Commit 488b642e authored by Roberto Ugolotti's avatar Roberto Ugolotti
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Add ML cookbook

parent 27347bb7
This repository contains configuration files or data that can be of interest to personalise some of the JEODPP services
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# Content
This repository contains files or data that can be of interest to run and personalise JEODPP services.
## jeodpp-text-terminal-service
This folder contains configurations file for `screen`.
## ml-cookbook
This folder contains some examples of using deep learning libraries inside Jupyter Notebooks.
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# Byte-compiled / optimized / DLL files
# C extensions
# Distribution / packaging
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
# Installer logs
# Unit test / coverage reports
# Translations
# Jupyter Notebooks
# Image Classification
This folder contains notebooks that perform image classification using PyTorch, Keras, and MXNet.
## How to run the examples
To run the script in [JeoLab]( copy a notebook and the zip file containing data (``) in your folder and launch it with an environment in which the library needed is installed (see [available environments](
It will automatically extract the data contained in `` and train a simple Convolutional Neural Network to distinguish between satellite images of forestal and industrial areas.
Each notebook contains some references to the documentation of the package used.
## How to use your own data
In case you want to use this script as a base to train your own dataset, the images must be divided into different folders according to their classes
%% Cell type:markdown id:d4beb900-5ae3-43b0-8517-c89876a895fa tags:
# Keras Example
This notebook shows a simple classification example using Keras
Code has been written starting from
Data is a selection of
%% Cell type:code id:f6284ec5-d53f-44d5-8b93-5c2229fa0496 tags:
``` python
# Example taken from
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
%% Cell type:code id:1863a3b6-f1f1-4172-b319-dacf28a91538 tags:
``` python
# Check that Tensorflow will use GPU
from tensorflow.python.client import device_lib
assert 'GPU' in str(device_lib.list_local_devices())
%% Cell type:markdown id:ec21d233-06b1-4d75-b4eb-04ff183a897c tags:
Reads data from disk. Data must be structured in this way:
%% Cell type:code id:f1b91f72-e6d8-4037-84c4-4ed824d9be1d tags:
``` python
!unzip -qo images
!rm -rf data/.ipynb_checkpoints/ # Otherwise Keras will try to read images from this directory and get the wrong number of classes
image_size = 64, 64
batch_size = 32
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
%% Cell type:markdown id:298ecb95-e700-43d2-a901-877d8417c827 tags:
Plot some images
%% Cell type:code id:1ea29621-1865-4127-b384-ad043238d220 tags:
``` python
import matplotlib.pyplot as plt
import numpy as np
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(9):
ax = plt.subplot(3, 3, i + 1)
%% Cell type:markdown id:317c35b1-a4de-447d-9e63-3959a6a6b97f tags:
Augment training data with flipping and rotation
%% Cell type:code id:f75972b2-cdd2-4fe2-9e90-cc5453a8b564 tags:
``` python
data_augmentation = keras.Sequential(
%% Cell type:markdown id:d6841041-a27c-44fe-8e07-60e842adbb9c tags:
Plot some augmented data
%% Cell type:code id:bb6ac2af-4194-400d-853c-f30f36bbcf8e tags:
``` python
plt.figure(figsize=(10, 10))
for images, _ in train_ds.take(1):
for i in range(9):
augmented_images = data_augmentation(images)
ax = plt.subplot(3, 3, i + 1)
%% Cell type:markdown id:c43e5b56-f98f-4e46-a2db-2aaa5aa4dc57 tags:
Create a deep network for classification. It contains the level of data_augmentation created before, a rescaling layer, and five convolutional layers, followed by the softmax layer used for classification.
