Python keras.__version__() Examples

def collect_environment(self, overwrite_variables=None):
        import socket
        import os
        import pip
        import platform

        env = {}

        if not overwrite_variables:
            overwrite_variables = {}

        import aetros
        env['aetros_version'] = aetros.__version__
        env['python_version'] = platform.python_version()
        env['python_executable'] = sys.executable

        env['hostname'] = socket.gethostname()
        env['variables'] = dict(os.environ)
        env['variables'].update(overwrite_variables)

        if 'AETROS_SSH_KEY' in env['variables']: del env['variables']['AETROS_SSH_KEY']
        if 'AETROS_SSH_KEY_BASE64' in env['variables']: del env['variables']['AETROS_SSH_KEY_BASE64']

        env['pip_packages'] = sorted([[i.key, i.version] for i in pip.get_installed_distributions()])
        self.set_system_info('environment', env) 

def __init__(self, params):
        super(NeuralNetworkAlgorithm, self).__init__(params)

        self.library_version = keras.__version__

        self.rounds = additional.get("one_step", 1)
        self.max_iters = additional.get("max_steps", 1)
        self.learner_params = {
            "dense_layers": params.get("dense_layers"),
            "dense_1_size": params.get("dense_1_size"),
            "dense_2_size": params.get("dense_2_size"),
            "dropout": params.get("dropout"),
            "learning_rate": params.get("learning_rate"),
            "momentum": params.get("momentum"),
            "decay": params.get("decay"),
        }
        self.model = None  # we need input data shape to construct model

        if "model_architecture_json" in params:
            self.model = model_from_json(
                json.loads(params.get("model_architecture_json"))
            )
            self.compile_model()

        logger.debug("NeuralNetworkAlgorithm __init__") 

def _get_decoder_initial_states(self):
        """
        Return decoder states as Input layers
        """
        decoder_states_inputs = []
        for units in self.encoder_layers:
            decoder_state_input_h = Input(shape=(units,))
            input_states = [decoder_state_input_h]
            if self.cell == LSTMCell:
                decoder_state_input_c = Input(shape=(units,))
                input_states = [decoder_state_input_h, decoder_state_input_c]
            decoder_states_inputs.extend(input_states)
        if keras.__version__ < '2.2':
            return list(reversed(decoder_states_inputs))
        else:
            return decoder_states_inputs 

def _load_model(model_path, keras_module, **kwargs):
    keras_models = importlib.import_module(keras_module.__name__ + ".models")
    custom_objects = kwargs.pop("custom_objects", {})
    custom_objects_path = None
    if os.path.isdir(model_path):
        if os.path.isfile(os.path.join(model_path, _CUSTOM_OBJECTS_SAVE_PATH)):
            custom_objects_path = os.path.join(model_path, _CUSTOM_OBJECTS_SAVE_PATH)
        model_path = os.path.join(model_path, _MODEL_SAVE_PATH)
    if custom_objects_path is not None:
        import cloudpickle
        with open(custom_objects_path, "rb") as in_f:
            pickled_custom_objects = cloudpickle.load(in_f)
            pickled_custom_objects.update(custom_objects)
            custom_objects = pickled_custom_objects
    from distutils.version import StrictVersion
    if StrictVersion(keras_module.__version__.split('-')[0]) >= StrictVersion("2.2.3"):
        # NOTE: Keras 2.2.3 does not work with unicode paths in python2. Pass in h5py.File instead
        # of string to avoid issues.
        import h5py
        with h5py.File(os.path.abspath(model_path), "r") as model_path:
            return keras_models.load_model(model_path, custom_objects=custom_objects, **kwargs)
    else:
        # NOTE: Older versions of Keras only handle filepath.
        return keras_models.load_model(model_path, custom_objects=custom_objects, **kwargs) 

