Environment : macOS 10.12.4
matplotlib==2.0.0
numpy==1.12.1
pandas==0.19.2
scikit-learn==0.18.1
scipy==0.19.0
six==1.10.0
sklearn==0.18.1
coremltools==0.3.0
protobuf==3.3.0
Upgrade the pip and install the following python packages
- shellscript.sh Select all
pip install --upgrade pip
sudo -H pip install numpy scipy matplotlib pandas sklearn coremltools protobuf
Convert to Core ML Run the following python code to show machine learning in python step by step and finally generate iris_lr.mlmodel
- iris_learn.py Select all
#!/usr/bin/env python
# Check the versions of libraries
# Python version
import sys
print('Python: {}'.format(sys.version))
# scipy
import scipy
print('scipy: {}'.format(scipy.__version__))
# numpy
import numpy
print('numpy: {}'.format(numpy.__version__))
# matplotlib
import matplotlib
print('matplotlib: {}'.format(matplotlib.__version__))
# pandas
import pandas
print('pandas: {}'.format(pandas.__version__))
# scikit-learn
import sklearn
print('sklearn: {}'.format(sklearn.__version__))
# Load libraries
import pandas
from pandas.tools.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
# Load dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']
dataset = pandas.read_csv(url, names=names)
# shape
print(dataset.shape)
# head
print(dataset.head(20))
# descriptions
print(dataset.describe())
# class distribution
print(dataset.groupby('class').size())
# box and whisker plots
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
plt.suptitle("Box and Whisker Plots for inputs")
plt.show()
# histograms
dataset.hist()
plt.suptitle('Histograms for inputs')
plt.show()
# scatter plot matrix
scatter_matrix(dataset)
plt.suptitle('Scatter Plot Matrix for inputs')
plt.show()
# Split-out validation dataset
array = dataset.values
X = array[:,0:4]
Y = array[:,4]
validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
# Test options and evaluation metric
seed = 7
scoring = 'accuracy'
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
# Compare Algorithms
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
# Make predictions on validation dataset
knn = KNeighborsClassifier()
knn.fit(X_train, Y_train)
predictions = knn.predict(X_validation)
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))
print("Make predictions on LogisticRegression Model")
model = LogisticRegression()
model.fit(X_train, Y_train)
predictions = model.predict(X_validation)
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))
# print prediction results on test data
for i, prediction in enumerate(predictions):
print 'Predicted: %s, Target: %s %s' % (prediction, Y_validation[i], '' if prediction==Y_validation[i] else '(WRONG!!!)')
#convert and save scikit.learn model
#support LogisticRegression of scikit.learn
print("Convert LogisticRegression Model to coreml model")
import coremltools
coreml_model = coremltools.converters.sklearn.convert(model,
["sepal-length", "sepal-width", "petal-length", "petal-width"],
"class")
#set model metadata
coreml_model.author = 'Author'
coreml_model.license = 'BSD'
coreml_model.short_description = 'LogisticRegression on Iris flower data set'
#set features description manually
coreml_model.input_description['sepal-length'] = 'Sepal Length in centimetres'
coreml_model.input_description['sepal-width'] = 'Sepal Width in centimetres'
coreml_model.input_description['petal-length'] = 'Petal Length in centimetres'
coreml_model.input_description['petal-width'] = 'Petal Width in centimetres'
#set the ouput description
coreml_model.output_description['class'] = 'Distinguish the species'
#save the model
coreml_model.save('iris_lr.mlmodel')
from coremltools.models import MLModel
model = MLModel('iris_lr.mlmodel')
#get the spec of the model
print(model.get_spec())
Download Xcode 9 beta and the sample code from Apple
https://docs-assets.developer.apple.com/published/51ff0c1668/IntegratingaCoreMLModelintoYourApp.zip
Modify it and add the model to the xcode project
Try the new refactoring tool in Xcode 9. It is amazing.
