4train.py

# organize imports
from __future__ import print_function

from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
import numpy as np
import h5py
import os
import json
import pickle
import seaborn as sns
import matplotlib.pyplot as plt

# load the user configs
with open('conf.json') as f:    
  config = json.load(f)

# config variables
test_size     = config["test_size"]
seed      = config["seed"]
features_path   = config["features_path"]
labels_path   = config["labels_path"]
classifier_path = config["classifier_path"]
train_path    = config["train_path"]
num_classes   = config["num_classes"]
classifier_path = config["classifier_path"]

# import features and labels
h5f_data  = h5py.File(features_path, 'r')
h5f_label = h5py.File(labels_path, 'r')

features_string = h5f_data['dataset_1']
labels_string   = h5f_label['dataset_1']

features = np.array(features_string)
labels   = np.array(labels_string)

h5f_data.close()
h5f_label.close()

# verify the shape of features and labels
print("[INFO] features shape: {}".format(features.shape))
print("[INFO] labels shape: {}".format(labels.shape))

print("[INFO] training started...")
# split the training and testing data
(trainData, testData, trainLabels, testLabels) = train_test_split(np.array(features),
                                                                  np.array(labels),
                                                                  test_size=test_size,
                                                                  random_state=seed)

print("[INFO] splitted train and test data...")
print("[INFO] train data  : {}".format(trainData.shape))
print("[INFO] test data   : {}".format(testData.shape))
print("[INFO] train labels: {}".format(trainLabels.shape))
print("[INFO] test labels : {}".format(testLabels.shape))

# use logistic regression as the model
print("[INFO] creating model...")
model = LogisticRegression(random_state=seed)
model.fit(trainData, trainLabels)

# use rank-1 and rank-5 predictions
print("[INFO] evaluating model...")

# loop over test data
for (label, features) in zip(testLabels, testData):
  # predict the probability of each class label and
  # take the top-5 class labels
  predictions = model.predict_proba(np.atleast_2d(features))[0]
  predictions = np.argsort(predictions)[::-1][:5]

# evaluate the model of test data
preds = model.predict(testData)

# dump classifier to file
print("[INFO] saving model...")
pickle.dump(model, open(classifier_path, 'wb'))

# display the confusion matrix
print("[INFO] confusion matrix")

# get the list of training lables
labels = sorted(list(os.listdir(train_path)))