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Commit e41ca86b authored by Jean-Luc Parouty Jean-Luc.Parouty@simap.grenoble-inp.fr's avatar Jean-Luc Parouty Jean-Luc.Parouty@simap.grenoble-inp.fr
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GTSRB/02.1-First-convolutions.ipynb
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%% Cell type:markdown id: tags:
German Traffic Sign Recognition Benchmark (GTSRB)
=================================================
---
Introduction au Deep Learning (IDLE) - S. Aria, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
## Episode 2 : First Convolutions
Our main steps:
- Read dataset
- Build a model
- Train the model
- Model evaluation
## 1/ Import and init
%% Cell type:code id: tags:
``` python
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.callbacks import TensorBoard
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import time
import idle.pwk as ooo
ooo.init()
```
%% Output
IDLE 2020 - Practical Work Module
Version : 0.1.1
Run time : Sunday 5 January 2020, 20:14:03
Run time : Monday 6 January 2020, 14:35:14
Matplotlib style : idle/talk.mplstyle
TensorFlow version : 2.0.0
Keras version : 2.2.4-tf
%% Cell type:markdown id: tags:
## 2/ Reload dataset (RGB25)
Dataset is one of the saved dataset: RGB25, RGB35, L25, L35, etc.
First of all, we're going to use the dataset : **L25**
(with a GPU, it only takes 35'' compared to more than 5' with a CPU !)
%% Cell type:code id: tags:
``` python
%%time
dataset ='RGB25'
img_lx = 25
img_ly = 25
img_lz = 3
# ---- Read dataset
x_train = np.load('./data/{}/x_train.npy'.format(dataset))
y_train = np.load('./data/{}/y_train.npy'.format(dataset))
x_test = np.load('./data/{}/x_test.npy'.format(dataset))
y_test = np.load('./data/{}/y_test.npy'.format(dataset))
# ---- Reshape data
x_train = x_train.reshape( x_train.shape[0], img_lx, img_ly, img_lz)
x_test = x_test.reshape( x_test.shape[0], img_lx, img_ly, img_lz)
input_shape = (img_lx, img_ly, img_lz)
print("Dataset loaded, size={:.1f} Mo\n".format(ooo.get_directory_size('./data/'+dataset)))
```
%% Output
Dataset loaded, size=742.0 Mo
CPU times: user 0 ns, sys: 824 ms, total: 824 ms
Wall time: 3.38 s
CPU times: user 0 ns, sys: 708 ms, total: 708 ms
Wall time: 6.07 s
%% Cell type:markdown id: tags:
## 3/ Have a look to the dataset
Note: Data must be reshape for matplotlib
%% Cell type:code id: tags:
``` python
print("x_train : ", x_train.shape)
print("y_train : ", y_train.shape)
print("x_test : ", x_test.shape)
print("y_test : ", y_test.shape)
if img_lz>1:
ooo.plot_images(x_train.reshape(-1,img_lx,img_ly,img_lz), y_train, range(6), columns=3, x_size=4, y_size=3)
ooo.plot_images(x_train.reshape(-1,img_lx,img_ly,img_lz), y_train, range(36), columns=12, x_size=1, y_size=1)
else:
ooo.plot_images(x_train.reshape(-1,img_lx,img_ly), y_train, range(6), columns=6, x_size=2, y_size=2)
ooo.plot_images(x_train.reshape(-1,img_lx,img_ly), y_train, range(36), columns=12, x_size=1, y_size=1)
```
%% Output
x_train : (39209, 25, 25, 3)
y_train : (39209,)
x_test : (12630, 25, 25, 3)
y_test : (12630,)
%% Cell type:markdown id: tags:
## 4/ Create model
%% Cell type:code id: tags:
``` python
batch_size = 128
num_classes = 43
epochs = 10
epochs = 5
```
%% Cell type:code id: tags:
``` python
keras.backend.clear_session()
model = keras.models.Sequential()
model.add( keras.layers.Conv2D(96, (3,3), activation='relu', input_shape=(img_lx, img_ly, img_lz)))
model.add( keras.layers.MaxPooling2D((2, 2)))
model.add( keras.layers.Conv2D(192, (3, 3), activation='relu'))
model.add( keras.layers.MaxPooling2D((2, 2)))
model.add( keras.layers.Flatten())
model.add( keras.layers.Dense(3072, activation='relu'))
model.add( keras.layers.Dense(500, activation='relu'))
model.add( keras.layers.Dense(43, activation='softmax'))
model.summary()
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
```
%% Output
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 23, 23, 96) 2688
