create_model.py

from config.global_parameters import default_model_name
from utils import load_pkl
import numpy as np
from model_utils import spatial_model



def train_classifier(genres=['romance', 'horror', 'action'], model_name=default_model_name):
    
    """Gather data for selected genres"""
    trainingData = []
    trainingLabels = []
#    num_of_random_frames = 75
    num_of_classes = len(genres)
    print "Number of classes:",num_of_classes
    for genreIndex, genre in enumerate(genres):
#        print "Looking for pickle file: data/{0}{1}.p".format(genre, str(num_of_videos)),
        try:
            genreFeatures = load_pkl(genre+"_ultimate_"+default_model_name)
            genreFeatures = np.array([np.array(f) for f in genreFeatures]) # numpy hack
        except Exception as e:
            print e
            return
        print "OK."
        for videoFeatures in genreFeatures:
            """to get all frames from a video -- hacky"""
            randomIndices = range(len(videoFeatures))
            selectedFeatures = np.array(videoFeatures[randomIndices])
            for feature in selectedFeatures:
                trainingData.append(feature)
                trainingLabels.append([genreIndex])
    trainingData = np.array(trainingData)
    trainingLabels = np.array(trainingLabels)
    print trainingData.shape 
    print trainingLabels.shape
#    trainingLabels = to_categorical(trainingLabels, num_of_classes)
    print trainingLabels
#    trainingLabels = trainingLabels.reshape((-1,num_of_classes))

    """Initialize the mode"""
    model = spatial_model(num_of_classes)
    model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

   
    """Start training"""
    batch_size = 32
    nb_epoch = 100 

    model.fit(trainingData, trainingLabels, batch_size=batch_size, nb_epoch=nb_epoch)#, callbacks=[remote])
    modelOutPath ='data/models/spatial'+model_name+'_'+str(num_of_classes)+"g_bs"+str(batch_size)+"_ep"+str(nb_epoch)+".h5"
    model.save(modelOutPath)
    print "Model saved at",modelOutPath
 

if __name__=="__main__":

    train_classifier(genres=['action','drama','fantasy','horror','romance'])