def prog(self):#Show progress
        nb_batches_total=(self.params['epochs'] if kv-1 else self.params['nb_epoch'])*(self.params['samples'] if kv-1 else self.params['nb_sample'])/self.params['batch_size']
        nb_batches_epoch=(self.params['samples'] if kv-1 else self.params['nb_sample'])/self.params['batch_size']
        prog_total=(self.t_batches/nb_batches_total if nb_batches_total else 0)+0.01
        prog_epoch=(self.c_batches/nb_batches_epoch if nb_batches_epoch else 0)+0.01
        if self.t_epochs:
            now=time.time()
            t_mean=float(sum(self.t_epochs)) / len(self.t_epochs)
            eta_t=(now-self.train_start)*((1/prog_total)-1)
            eta_e=t_mean*(1-prog_epoch)
            t_end=time.asctime(time.localtime(now+eta_t))
            e_end=time.asctime(time.localtime(now+eta_e))
            m='\nTotal:\nProg:'+str(prog_total*100.)[:5]+'%\nEpoch:'+str(self.epoch[-1])+'/'+str(self.stopped_epoch)+'\nETA:'+str(eta_t)[:8]+'sec\nTrain will be finished at '+t_end+'\nCurrent epoch:\nPROG:'+str(prog_epoch*100.)[:5]+'%\nETA:'+str(eta_e)[:8]+'sec\nCurrent epoch will be finished at '+e_end
            self.t_send(msg=m)
            print(m)
        else:
            now=time.time()
            eta_t=(now-self.train_start)*((1/prog_total)-1)
            eta_e=(now-self.train_start)*((1/prog_epoch)-1)
            t_end=time.asctime(time.localtime(now+eta_t))
            e_end=time.asctime(time.localtime(now+eta_e))
            m='\nTotal:\nProg:'+str(prog_total*100.)[:5]+'%\nEpoch:'+str(len(self.epoch))+'/'+str(self.stopped_epoch)+'\nETA:'+str(eta_t)[:8]+'sec\nTrain will be finished at '+t_end+'\nCurrent epoch:\nPROG:'+str(prog_epoch*100.)[:5]+'%\nETA:'+str(eta_e)[:8]+'sec\nCurrent epoch will be finished at '+e_end
            self.t_send(msg=m)
            print(m)
            
#==============================================================================
# 
#============================================================================== 

def assert_keras_version():
    """ Assert that the Keras version is up to date.
    """
    detected = keras.__version__
    required = '.'.join(map(str, minimum_keras_version))
    assert(keras_version() >= minimum_keras_version), 'You are using keras version {}. The minimum required version is {}.'.format(detected, required) 

def on_step_end(self, step, logs):
        """ Update progression bar at the end of each step """
        if self.info_names is None:
            self.info_names = logs['info'].keys()
        values = [('reward', logs['reward'])]
        if KERAS_VERSION > '2.1.3':
            self.progbar.update((self.step % self.interval) + 1, values=values)
        else:
            self.progbar.update((self.step % self.interval) +
                                1, values=values, force=True)
        self.step += 1
        self.metrics.append(logs['metrics'])
        if len(self.info_names) > 0:
            self.infos.append([logs['info'][k] for k in self.info_names]) 

def keras_version():
    """ Get the Keras version.

    Returns
        tuple of (major, minor, patch).
    """
    return tuple(map(int, keras.__version__.split('.'))) 

def assert_keras_version():
    """ Assert that the Keras version is up to date.
    """
    detected = keras.__version__
    required = '.'.join(map(str, minimum_keras_version))
    assert(keras_version() >= minimum_keras_version), 'You are using keras version {}. The minimum required version is {}.'.format(detected, required) 

def log_model(keras_model, artifact_path, image_dims, domain):
    """
    Log a KerasImageClassifierPyfunc model as an MLflow artifact for the current run.