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 11, 11, 96) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 9, 9, 192) 166080
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 4, 4, 192) 0
_________________________________________________________________
flatten (Flatten) (None, 3072) 0
_________________________________________________________________
dense (Dense) (None, 3072) 9440256
_________________________________________________________________
dense_1 (Dense) (None, 500) 1536500
_________________________________________________________________
dense_2 (Dense) (None, 43) 21543
=================================================================
Total params: 11,167,067
Trainable params: 11,167,067
Non-trainable params: 0
_________________________________________________________________
%% Cell type:markdown id: tags:
## 5/ Run model
%% Cell type:code id: tags:
``` python
%%time
history = model.fit( x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
```
%% Output
Train on 39209 samples, validate on 12630 samples
Epoch 1/10
39209/39209 [==============================] - 4s 92us/sample - loss: 0.9183 - accuracy: 0.7400 - val_loss: 0.3855 - val_accuracy: 0.9032
Epoch 2/10
39209/39209 [==============================] - 3s 77us/sample - loss: 0.0895 - accuracy: 0.9770 - val_loss: 0.3091 - val_accuracy: 0.9251
Epoch 3/10
39209/39209 [==============================] - 3s 78us/sample - loss: 0.0400 - accuracy: 0.9895 - val_loss: 0.2527 - val_accuracy: 0.9367
Epoch 4/10
39209/39209 [==============================] - 3s 78us/sample - loss: 0.0278 - accuracy: 0.9922 - val_loss: 0.2833 - val_accuracy: 0.9399
Epoch 5/10
39209/39209 [==============================] - 3s 77us/sample - loss: 0.0182 - accuracy: 0.9952 - val_loss: 0.2724 - val_accuracy: 0.9473
Epoch 6/10
39209/39209 [==============================] - 3s 75us/sample - loss: 0.0132 - accuracy: 0.9963 - val_loss: 0.2321 - val_accuracy: 0.9469
Epoch 7/10
39209/39209 [==============================] - 3s 77us/sample - loss: 0.0198 - accuracy: 0.9944 - val_loss: 0.2921 - val_accuracy: 0.9390
Epoch 8/10
39209/39209 [==============================] - 3s 78us/sample - loss: 0.0169 - accuracy: 0.9958 - val_loss: 0.2827 - val_accuracy: 0.9444
Epoch 9/10
39209/39209 [==============================] - 3s 78us/sample - loss: 0.0217 - accuracy: 0.9939 - val_loss: 0.2007 - val_accuracy: 0.9565
Epoch 10/10
39209/39209 [==============================] - 3s 78us/sample - loss: 0.0119 - accuracy: 0.9968 - val_loss: 0.1834 - val_accuracy: 0.9628
CPU times: user 43.7 s, sys: 4.45 s, total: 48.2 s
Wall time: 30.9 s
Epoch 1/5
39209/39209 [==============================] - 18s 468us/sample - loss: 0.9595 - accuracy: 0.7357 - val_loss: 0.4068 - val_accuracy: 0.9015
Epoch 2/5
39209/39209 [==============================] - 16s 417us/sample - loss: 0.0876 - accuracy: 0.9770 - val_loss: 0.3472 - val_accuracy: 0.9190
Epoch 3/5
39209/39209 [==============================] - 16s 409us/sample - loss: 0.0375 - accuracy: 0.9900 - val_loss: 0.2917 - val_accuracy: 0.9363
Epoch 4/5
39209/39209 [==============================] - 17s 421us/sample - loss: 0.0263 - accuracy: 0.9928 - val_loss: 0.3384 - val_accuracy: 0.9284
Epoch 5/5
39209/39209 [==============================] - 17s 421us/sample - loss: 0.0237 - accuracy: 0.9929 - val_loss: 0.3022 - val_accuracy: 0.9433
CPU times: user 16min 31s, sys: 1min 27s, total: 17min 59s
Wall time: 1min 23s
%% Cell type:markdown id: tags:
## 6/ Evaluation
%% Cell type:code id: tags:
``` python
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss : {:5.4f}'.format(score[0]))
print('Test accuracy : {:5.4f}'.format(score[1]))
```
%% Output
Test loss : 0.1834
Test accuracy : 0.9628
Test loss : 0.3022
Test accuracy : 0.9433
%% Cell type:markdown id: tags:
---
### Results :
```
L25 : size=250 Mo 93.15%
RGB25 : size=740 Mo 95.42%
RGB25 : size=740 Mo 94.33%
...
```
%% Cell type:code id: tags:
``` python
```
......
GTSRB/03-Tracking-and-visualizing.ipynb 0 → 100644
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