Python backend.set_image_dim_ordering方法代码示例

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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') 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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') 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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') 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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') 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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') 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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() 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import set_image_dim_ordering [as 别名]
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()