    :param keras_model: Keras model to be saved.
    :param artifact_path: Run-relative artifact path this model is to be saved to.
    :param image_dims: Image dimensions the Keras model expects.
    :param domain: Labels for the classes this model can predict.
    """

    with TempDir() as tmp:
        data_path = tmp.path("image_model")
        os.mkdir(data_path)
        conf = {
            "image_dims": "/".join(map(str, image_dims)),
            "domain": "/".join(map(str, domain))
        }
        with open(os.path.join(data_path, "conf.yaml"), "w") as f:
            yaml.safe_dump(conf, stream=f)
        keras_path = os.path.join(data_path, "keras_model")
        mlflow.keras.save_model(keras_model, path=keras_path)
        conda_env = tmp.path("conda_env.yaml")
        with open(conda_env, "w") as f:
            f.write(conda_env_template.format(python_version=PYTHON_VERSION,
                                              keras_version=keras.__version__,
                                              tf_name=tf.__name__,  # can have optional -gpu suffix
                                              tf_version=tf.__version__,
                                              pillow_version=PIL.__version__))

        mlflow.pyfunc.log_model(artifact_path=artifact_path,
                                loader_module=__name__,
                                code_path=[__file__],
                                data_path=data_path,
                                conda_env=conda_env) 

def log_model(keras_model, artifact_path, image_dims, domain):
    """
    Log a KerasImageClassifierPyfunc model as an MLflow artifact for the current run.

    :param keras_model: Keras model to be saved.
    :param artifact_path: Run-relative artifact path this model is to be saved to.
    :param image_dims: Image dimensions the Keras model expects.
    :param domain: Labels for the classes this model can predict.
    """

    with TempDir() as tmp:
        data_path = tmp.path("image_model")
        os.mkdir(data_path)
        conf = {
            "image_dims": "/".join(map(str, image_dims)),
            "domain": "/".join(map(str, domain))
        }
        with open(os.path.join(data_path, "conf.yaml"), "w") as f:
            yaml.safe_dump(conf, stream=f)
        keras_path = os.path.join(data_path, "keras_model")
        mlflow.keras.save_model(keras_model, path=keras_path)
        conda_env = tmp.path("conda_env.yaml")
        with open(conda_env, "w") as f:
            f.write(conda_env_template.format(python_version=PYTHON_VERSION,
                                              keras_version=keras.__version__,
                                              tf_name=tf.__name__,  # can have optional -gpu suffix
                                              tf_version=tf.__version__,
                                              pillow_version=PIL.__version__))

        mlflow.pyfunc.log_model(artifact_path=artifact_path,
                                loader_module=__name__,
                                code_path=[__file__],
                                data_path=data_path,
                                conda_env=conda_env) 

def log_keras_version_info():
    import keras
    logger.info("Keras version: " + keras.__version__)
    from keras import backend as K
    try:
        backend = K.backend()
    except AttributeError:
        backend = K._BACKEND  # pylint: disable=protected-access
    if backend == 'theano':
        import theano
        logger.info("Theano version: " + theano.__version__)
        logger.warning("Using Keras' theano backend is not supported! Expect to crash...")
    elif backend == 'tensorflow':
        import tensorflow
        logger.info("Tensorflow version: " + tensorflow.__version__)  # pylint: disable=no-member 

def get_default_conda_env(include_cloudpickle=False, keras_module=None):
    """
    :return: The default Conda environment for MLflow Models produced by calls to
             :func:`save_model()` and :func:`log_model()`.
    """
    import tensorflow as tf
    conda_deps = []  # if we use tf.keras we only need to declare dependency on tensorflow
    pip_deps = []
    if keras_module is None:
        import keras
        keras_module = keras
    if keras_module.__name__ == "keras":
        # Temporary fix: the created conda environment has issues installing keras >= 2.3.1
        if LooseVersion(keras_module.__version__) < LooseVersion('2.3.1'):
            conda_deps.append("keras=={}".format(keras_module.__version__))
        else:
            pip_deps.append("keras=={}".format(keras_module.__version__))
    if include_cloudpickle:
        import cloudpickle
        pip_deps.append("cloudpickle=={}".format(cloudpickle.__version__))
    # Temporary fix: conda-forge currently does not have tensorflow > 1.14
    # The Keras pyfunc representation requires the TensorFlow
    # backend for Keras. Therefore, the conda environment must
    # include TensorFlow
    if LooseVersion(tf.__version__) <= LooseVersion('1.13.2'):
        conda_deps.append("tensorflow=={}".format(tf.__version__))
    else:
        pip_deps.append("tensorflow=={}".format(tf.__version__))

