IT Lab艾鍗學院技術Blog: Machine Learning

2020年9月2日星期三

Python Open GL及RL 套件安裝

進行下列軟體安裝 ( 底下安裝指令在Ubuntu 18.04的測試過,皆可順利安裝)

若沒有Linux 環境怎麼辦? ==> 請參考使用Google Compute Engine 建置Linux環境

得先確認 python版本須為3.6以後 (事實上, Ubuntu 18.04 所安裝的Python己經是3.6以後的版本),

執行 python3 -V 會顯示python的版本 ==> Python 3.6.9

開始安裝套件

[Install pip3]

sudo apt-get install python3-pip

[Install PyOpenGL]

sudo apt-get install -y python-opengl

[Install RL SDK]

sudo pip3 install pybullet

sudo pip3 install gym

執行 Hello PyBullet World 範例

可用SFTP 將測試檔案test.py 上傳Ubuntu 主機或直接在主機上新增程式

新增一個純文字檔案, 將下列代碼複製貼上, 並存為test.py

[test.py 來源https://docs.google.com/document/d/10sXEhzFRSnvFcl3XxNGhnD4N2SedqwdAvK3dsihxVUA/edit#

執行python3 test.py , 終端機上會出現底下畫面

會開啓Open GL所顯示的3D畫面視窗, 恭喜你環境建置成功啦

[References]

https://github.com/bulletphysics/bullet3/releases

https://gym.openai.com/docs/#installation

2017年11月17日星期五

CNN using keras

傳統 DNN, Layer 是一個平面的

在CNN中, 每一個Layer是一個Cube

A ConvNet arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a ConvNet transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).

An example input volume in red (e.g. a 32x32x3 CIFAR-10 image), and an example volume of neurons in the first Convolutional layer. Each neuron in the convolutional layer is connected only to a local region in the input volume spatially, but to the full depth (i.e. all color channels). Note, there are multiple neurons (5 in this example) along the depth, all looking at the same region in the input - see discussion of depth columns in text below

he neurons from the Neural Network chapter remain unchanged: They still compute a dot product of their weights with the input followed by a non-linearity, but their connectivity is now restricted to be local spatially.

Pooling layer downsamples the volume spatially, independently in each depth slice of the input volume. Left: In this example, the input volume of size [224x224x64] is pooled with filter size 2, stride 2 into output volume of size [112x112x64]. Notice that the volume depth is preserved. Right: The most common downsampling operation is max, giving rise to max pooling, here shown with a stride of 2. That is, each max is taken over 4 numbers (little 2x2 square).

如何計算參數量

# The data, shuffled and split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print('y_train shape:', y_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(2, (5, 5), padding='same', input_shape=x_train.shape[1:]))
print('input_shape:',x_train.shape[1:])
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))

# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)

# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
model.summary()

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_46 (Conv2D)           (None, 32, 32, 2)         152  ((5*5*3)+1)*2=152
                                  其他位置也都是共用這152參數,所以不是圖愈大參數愈多
                                  +1: bias 項, WX+b

______________________________________________________________

activation_67 (Activation)   (None, 32, 32, 2)         0         
_________________________________________________________________
conv2d_47 (Conv2D)           (None, 30, 30, 32)        608  ((3*3*2)+1)*32=608

_________________________________________________________________
activation_68 (Activation)   (None, 30, 30, 32)        0         
_________________________________________________________________
max_pooling2d_23 (MaxPooling (None, 15, 15, 32)        0         
_________________________________________________________________
dropout_34 (Dropout)         (None, 15, 15, 32)        0         
_________________________________________________________________
conv2d_48 (Conv2D)           (None, 15, 15, 64)        18496     
_________________________________________________________________
activation_69 (Activation)   (None, 15, 15, 64)        0         
_________________________________________________________________
conv2d_49 (Conv2D)           (None, 13, 13, 64)        36928     
_________________________________________________________________
activation_70 (Activation)   (None, 13, 13, 64)        0         
_________________________________________________________________
max_pooling2d_24 (MaxPooling (None, 6, 6, 64)          0         
_________________________________________________________________
dropout_35 (Dropout)         (None, 6, 6, 64)          0         
_________________________________________________________________
flatten_12 (Flatten)         (None, 2304)              0         
_________________________________________________________________
dense_23 (Dense)             (None, 512)               1180160   
_________________________________________________________________
activation_71 (Activation)   (None, 512)               0         
_________________________________________________________________
dropout_36 (Dropout)         (None, 512)               0         
_________________________________________________________________
dense_24 (Dense)             (None, 10)                5130      
_________________________________________________________________
activation_72 (Activation)   (None, 10)                0         
=================================================================
Total params: 1,241,474
Trainable params: 1,241,474
Non-trainable params: 0
_________________________________________________________________