Train data using Neural Network Model Keras
Reference : http://machinelearningmastery.com/5-step-life-cycle-neural-network-models-keras/
- shellscript.sh Select all
# download training data
curl -O http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data
# install and activate virtual environment and install necessary python packages
# use deactivate to stop the python virtual env
sudo -H pip install --upgrade virtualenv
virtualenv --system-site-packages ~/tensorflow
source ~/tensorflow/bin/activate
# macOS, CPU only non-optimised, Python 2.7:
# https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.1.0-py2-none-any.whl
# macOS, GPU enabled, Python 2.7:
# https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.1.0-py2-none-any.whl
# or find optimised wheel files from the community https://github.com/yaroslavvb/tensorflow-community-wheels/issues
# this optimised one (SSE4.1,SSE4.2,AVX,AVX2,FMA) works for Python 2.7 macOS 10.12 Tensoflow 1.1.0 CPU https://github.com/fdalvi/tensorflow-builds
# this one works for GeForce GT 650M GPU and CPU (SSE4.2, AVX) and CUDA 8.0, and cuDNN v5.1 https://github.com/bodak/tensorflow-wheels/releases/tag/v1.1.0_27
# instruction to build your own python package https://ctmakro.github.io/site/on_learning/tf1c.html
# suppose, install the official non-optimised wheel file as below
pip install --upgrade https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.1.0-py2-none-any.whl
pip install coremltools protobuf
pip install keras==1.2.2 h5py
Convert to Core ML Run the following python code in virtual environment (tensorflow) to generate pima_keras.mlmodel
- keras_learn.py Select all
#!/usr/bin/env python
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# load pima indians dataset
#dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
dataset = numpy.loadtxt("pima-indians-diabetes.data", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
#model.fit(X, Y, epochs=150, batch_size=10)
model.fit(X, Y, 10, 150) # parameters change to keras 1.2.2
# evaluate the model
scores = model.evaluate(X, Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
#convert and save keras model
model.save('pima.h5')
print("Convert Model to coreml model")
import coremltools
coreml_model = coremltools.converters.keras.convert('pima.h5')
#set model metadata
coreml_model.author = 'Author'
coreml_model.license = 'BSD'
coreml_model.short_description = 'pima-indians-diabetes'
#save the model
coreml_model.save('pima_keras.mlmodel')
from coremltools.models import MLModel
mlmodel = MLModel('pima_keras.mlmodel')
#get the spec of the model
print(mlmodel.get_spec())
Note: coremltools require python 2.7 (not for 3.x) and supports keras==1.2.2 with Tenorflow (1.0.x, 1.1.x) only. Tenorflow_gpu requires Nvidia Cuda 8.0 and cuDNN v5.1 (which also requires macOS 10.11/10.12) but recent models of Mac are all bundled AMD GPUs. Unless you could get an old Mac Pro with upgraded Nvidia GPU with at least 4 GB of video RAM, it is better to stay with Mac CPU i7 or get a Linux machine for data training purpose only.
Hardware reference for Linux : https://www.oreilly.com/learning/build-a-super-fast-deep-learning-machine-for-under-1000
For Windows PC, tensorflow/tensorflow_gpu is only available for Python 3.5 and 64 bits only as below. As current coremltools keras convertors are not compatible with python 3.5, so direct conversion is not available in PC yet.
https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.1.0-cp35-cp35m-win_amd64.whl
https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.1.0-cp35-cp35m-win_amd64.whl
keras-inception-test Run the following python code in virtual environment (tensorflow) to test Keras Inceptionv3 model. This will download the trained Inception V3 weights from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/inception_v3_weights_tf_dim_ordering_tf_kernels.h5
- shellscript.sh Select all
git clone git://github.com/vml-ffleschner/coremltools-keras-inception-test
cd coremltools-keras-inception-test/
# based on the created virtualenv in ~/tensorflow as above
source ~/tensorflow/bin/activate
# additional installation of packages
pip install olefile pillow
#Add
coreml_model.author = 'Author'
coreml_model.license = 'BSD'
coreml_model.short_description = 'Image InceptionV3 model'
coreml_model.save('Inceptionv3.mlmodel')
print("CoreML model file Created")
#After
#print("CoreML Converted")
#in playground.py
# note : coreml_model.predict requires macOS 10.13 High Sierra
python playground.py
Install tensorflow 1.1.0 library for Java is here
- shellscript.sh Select all
curl -O https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.1.0.jar
curl -O https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-darwin-x86_64-1.1.0.tar.gz
# install
tar xzvf libtensorflow_jni-cpu-darwin-x86_64-1.1.0.tar.gz -C ./jni
# compile and run HelloTF
javac -cp libtensorflow-1.1.0.jar HelloTF.java
java -cp libtensorflow-1.1.0.jar:. -Djava.library.path=./jni HelloTF
