0

Mesh data of 3D model: I/O and handling in Python 3

pyntcloud is a package to work with 3D point cloud in Python 3.
You can install using anaconda as:
pip install -i https://pypi.anaconda.org/pypi/simple laspy
pip install git+https://github.com/daavoo/pyntcloud

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0

tensorflow-gpu 1.4.0 + keras 2.1.1 on Ubuntu 16.04

Tensorflow 1.4.0 binary with CUDA 8 and cuDNN 6
Tensorflow 1.5.0 binary will be with CUDA 9 and cuDNN 7

The following procedure assumes that you use anaconda for python package manager.

  1. prepare for driver install
    1. Delete installed drivers by sudo apt purge nvidia*
    2. Press ctrl + alt + F1 and Login
    3. Stop X server by sudo service lightdm stop
    4. Disable the Nouveau kernel driver by creating a new file /etc/modprobe.d/blacklist-nouveau.conf
    5. The file contents is as follows

      blacklist nouveau
      blacklist lbm-nouveau
      options nouveau modeset=0
      alias nouveau off
      alias lbm-nouveau off
    6. Then, create another file by echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf
    7. Update by sudo update-initramfs -u
    8. Then reboot by sudo reboot
  2. install driver
    1. Download the driver installer from here, say NVIDIA-***.run.
    2. Change the permission by sudo chmod +x NVIDIA-***.run
    3. Execute the installer by sudo ./NVIDIA-***.run
    4. Let’s confirm the installation by nvidia-smi
  3. install CUDA toolkit
    1. Download the toolkit installer from here, say cuda_***.run.
    2. Change the permission by sudo chmod +x cuda_***.run
    3. Install the toolkit by sudo ./cuda_***.run –toolkit –silent
    4. Install the samples by sudo ./cuda_***.run –samples –silent
    5. Add the following lines to the .bashrc

      export PATH="$PATH:/usr/local/cuda/bin"

      export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"


      export CUDA_HOME=/usr/local/cuda

    6. Update by source ~/.bashrc
  4. install cuDNN
    1. Download packed file from here, say cudnn-***.tgz
    2. Untar the file by tar -xzvf cudnn-***.tgz
    3. Copy header and lib files by
      1. sudo cp cuda/lib64/* /usr/local/cuda/lib64/
      2. sudo cp cuda/include/cudnn.h /usr/local/cuda/include/

     

  5. install Tensorflow
    1. install by pip install tensorflow-gpu
  6. install Keras
    1. install by pip install keras
0

Keras: MNIST classification

Keras implementation for MNIST classification with batch normalization and leaky ReLU.


import numpy as np
import time
import pandas as pd
import matplotlib.pyplot as plt

from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, LeakyReLU, Activation, Flatten
from keras.optimizers import Adam
from keras.layers.normalization import BatchNormalization
from keras.utils import np_utils
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import plot_model
from keras.backend import set_learning_phase, learning_phase

def build_classifier(input_shape, num_classes, num_pooling):
"""
input_shape = (image_width, image_height, 1)
"""
# settings
kernel_size = (3, 3)
pool_size = (2, 2)
num_featuremaps = 32
size_featurevector = 1024

# Three steps to create a CNN
# 1. Convolution
# 2. Activation
# 3. Pooling
# Repeat Steps 1,2,3 for adding more hidden layers

# 4. After that make a fully connected network
# This fully connected network gives ability to the CNN
# to classify the samples

model = Sequential()

# add convolution blocks num_pooling times for featuremap extraction
for block in range(num_pooling):
if block == 0:
model.add(Conv2D(num_featuremaps, kernel_size=kernel_size, padding='same', activation='linear', input_shape=input_shape))
else:
num_featuremaps *= 2
model.add(Conv2D(num_featuremaps, kernel_size=kernel_size, padding='same', activation='linear'))
model.add(BatchNormalization())
model.add(LeakyReLU(0.2))

model.add(Conv2D(num_featuremaps, kernel_size=kernel_size, padding='same', activation='linear'))
model.add(BatchNormalization())
model.add(LeakyReLU(0.2))

model.add(MaxPooling2D(pool_size=pool_size))

model.add(Flatten())

# add fully connected layers for classification
for block in range(num_pooling):
model.add(Dense(size_featurevector, activation='linear'))
model.add(LeakyReLU(alpha=0.2))
model.add(BatchNormalization())
model.add(Dropout(0.5))

model.add(Dense(num_classes))

model.add(Activation('softmax'))

return model

 

def demo_load_model():
num_pooling = 3
input_shapes = [(64, 64, 1), (128, 128, 1), (256, 256, 1)]
for input_shape in input_shapes:
model = build_pointset_classifier(input_shape,
100,
num_pooling)

filename = 'pointset_classifier_%s.png' % str(input_shape[0])
plot_model(model,
to_file=filename,
show_shapes=True)

 

if __name__ == '__main__':
""" settings
"""
img_width = 28
img_height = 28
input_shape = (img_width, img_height, 1)
num_classes = 10
num_epoch = 3
size_batch = 100

""" load data
"""
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(x_train.shape[0], img_width, img_height, 1)
x_train = x_train.astype('float32')
x_train /= 255.0
y_train = np_utils.to_categorical(y_train, num_classes)

x_test = x_test.reshape(x_test.shape[0], img_width, img_height, 1)
x_test = x_test.astype('float32')
x_test /= 255.0
y_test = np_utils.to_categorical(y_test, num_classes)

""" build neural network
"""
num_pooling = 2
set_learning_phase(1)
model = build_classifier(input_shape,
num_classes,
num_pooling)

filename = 'classifier_%s.png' % str(input_shape[0])
plot_model(model,
to_file=filename,
show_shapes=True)

model.compile(loss='categorical_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
model.summary()
""" train the model
"""
time_begin = time.clock()
history = model.fit(x_train, y_train,
validation_data=(x_test, y_test),
epochs=num_epoch,
batch_size=size_batch,
verbose=1)
print('Time elapsed: %.0f' % (time.clock() - time_begin))

""" valid the model
"""
set_learning_phase(0)
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
0

Keras: error at the beginning of fit with Dropout

When Keras’ model contains Dropout component in it, we must tell the model whether we are training or test mode because the network behaves differently. The solution is mentioned in github issue and Keras docs.

 

from keras.backend import set_learning_phase
...
set_learning_phase(1)
model = ...
model.compile(...)
model.summary()
model.fit(...)
set_learning_phase(0)
model.predict()
0

Keras: plot_model returns pydot and graphviz related error on Windows

plot_model of Keras installed on Windows may return pydot and graphviz related error like “Graphviz’s executables are not found’. The error cause is that the computer does not know the path of Graphviz’s executable exactly mentioned as the error message. The solution is explained here.

  1. Install GraphViz software not python module
  2. add GraphViz’s bin directory to system path
  3. install graphviz like conda install graphviz
  4. install pydot like pip install git+https://github.com/nlhepler/pydot.git

 

0

TensorFlow’s DLL import error on windows

We need the appropriate cuDNN and this kind of import error occurred by missing the right version.

See this release note. TensorFlow 1.3.0 (and other prebuilt binaries) have been built with cuDNN 6 and TensorFlow 1.4 with cuDNN 7.

cuDNN installer are downloadable from here. On 2017.08.30, they provide the following versions.

  • cuDNN 7.0 for CUDA 9.0 RC
  • cuDNN 7.0 for CUDA 8.0
  • cuDNN 6.0 for CUDA 8.0
  • cuDNN 6.0 for CUDA 7.5
  • cuDNN 5.1 for CUDA 8.0
  • cuDNN 5.1 for CUDA 7.5