    return _mlflow_conda_env(
        additional_conda_deps=conda_deps,
        additional_pip_deps=pip_deps,
        additional_conda_channels=None) 

def _load_pyfunc(path):
    """
    Load PyFunc implementation. Called by ``pyfunc.load_pyfunc``.

    :param path: Local filesystem path to the MLflow Model with the ``keras`` flavor.
    """
    import tensorflow as tf
    if os.path.isfile(os.path.join(path, _KERAS_MODULE_SPEC_PATH)):
        with open(os.path.join(path, _KERAS_MODULE_SPEC_PATH), "r") as f:
            keras_module = importlib.import_module(f.read())
    else:
        import keras
        keras_module = keras

    K = importlib.import_module(keras_module.__name__ + ".backend")
    if keras_module.__name__ == "tensorflow.keras" or K.backend() == 'tensorflow':
        if LooseVersion(tf.__version__) < LooseVersion('2.0.0'):
            graph = tf.Graph()
            sess = tf.Session(graph=graph)
            # By default tf backed models depend on the global graph and session.
            # We create an use new Graph and Session and store them with the model
            # This way the model is independent on the global state.
            with graph.as_default():
                with sess.as_default():  # pylint:disable=not-context-manager
                    K.set_learning_phase(0)
                    m = _load_model(path, keras_module=keras_module, compile=False)
                    return _KerasModelWrapper(m, graph, sess)
        else:
            K.set_learning_phase(0)
            m = _load_model(path, keras_module=keras_module, compile=False)
            return _KerasModelWrapper(m, None, None)

    else:
        raise MlflowException("Unsupported backend '%s'" % K._BACKEND) 

def log_model(keras_model, artifact_path, image_dims, domain):
    """
    Log a KerasImageClassifierPyfunc model as an MLflow artifact for the current run.

    :param keras_model: Keras model to be saved.
    :param artifact_path: Run-relative artifact path this model is to be saved to.
    :param image_dims: Image dimensions the Keras model expects.
    :param domain: Labels for the classes this model can predict.
    """

    with TempDir() as tmp:
        data_path = tmp.path("image_model")
        os.mkdir(data_path)
        conf = {
            "image_dims": "/".join(map(str, image_dims)),
            "domain": "/".join(map(str, domain))
        }
        with open(os.path.join(data_path, "conf.yaml"), "w") as f:
            yaml.safe_dump(conf, stream=f)
        keras_path = os.path.join(data_path, "keras_model")
        mlflow.keras.save_model(keras_model, path=keras_path)
        conda_env = tmp.path("conda_env.yaml")
        with open(conda_env, "w") as f:
            f.write(conda_env_template.format(python_version=PYTHON_VERSION,
                                              keras_version=keras.__version__,
                                              tf_name=tf.__name__,  # can have optional -gpu suffix
                                              tf_version=tf.__version__,
                                              pip_version=pip.__version__,
                                              pillow_version=PIL.__version__))

        mlflow.pyfunc.log_model(artifact_path=artifact_path,
                                loader_module=__name__,
                                code_path=[__file__],
                                data_path=data_path,
                                conda_env=conda_env) 

def _updated_config(self):
        '''shared between different serialization methods'''
        from keras import __version__ as keras_version

        config = self.get_config()
        model_config = {
            'class_name': self.__class__.__name__,
            'config': config,
            'keras_version': keras_version
        }
        return model_config 

def pretty_versions():
    import keras
    import tensorflow as tf
    k_version = keras.__version__
    t_version = tf.__version__
    return "keras-vggface : {}, keras : {} , tensorflow : {} ".format(__version__,k_version,t_version) 

def keras_version():
    """ Get the Keras version.

