Python keras.backend.set_image_dim_ordering() Examples

The following are 11 code examples of keras.backend.set_image_dim_ordering(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module keras.backend , or try the search function .
Example #1
Source File: util.py    From keras-transfer-learning-for-oxford102 with MIT License 7 votes vote down vote up 
def set_img_format():
    try:
        if K.backend() == 'theano':
            K.set_image_data_format('channels_first')
        else:
            K.set_image_data_format('channels_last')
    except AttributeError:
        if K._BACKEND == 'theano':
            K.set_image_dim_ordering('th')
        else:
            K.set_image_dim_ordering('tf')
Example #2
Source File: vgg16_bidirectional_lstm_train.py    From keras-video-classifier with MIT License 6 votes vote down vote up 
def main():
    K.set_image_dim_ordering('tf')
    sys.path.append(patch_path('..'))

    from keras_video_classifier.library.recurrent_networks import VGG16BidirectionalLSTMVideoClassifier
    from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
    from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf

    data_set_name = 'UCF-101'
    input_dir_path = patch_path('very_large_data')
    output_dir_path = patch_path('models/' + data_set_name)
    report_dir_path = patch_path('reports/' + data_set_name)

    np.random.seed(42)

    # this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
    load_ucf(input_dir_path)

    classifier = VGG16BidirectionalLSTMVideoClassifier()

    history = classifier.fit(data_dir_path=input_dir_path, model_dir_path=output_dir_path, data_set_name=data_set_name)

    plot_and_save_history(history, VGG16BidirectionalLSTMVideoClassifier.model_name,
                          report_dir_path + '/' + VGG16BidirectionalLSTMVideoClassifier.model_name + '-history.png')
Example #3
Source File: vgg16_lstm_hi_dim_train.py    From keras-video-classifier with MIT License 6 votes vote down vote up 
def main():
    K.set_image_dim_ordering('tf')
    sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

    from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
    from keras_video_classifier.library.recurrent_networks import VGG16LSTMVideoClassifier
    from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf

    data_set_name = 'UCF-101'
    input_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
    output_dir_path = os.path.join(os.path.dirname(__file__), 'models', data_set_name)
    report_dir_path = os.path.join(os.path.dirname(__file__), 'reports', data_set_name)

    np.random.seed(42)

    # this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
    load_ucf(input_dir_path)

    classifier = VGG16LSTMVideoClassifier()

    history = classifier.fit(data_dir_path=input_dir_path, model_dir_path=output_dir_path, vgg16_include_top=False,
                             data_set_name=data_set_name)

    plot_and_save_history(history, VGG16LSTMVideoClassifier.model_name,
                          report_dir_path + '/' + VGG16LSTMVideoClassifier.model_name + '-hi-dim-history.png')
Example #4
Source File: vgg16_bidirectional_lstm_hi_dim_train.py    From keras-video-classifier with MIT License 6 votes vote down vote up 
def main():
    K.set_image_dim_ordering('tf')
    sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

    from keras_video_classifier.library.recurrent_networks import VGG16BidirectionalLSTMVideoClassifier
    from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
    from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf

    data_set_name = 'UCF-101'
    input_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
    output_dir_path = os.path.join(os.path.dirname(__file__), 'models', data_set_name)
    report_dir_path = os.path.join(os.path.dirname(__file__), 'reports', data_set_name)

    np.random.seed(42)

    # this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
    load_ucf(input_dir_path)

    classifier = VGG16BidirectionalLSTMVideoClassifier()

    history = classifier.fit(data_dir_path=input_dir_path, model_dir_path=output_dir_path, vgg16_include_top=False,
                             data_set_name=data_set_name)

    plot_and_save_history(history, VGG16BidirectionalLSTMVideoClassifier.model_name,
                          report_dir_path + '/' + VGG16BidirectionalLSTMVideoClassifier.model_name + '-hi-dim-history.png')
Example #5
Source File: vgg16_lstm_train.py    From keras-video-classifier with MIT License 6 votes vote down vote up 
def main():
    K.set_image_dim_ordering('tf')
    sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

    from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
    from keras_video_classifier.library.recurrent_networks import VGG16LSTMVideoClassifier
    from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf

    data_set_name = 'UCF-101'
    input_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
    output_dir_path = os.path.join(os.path.dirname(__file__), 'models', data_set_name)
    report_dir_path = os.path.join(os.path.dirname(__file__), 'reports', data_set_name)

    np.random.seed(42)