%% Cell type:code id:dd137900-44bd-4536-bb3b-ecd0602fc9c3 tags:
``` python
def make_model(input_shape, num_classes):
inputs = keras.Input(shape=input_shape)
# Image augmentation block
x = data_augmentation(inputs)
# Entry block
x = layers.experimental.preprocessing.Rescaling(1.0 / 255)(x)
x = layers.Conv2D(32, 3, strides=2, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.Conv2D(64, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
previous_block_activation = x # Set aside residual
for size in [128, 256, 512, 728]:
x = layers.Activation("relu")(x)
x = layers.SeparableConv2D(size, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
# Project residual
residual = layers.Conv2D(size, 1, strides=2, padding="same")(
x = layers.add([x, residual]) # Add back residual
previous_block_activation = x # Set aside next residual
x = layers.SeparableConv2D(1024, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.GlobalAveragePooling2D()(x)
activation = "softmax"
units = num_classes
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(units, activation=activation)(x)
return keras.Model(inputs, outputs)
model = make_model(input_shape=image_size + (3,), num_classes=2)
# keras.utils.plot_model(model, show_shapes=True) # Needed pydot for this
%% Cell type:code id:fcb0538a-7f79-450a-8f65-a45967561704 tags:
``` python
epochs = 10
train_ds, epochs=epochs, validation_data=val_ds,
%% Cell type:code id:c37c970d-b1d7-42d6-a97f-0b040cf8a85e tags:
``` python
img = keras.preprocessing.image.load_img(
"images/Forest/Forest_1.jpg", target_size=image_size
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create batch axis
predictions = model.predict(img_array)
score = predictions[0]
"This image is %.1f percent Forest and %.1f percent Industrial."
% (100 * score[0], 100 * score[1])
%% Cell type:markdown id:11a99532-17de-4450-8ffa-5247496c622f tags:
# MxNet Example
This notebook shows a simple classification example using MxNet
Code has been written starting from
Data is a selection of
%% Cell type:code id:be891056-f8ed-4340-a920-87bb1bbd72f4 tags:
``` python
import mxnet as mx
from mxnet import gluon
from mxnet import autograd as ag
from mxnet.gluon import nn
from import transforms
%% Cell type:code id:b386909e-261f-4971-b0ba-1e8b5c584a02 tags:
``` python
batch_size = 32
n_classes = 2
%% Cell type:code id:59057db7-c23a-4bc1-ae9e-87624f3dd7d6 tags:
``` python
!unzip -qo
!rm -rf images/.ipynb_checkpoints/ # Otherwise gluon will try to read images from this directory and get the wrong number of classes
# Images read from disk will be converted to tensor and normalized. Data augmentation is also performed
transform_train = transforms.Compose([
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # ImageNet mean and stddev
train_data ='images').transform_first(transform_train),
batch_size=batch_size, shuffle=True, num_workers=1)
%% Cell type:markdown id:eedb5f93-7163-4652-9499-4887998a20a0 tags:
Define a Convolutional Neural Network
%% Cell type:code id:9c534007-8507-49fd-afaa-dc3e2aaffb14 tags:
``` python
net = nn.Sequential()
# Add a sequence of layers.
nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
# The dense layer will automatically reshape the 4-D output of last
# max pooling layer into the 2-D shape: (x.shape[0], x.size/x.shape[0])
nn.Dense(120, activation="relu"),
nn.Dense(84, activation="relu"),
%% Cell type:markdown id:78e13e93-75bd-466d-a138-a813618205b6 tags:
Get the GPU and inizialize the network
%% Cell type:code id:2ab8b7d8-7135-4114-991e-290bab6e1f86 tags:
``` python
assert mx.context.num_gpus()
ctx = mx.gpu(0)
%% Cell type:markdown id:9df40855-e429-424f-a877-386f2d27910c tags:
Define training parameter: optimization, loss, and metric
%% Cell type:code id:0cfbc537-f514-4933-a329-bcb0ad058945 tags:
``` python
# Nesterov accelerated gradient descent
optimizer = 'nag'
# Set parameters
optimizer_params = {'learning_rate': 0.1, 'wd': 0.0001, 'momentum': 0.9}
# Define our trainer for net
trainer = gluon.Trainer(net.collect_params(), optimizer, optimizer_params)
# Define loss and metric
loss_fn = gluon.loss.SoftmaxCrossEntropyLoss()
train_metric = mx.metric.Accuracy()
%% Cell type:markdown id:6c880933-cc88-44aa-8d61-d8c9ba1b5508 tags:
Train the network