    Returns
        tuple of (major, minor, patch).
    """
    return tuple(map(int, keras.__version__.split('.'))) 

def assert_keras_version():
    """ Assert that the Keras version is up to date.
    """
    detected = keras.__version__
    required = '.'.join(map(str, minimum_keras_version))
    assert(keras_version() >= minimum_keras_version), 'You are using keras version {}. The minimum required version is {}.'.format(detected, required) 

def keras_version():
    return tuple(map(int, keras.__version__.split('.'))) 

def assert_keras_version():
    detected = keras.__version__
    required = '.'.join(map(str, minimum_keras_version))
    assert(keras_version() >= minimum_keras_version), 'You are using keras version {}. The minimum required version is {}.'.format(detected, required) 

def keras_version():
    """ Get the Keras version.

    Returns
        tuple of (major, minor, patch).
    """
    return tuple(map(int, keras.__version__.split('.'))) 

def conv_2d(filters, kernel_shape, strides, padding, input_shape=None):
    """
    Defines the right convolutional layer according to the
    version of Keras that is installed.
    :param filters: (required integer) the dimensionality of the output
                    space (i.e. the number output of filters in the
                    convolution)
    :param kernel_shape: (required tuple or list of 2 integers) specifies
                         the strides of the convolution along the width and
                         height.
    :param padding: (required string) can be either 'valid' (no padding around
                    input or feature map) or 'same' (pad to ensure that the
                    output feature map size is identical to the layer input)
    :param input_shape: (optional) give input shape if this is the first
                        layer of the model
    :return: the Keras layer
    """
    if LooseVersion(keras.__version__) >= LooseVersion('2.0.0'):
        if input_shape is not None:
            return Conv2D(filters=filters, kernel_size=kernel_shape,
                          strides=strides, padding=padding,
                          input_shape=input_shape)
        else:
            return Conv2D(filters=filters, kernel_size=kernel_shape,
                          strides=strides, padding=padding)
    else:
        if input_shape is not None:
            return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                                 subsample=strides, border_mode=padding,
                                 input_shape=input_shape)
        else:
            return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                                 subsample=strides, border_mode=padding) 

def save_model(model, filepath, overwrite=True):

    def get_json_type(obj):
        if hasattr(obj, 'get_config'):
            return {'class_name': obj.__class__.__name__,
                    'config': obj.get_config()}

        if type(obj).__module__ == np.__name__:
            return obj.item()

        if callable(obj) or type(obj).__name__ == type.__name__:
            return obj.__name__

        raise TypeError('Not JSON Serializable:', obj)

    import h5py
    from keras import __version__ as keras_version

    if not overwrite and os.path.isfile(filepath):
        proceed = keras.models.ask_to_proceed_with_overwrite(filepath)
        if not proceed:
            return

    f = h5py.File(filepath, 'w')
    f.attrs['keras_version'] = str(keras_version).encode('utf8')
    f.attrs['generator_config'] = json.dumps({
        'class_name': model.discriminator.__class__.__name__,
        'config': model.generator.get_config(),
    }, default=get_json_type).encode('utf8')
    f.attrs['discriminator_config'] = json.dumps({
        'class_name': model.discriminator.__class__.__name__,
        'config': model.discriminator.get_config(),
    }, default=get_json_type).encode('utf8')

    generator_weights_group = f.create_group('generator_weights')
    discriminator_weights_group = f.create_group('discriminator_weights')
    model.generator.save_weights_to_hdf5_group(generator_weights_group)
    model.discriminator.save_weights_to_hdf5_group(discriminator_weights_group)

