Python tensorflow.keras.models() Examples
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code examples of tensorflow.keras.models().
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Example #1
Source File: model.py From ocrd_anybaseocr with Apache License 2.0 | 6 votes |
def compute_backbone_shapes(config, image_shape): """Computes the width and height of each stage of the backbone network. Returns: [N, (height, width)]. Where N is the number of stages """ if callable(config.BACKBONE): return config.COMPUTE_BACKBONE_SHAPE(image_shape) # Currently supports ResNet only assert config.BACKBONE in ["resnet50", "resnet101"] return np.array( [[int(math.ceil(image_shape[0] / stride)), int(math.ceil(image_shape[1] / stride))] for stride in config.BACKBONE_STRIDES]) ############################################################ # Resnet Graph ############################################################ # Code adopted from: # https://github.com/fchollet/deep-learning-models/blob/master/resnet50.py
Example #2
Source File: model.py From ocrd_anybaseocr with Apache License 2.0 | 5 votes |
def get_imagenet_weights(self): """Downloads ImageNet trained weights from Keras. Returns path to weights file. """ from keras.utils.data_utils import get_file TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/'\ 'releases/download/v0.2/'\ 'resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5' weights_path = get_file('resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5', TF_WEIGHTS_PATH_NO_TOP, cache_subdir='models', md5_hash='a268eb855778b3df3c7506639542a6af') return weights_path
Example #3
Source File: __init__.py From segmentation_models with MIT License | 5 votes |
def get_preprocessing(name): preprocess_input = Backbones.get_preprocessing(name) # add bakcend, models, layers, utils submodules in kwargs preprocess_input = inject_global_submodules(preprocess_input) # delete other kwargs # keras-applications preprocessing raise an error if something # except `backend`, `layers`, `models`, `utils` passed in kwargs preprocess_input = filter_kwargs(preprocess_input) return preprocess_input
Example #4
Source File: __init__.py From segmentation_models with MIT License | 5 votes |
def filter_kwargs(func): @functools.wraps(func) def wrapper(*args, **kwargs): new_kwargs = {k: v for k, v in kwargs.items() if k in ['backend', 'layers', 'models', 'utils']} return func(*args, **new_kwargs) return wrapper
Example #5
Source File: __init__.py From segmentation_models with MIT License | 5 votes |
def inject_global_submodules(func): @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = _KERAS_BACKEND kwargs['layers'] = _KERAS_LAYERS kwargs['models'] = _KERAS_MODELS kwargs['utils'] = _KERAS_UTILS return func(*args, **kwargs) return wrapper
Example #6
Source File: __init__.py From garbage_classify with Apache License 2.0 | 5 votes |
def inject_tfkeras_modules(func): import tensorflow.keras as tfkeras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = tfkeras.backend kwargs['layers'] = tfkeras.layers kwargs['models'] = tfkeras.models kwargs['utils'] = tfkeras.utils return func(*args, **kwargs) return wrapper
Example #7
Source File: __init__.py From garbage_classify with Apache License 2.0 | 5 votes |
def inject_keras_modules(func): import keras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = keras.backend kwargs['layers'] = keras.layers kwargs['models'] = keras.models kwargs['utils'] = keras.utils return func(*args, **kwargs) return wrapper
Example #8
Source File: __init__.py From garbage_classify with Apache License 2.0 | 5 votes |
def get_submodules_from_kwargs(kwargs): backend = kwargs.get('backend', _KERAS_BACKEND) layers = kwargs.get('layers', _KERAS_LAYERS) models = kwargs.get('models', _KERAS_MODELS) utils = kwargs.get('utils', _KERAS_UTILS) for key in kwargs.keys(): if key not in ['backend', 'layers', 'models', 'utils']: raise TypeError('Invalid keyword argument: %s', key) return backend, layers, models, utils
Example #9
Source File: imagenet_utils.py From DeepPoseKit with Apache License 2.0 | 5 votes |
def decode_predictions(preds, top=5, **kwargs): """Decodes the prediction of an ImageNet model. # Arguments preds: Numpy tensor encoding a batch of predictions. top: Integer, how many top-guesses to return. # Returns A list of lists of top class prediction tuples `(class_name, class_description, score)`. One list of tuples per sample in batch input. # Raises ValueError: In case of invalid shape of the `pred` array (must be 2D). """ global CLASS_INDEX if len(preds.shape) != 2 or preds.shape[1] != 1000: raise ValueError('`decode_predictions` expects ' 'a batch of predictions ' '(i.e. a 2D array of shape (samples, 1000)). ' 'Found array with shape: ' + str(preds.shape)) if CLASS_INDEX is None: fpath = keras_utils.get_file( 'imagenet_class_index.json', CLASS_INDEX_PATH, cache_subdir='models', file_hash='c2c37ea517e94d9795004a39431a14cb') with open(fpath) as f: CLASS_INDEX = json.load(f) results = [] for pred in preds: top_indices = pred.argsort()[-top:][::-1] result = [tuple(CLASS_INDEX[str(i)]) + (pred[i],) for i in top_indices] result.sort(key=lambda x: x[2], reverse=True) results.append(result) return results
Example #10
Source File: parallel_model.py From ocrd_anybaseocr with Apache License 2.0 | 5 votes |
def summary(self, *args, **kwargs): """Override summary() to display summaries of both, the wrapper and inner models.""" super(ParallelModel, self).summary(*args, **kwargs) self.inner_model.summary(*args, **kwargs)
Example #11