    # this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
    load_ucf(input_dir_path)

    classifier = VGG16LSTMVideoClassifier()

    history = classifier.fit(data_dir_path=input_dir_path, model_dir_path=output_dir_path, data_set_name=data_set_name)

    plot_and_save_history(history, VGG16LSTMVideoClassifier.model_name,
                          report_dir_path + '/' + VGG16LSTMVideoClassifier.model_name + '-history.png')
Example #6
Source File: conv_dqn.py    From openai_lab with MIT License 5 votes vote down vote up 
def __init__(self, *args, **kwargs):
        from keras.layers.core import Dense, Flatten
        from keras.layers.convolutional import Convolution2D
        from keras import backend as K
        if K.backend() == 'theano':
            K.set_image_dim_ordering('tf')
        self.Dense = Dense
        self.Flatten = Flatten
        self.Convolution2D = Convolution2D

        self.kernel = 4
        self.stride = (2, 2)
        super(ConvDQN, self).__init__(*args, **kwargs)
Example #7
Source File: vgg16_lstm_hi_dim_predict.py    From keras-video-classifier with MIT License 5 votes vote down vote up 
def main():
    K.set_image_dim_ordering('tf')
    sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

    from keras_video_classifier.library.recurrent_networks import VGG16LSTMVideoClassifier
    from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf, scan_ucf_with_labels

    vgg16_include_top = False
    data_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
    model_dir_path = os.path.join(os.path.dirname(__file__), 'models', 'UCF-101')
    config_file_path = VGG16LSTMVideoClassifier.get_config_file_path(model_dir_path,
                                                                     vgg16_include_top=vgg16_include_top)
    weight_file_path = VGG16LSTMVideoClassifier.get_weight_file_path(model_dir_path,
                                                                     vgg16_include_top=vgg16_include_top)

    np.random.seed(42)

    load_ucf(data_dir_path)

    predictor = VGG16LSTMVideoClassifier()
    predictor.load_model(config_file_path, weight_file_path)

    videos = scan_ucf_with_labels(data_dir_path, [label for (label, label_index) in predictor.labels.items()])

    video_file_path_list = np.array([file_path for file_path in videos.keys()])
    np.random.shuffle(video_file_path_list)

    correct_count = 0
    count = 0

    for video_file_path in video_file_path_list:
        label = videos[video_file_path]
        predicted_label = predictor.predict(video_file_path)
        print('predicted: ' + predicted_label + ' actual: ' + label)
        correct_count = correct_count + 1 if label == predicted_label else correct_count
        count += 1
        accuracy = correct_count / count
        print('accuracy: ', accuracy)
Example #8
Source File: p2b1_baseline_keras2.py    From Benchmarks with MIT License 5 votes vote down vote up 
def initialize_parameters(default_model = 'p2b1_default_model.txt'):

    # Build benchmark object
    p2b1Bmk = p2b1.BenchmarkP2B1(p2b1.file_path, default_model, 'keras',
    prog='p2b1_baseline', desc='Train Molecular Frame Autoencoder - Pilot 2 Benchmark 1')

    # Initialize parameters
    GP = candle.finalize_parameters(p2b1Bmk)
    #p2b1.logger.info('Params: {}'.format(gParameters))

    print ('\nTraining parameters:')
    for key in sorted(GP):
        print ("\t%s: %s" % (key, GP[key]))

    # print json.dumps(GP, indent=4, skipkeys=True, sort_keys=True)

    if GP['backend'] != 'theano' and GP['backend'] != 'tensorflow':
        sys.exit('Invalid backend selected: %s' % GP['backend'])

    os.environ['KERAS_BACKEND'] = GP['backend']
    reload(K)
    '''
    if GP['backend'] == 'theano':
        K.set_image_dim_ordering('th')
    elif GP['backend'] == 'tensorflow':
        K.set_image_dim_ordering('tf')
    '''
    K.set_image_data_format('channels_last')
#"th" format means that the convolutional kernels will have the shape (depth, input_depth, rows, cols)

#"tf" format means that the convolutional kernels will have the shape (rows, cols, input_depth, depth)
    print ("Image data format: ", K.image_data_format())
#    print "Image ordering: ", K.image_dim_ordering()
    return GP
Example #9
Source File: dcgan_v3.py    From keras-text-to-image with MIT License 5 votes vote down vote up 
def __init__(self):
        K.set_image_dim_ordering('tf')
        self.generator = None
        self.discriminator = None
        self.model = None
        self.img_width = 7
        self.img_height = 7
        self.img_channels = 1
        self.random_input_dim = 100
        self.text_input_dim = 100
        self.config = None
        self.glove_source_dir_path = './very_large_data'
        self.glove_model = GloveModel()
Example #10
Source File: dcgan_v2.py    From keras-text-to-image with MIT License 5 votes vote down vote up 
def __init__(self):
        K.set_image_dim_ordering('tf')
        self.generator = None
        self.discriminator = None
        self.model = None
        self.img_width = 7
        self.img_height = 7
        self.img_channels = 1
        self.text_input_dim = 100
        self.config = None
        self.glove_source_dir_path = './very_large_data'
        self.glove_model = GloveModel()
Example #11
Source File: dcgan.py    From keras-text-to-image with MIT License 5 votes vote down vote up 
def __init__(self):
        K.set_image_dim_ordering('tf')
        self.generator = None
        self.discriminator = None
        self.model = None
        self.img_width = 7
        self.img_height = 7
        self.img_channels = 1
        self.random_input_dim = 100
        self.text_input_dim = 100
        self.config = None
        self.glove_source_dir_path = './very_large_data'
        self.glove_model = GloveModel()