    f.flush()
    f.close() 

def upload_keras_graph(self, model):
        from aetros.keras import model_to_graph
        import keras

        if keras.__version__[0] == '2':
            graph = model_to_graph(model)
            self.set_graph(graph) 

def set_generator_validation_nb(self, number):
        """
        sets self.nb_val_samples which is used in model.fit if input is a generator
        :param number:
        :return:
        """

        self.nb_val_samples = number
        diff_to_batch = number % self.get_batch_size()
        if diff_to_batch > 0:
            self.nb_val_samples += self.get_batch_size() - diff_to_batch

        import keras
        if '1' != keras.__version__[0]:
            self.nb_val_samples = self.nb_val_samples // self.get_batch_size() 

def conv_2d(filters, kernel_shape, strides, padding, input_shape=None):
    """
    Defines the right convolutional layer according to the
    version of Keras that is installed.
    :param filters: (required integer) the dimensionality of the output
                    space (i.e. the number output of filters in the
                    convolution)
    :param kernel_shape: (required tuple or list of 2 integers) specifies
                         the strides of the convolution along the width and
                         height.
    :param padding: (required string) can be either 'valid' (no padding around
                    input or feature map) or 'same' (pad to ensure that the
                    output feature map size is identical to the layer input)
    :param input_shape: (optional) give input shape if this is the first
                        layer of the model
    :return: the Keras layer
    """
    if LooseVersion(keras.__version__) >= LooseVersion('2.0.0'):
        print "Using Keras version", keras.__version__
        if input_shape is not None:
            return Conv2D(filters=filters, kernel_size=kernel_shape,
                          strides=strides, padding=padding,
                          input_shape=input_shape)
        else:
            return Conv2D(filters=filters, kernel_size=kernel_shape,
                          strides=strides, padding=padding)
    else:
        print "Using *old* keras version", keras.__version__
        if input_shape is not None:
            return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                                 subsample=strides, border_mode=padding,
                                 input_shape=input_shape)
        else:
            return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                                 subsample=strides, border_mode=padding) 

def conv_2d(filters, kernel_shape, strides, padding, input_shape=None):
    """
    Defines the right convolutional layer according to the
    version of Keras that is installed.
    :param filters: (required integer) the dimensionality of the output
                    space (i.e. the number output of filters in the
                    convolution)
    :param kernel_shape: (required tuple or list of 2 integers) specifies
                         the strides of the convolution along the width and
                         height.
    :param padding: (required string) can be either 'valid' (no padding around
                    input or feature map) or 'same' (pad to ensure that the
                    output feature map size is identical to the layer input)
    :param input_shape: (optional) give input shape if this is the first
                        layer of the model
    :return: the Keras layer
    """
    if LooseVersion(keras.__version__) >= LooseVersion('2.0.0'):
        print "Using Keras version", keras.__version__
        if input_shape is not None:
            return Conv2D(filters=filters, kernel_size=kernel_shape,
                          strides=strides, padding=padding,
                          input_shape=input_shape)
        else:
            return Conv2D(filters=filters, kernel_size=kernel_shape,
                          strides=strides, padding=padding)
    else:
        print "Using *old* keras version", keras.__version__
        if input_shape is not None:
            return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                                 subsample=strides, border_mode=padding,
                                 input_shape=input_shape)
        else:
            return Convolution2D(filters, kernel_shape[0], kernel_shape[1],
                                 subsample=strides, border_mode=padding) 

def keras_version():
    return tuple(map(int, keras.__version__.split('.'))) 

def assert_keras_version():
    detected = keras.__version__
    required = '.'.join(map(str, minimum_keras_version))
    assert(keras_version() >= minimum_keras_version), 'You are using keras version {}. The minimum required version is {}.'.format(detected, required)