Source File: tfkeras.py From classification_models with MIT License | 5 votes |
def get_kwargs(): return { 'backend': tfkeras.backend, 'layers': tfkeras.layers, 'models': tfkeras.models, 'utils': tfkeras.utils, }
Example #12
Source File: __init__.py From EfficientDet with Apache License 2.0 | 5 votes |
def inject_tfkeras_modules(func): import tensorflow.keras as tfkeras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = tfkeras.backend kwargs['layers'] = tfkeras.layers kwargs['models'] = tfkeras.models kwargs['utils'] = tfkeras.utils return func(*args, **kwargs) return wrapper
Example #13
Source File: __init__.py From EfficientDet with Apache License 2.0 | 5 votes |
def inject_keras_modules(func): import keras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = keras.backend kwargs['layers'] = keras.layers kwargs['models'] = keras.models kwargs['utils'] = keras.utils return func(*args, **kwargs) return wrapper
Example #14
Source File: __init__.py From EfficientDet with Apache License 2.0 | 5 votes |
def get_submodules_from_kwargs(kwargs): backend = kwargs.get('backend', _KERAS_BACKEND) layers = kwargs.get('layers', _KERAS_LAYERS) models = kwargs.get('models', _KERAS_MODELS) utils = kwargs.get('utils', _KERAS_UTILS) for key in kwargs.keys(): if key not in ['backend', 'layers', 'models', 'utils']: raise TypeError('Invalid keyword argument: %s', key) return backend, layers, models, utils
Example #15
Source File: __init__.py From efficientnet with Apache License 2.0 | 5 votes |
def inject_tfkeras_modules(func): import tensorflow.keras as tfkeras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = tfkeras.backend kwargs['layers'] = tfkeras.layers kwargs['models'] = tfkeras.models kwargs['utils'] = tfkeras.utils return func(*args, **kwargs) return wrapper
Example #16
Source File: __init__.py From efficientnet with Apache License 2.0 | 5 votes |
def inject_keras_modules(func): import keras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = keras.backend kwargs['layers'] = keras.layers kwargs['models'] = keras.models kwargs['utils'] = keras.utils return func(*args, **kwargs) return wrapper
Example #17
Source File: __init__.py From efficientnet with Apache License 2.0 | 5 votes |
def get_submodules_from_kwargs(kwargs): backend = kwargs.get('backend', _KERAS_BACKEND) layers = kwargs.get('layers', _KERAS_LAYERS) models = kwargs.get('models', _KERAS_MODELS) utils = kwargs.get('utils', _KERAS_UTILS) for key in kwargs.keys(): if key not in ['backend', 'layers', 'models', 'utils']: raise TypeError('Invalid keyword argument: %s', key) return backend, layers, models, utils
Example #18
Source File: 02_keras_to_tensorflow.py From PINTO_model_zoo with MIT License | 4 votes |
def load_model(input_model_path, input_json_path=None, input_yaml_path=None): if not Path(input_model_path).exists(): raise FileNotFoundError( 'Model file `{}` does not exist.'.format(input_model_path)) try: model = keras.models.load_model(input_model_path) return model except FileNotFoundError as err: logging.error('Input mode file (%s) does not exist.', FLAGS.input_model) raise err except ValueError as wrong_file_err: if input_json_path: if not Path(input_json_path).exists(): raise FileNotFoundError( 'Model description json file `{}` does not exist.'.format( input_json_path)) try: model = model_from_json(open(str(input_json_path)).read()) model.load_weights(input_model_path) return model except Exception as err: logging.error("Couldn't load model from json.") raise err elif input_yaml_path: if not Path(input_yaml_path).exists(): raise FileNotFoundError( 'Model description yaml file `{}` does not exist.'.format( input_yaml_path)) try: model = model_from_yaml(open(str(input_yaml_path)).read()) model.load_weights(input_model_path) return model except Exception as err: logging.error("Couldn't load model from yaml.") raise err else: logging.error( 'Input file specified only holds the weights, and not ' 'the model definition. Save the model using ' 'model.save(filename.h5) which will contain the network ' 'architecture as well as its weights. ' 'If the model is saved using the ' 'model.save_weights(filename) function, either ' 'input_model_json or input_model_yaml flags should be set to ' 'to import the network architecture prior to loading the ' 'weights. \n' 'Check the keras documentation for more details ' '(https://keras.io/getting-started/faq/)') raise wrong_file_err
Example #19
Source File: __init__.py From segmentation_models with MIT License | 4 votes |
def set_framework(name): """Set framework for Segmentation Models Args: name (str): one of ``keras``, ``tf.keras``, case insensitive. Raises: ValueError: in case of incorrect framework name. ImportError: in case framework is not installed. """ name = name.lower() if name == _KERAS_FRAMEWORK_NAME: import keras import efficientnet.keras # init custom objects elif name == _TF_KERAS_FRAMEWORK_NAME: from tensorflow import keras import efficientnet.tfkeras # init custom objects else: raise ValueError('Not correct module name `{}`, use `{}` or `{}`'.format( name, _KERAS_FRAMEWORK_NAME, _TF_KERAS_FRAMEWORK_NAME)) global _KERAS_BACKEND, _KERAS_LAYERS, _KERAS_MODELS global _KERAS_UTILS, _KERAS_LOSSES, _KERAS_FRAMEWORK _KERAS_FRAMEWORK = name _KERAS_BACKEND = keras.backend _KERAS_LAYERS = keras.layers _KERAS_MODELS = keras.models _KERAS_UTILS = keras.utils _KERAS_LOSSES = keras.losses # allow losses/metrics get keras submodules base.KerasObject.set_submodules( backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils, ) # set default framework