/938]
loss: 0.861103 [600/938]
loss: 0.767219 [700/938]
loss: 0.422253 [800/938]
loss: 0.513922 [900/938]
Test:
Accuracy: 83.8%, Avg loss: 0.529534
Epoch 2
-------------------------------
loss: 0.580867 [ 0/938]
loss: 0.479347 [100/938]
loss: 0.677991 [200/938]
loss: 0.550141 [300/938]
loss: 0.226565 [400/938]
loss: 0.314738 [500/938]
loss: 0.298739 [600/938]
loss: 0.459540 [700/938]
loss: 0.332978 [800/938]
loss: 0.406709 [900/938]
Test:
Accuracy: 90.2%, Avg loss: 0.334828
Epoch 3
-------------------------------
loss: 0.461890 [ 0/938]
loss: 0.242303 [100/938]
loss: 0.281414 [200/938]
loss: 0.207835 [300/938]
loss: 0.206000 [400/938]
loss: 0.409646 [500/938]
loss: 0.193608 [600/938]
loss: 0.217575 [700/938]
loss: 0.212817 [800/938]
loss: 0.202862 [900/938]
Test:
Accuracy: 91.9%, Avg loss: 0.280962
Done!
For the detailed information, see Train.
Saving a Model
After the model is trained, its parameters need to be saved.
# Save checkpoint
mindspore.save_checkpoint(model, "model.ckpt")
print("Saved Model to model.ckpt")
Saved Model to model.ckpt
Loading a Model
There are two steps to load the saved weights:
Reinstantiate the model object and construct the model.
Load the model parameters and load them onto the model.
# Instantiate a random initialized model
model = Network()
# Load checkpoint and load parameter to model
param_dict = mindspore.load_checkpoint("model.ckpt")
param_not_load, _ = mindspore.load_param_into_net(model, param_dict)
print(param_not_load)
[]
param_not_loadis an unloaded parameter list. When the list is empty, it means all parameters are loaded successfully.
The loaded model can be used directly for predictive inference.
model.set_train(False)
for data, label in test_dataset:
pred = model(data)
predicted = pred.argmax(1)
print(f'Predicted: "{predicted[:10]}", Actual: "{label[:10]}"')
break
Predicted: "[3 9 6 1 6 7 4 5 2 2]", Actual: "[3 9 6 1 6 7 4 5 2 2]"
For more detailed information, see Save and Load.