%% Cell type:code id:74edac2b-57ef-4b93-8517-5b1c309f4117 tags:
``` python
epochs = 10
for epoch in range(epochs):
train_loss = 0
# Loop through each batch of training data
for i, batch in enumerate(train_data):
# Extract data and label
data = gluon.utils.split_and_load(batch[0], ctx_list=[ctx], batch_axis=0)
label = gluon.utils.split_and_load(batch[1], ctx_list=[ctx], batch_axis=0)
# AutoGrad
with ag.record():
output = [net(X) for X in data]
loss = [loss_fn(yhat, y) for yhat, y in zip(output, label)]
# Backpropagation
for l in loss:
# Optimize
# Update metrics
train_loss += sum([l.sum().asscalar() for l in loss])
train_metric.update(label, output)
name, acc = train_metric.get()
print('Epoch %d - accuracy on training set %.3e' % (epoch + 1, acc))
%% Cell type:markdown id:adab7ae6-fc15-4bac-aa25-506628d78abd tags:
# Torch Example
This notebook shows a simple classification example using Torch
Code has been written starting from
Data is a selection of
%% Cell type:code id:5613e36b-5d23-464f-9abb-914fdd1ecfff tags:
``` python
import torch
import torchvision
from torchvision import datasets, transforms
import numpy as np
import pylab as plt
%% Cell type:markdown id:cb030ffd-505b-4d52-a8eb-6c88443d2cde tags:
Ensure that CUDA is available and that PyTorch sees the GPU. Then, create a device object. PyTorch requires to explicitely send networks and data to GPU.
%% Cell type:code id:6bd24fe2-bfd0-4e4c-9240-28c47c74ebc6 tags:
``` python
assert torch.cuda.is_available()
assert torch.cuda.device_count() > 0
device = torch.device("cuda:0")
%% Cell type:code id:ca5f3f2b-5fcc-461e-899e-0eaaab0552bc tags:
``` python
batch_size = 8
classes = ['Forest', 'Industrial']
n_classes = len(classes)
n_epochs = 10
%% Cell type:markdown id:5d34c385-c824-42f3-9a18-3aa1f34986e6 tags:
Read data from disk. Convert it to a tensor (otherwise it will be read as a PIL image), normalize and resize.
Then data is split between training and test set.
%% Cell type:code id:c49ed5b8-41a9-418a-a431-3da3db19e233 tags:
``` python
img_transform = transforms.Compose(
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
transforms.Resize((32, 32))]
!unzip -qo
!rm -rf images/.ipynb_checkpoints/ # Otherwise PyTorch will try to read images from this directory and get the wrong number of classes
dataset = datasets.ImageFolder('images', transform=img_transform)
train_set, test_set =, [int(.9 * len(dataset)), int(.1 * len(dataset))])
train_loader =, batch_size=batch_size, shuffle=True)
test_loader =, batch_size=batch_size, shuffle=True)
train_iter = iter(train_loader)
%% Cell type:markdown id:945b0555-ab79-4cfa-ab3b-83411601f68f tags:
Show some examples of training data
%% Cell type:code id:8822e7f6-30f4-4c04-8da4-06d3aca05b6b tags:
``` python
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
images, labels =
# show images
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(batch_size)))
%% Cell type:markdown id:f5917040-e0d6-4a12-9017-91bfa1848ea4 tags:
Creates a simple Convolutional Neural Network
%% Cell type:code id:78e10cb5-2714-40d4-8957-521376ef2719 tags:
``` python
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, n_classes):
self.conv1 = nn.Conv2d(3, 36, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(36, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, n_classes)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = torch.flatten(x, 1) # flatten all dimensions except batch
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net(n_classes).to(device) # Send network to be executed on GPU
%% Cell type:markdown id:3431cb5a-4a0e-456b-ad1e-d3e3b102e519 tags:
Defines the optimizer
%% Cell type:code id:27fa5f98-9f92-43b4-a8f1-25d1662b66a0 tags:
``` python
import torch.optim as optim
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
%% Cell type:markdown id:c6fc47b1-25d9-418c-9fc4-c67ca7b4fe4b tags:
Train the network. Iterate over epochs, and over training set.
%% Cell type:code id:e5e0c966-72f5-43b9-9aa5-3d09a62e027e tags:
``` python
for epoch in range(n_epochs): # loop over the dataset multiple times
print('Epoch %d' % (epoch + 1))
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
# forward + backward + optimize
# Remember to send data to GPU
outputs = net(
loss = criterion(outputs,