Python keras.io() Examples
The following are 4 code examples for showing how to use keras.io(). These examples are extracted from open source projects. 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.
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Example 1
| Project: typhon Author: atmtools File: qrnn.py License: MIT License | 4 votes |
|
def __init__(self,
input_dim,
quantiles,
depth=3,
width=128,
activation="relu",
ensemble_size=1,
**kwargs):
"""
Create a QRNN model.
Arguments:
input_dim(int): The dimension of the measurement space, i.e. the number
of elements in a single measurement vector y
quantiles(np.array): 1D-array containing the quantiles to estimate of
the posterior distribution. Given as fractions
within the range [0, 1].
depth(int): The number of hidden layers in the neural network to
use for the regression. Default is 3, i.e. three hidden
plus input and output layer.
width(int): The number of neurons in each hidden layer.
activation(str): The name of the activation functions to use. Default
is "relu", for rectified linear unit. See
`this <https://keras.io/activations>`_ link for
available functions.
**kwargs: Additional keyword arguments are passed to the constructor
call `keras.layers.Dense` of the hidden layers, which can
for example be used to add regularization. For more info consult
`Keras documentation. <https://keras.io/layers/core/#dense>`_
"""
self.input_dim = input_dim
self.quantiles = np.array(quantiles)
self.depth = depth
self.width = width
self.activation = activation
model = Sequential()
if depth == 0:
model.add(Dense(input_dim=input_dim,
units=len(quantiles),
activation=None))
else:
model.add(Dense(input_dim=input_dim,
units=width,
activation=activation))
for i in range(depth - 2):
model.add(Dense(units=width,
activation=activation,
**kwargs))
model.add(Dense(units=len(quantiles), activation=None))
self.models = [clone_model(model) for i in range(ensemble_size)]
Example 2
| Project: SparseSC Author: microsoft File: match_space.py License: MIT License | 4 votes |
|
def keras_reproducible(seed=1234, verbose=0, TF_CPP_MIN_LOG_LEVEL="3"):
"""
https://keras.io/getting-started/faq/#how-can-i-obtain-reproducible-results-using-keras-during-development
"""
import random
import pkg_resources
import os
random.seed(seed)
np.random.seed(seed)
os.environ["PYTHONHASHSEED"] = "0" # might need to do this outside the script
if verbose == 0:
os.environ[
"TF_CPP_MIN_LOG_LEVEL"
] = TF_CPP_MIN_LOG_LEVEL # 2 will print warnings
try:
import tensorflow
except ImportError:
raise ImportError("Missing required package 'tensorflow'")
# Use the TF 1.x API
if pkg_resources.get_distribution("tensorflow").version.startswith("1."):
tf = tensorflow
else:
tf = tensorflow.compat.v1
if verbose == 0:
# https://github.com/tensorflow/tensorflow/issues/27023
try:
from tensorflow.python.util import deprecation
deprecation._PRINT_DEPRECATION_WARNINGS = False
except ImportError:
try:
from tensorflow.python.util import module_wrapper as deprecation
except ImportError:
from tensorflow.python.util import deprecation_wrapper as deprecation
deprecation._PER_MODULE_WARNING_LIMIT = 0
# this was deprecated in 1.15 (maybe earlier)
tensorflow.compat.v1.logging.set_verbosity(tensorflow.compat.v1.logging.ERROR)
ConfigProto = tf.ConfigProto
session_conf = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1
)
with capture_all(): # doesn't have quiet option
try:
from tensorflow.python.keras import backend as K
except ImportError:
raise ImportError("Missing required module 'keras'")
tf.set_random_seed(seed)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)
Example 3
| Project: keras_to_tensorflow Author: amir-abdi File: keras_to_tensorflow.py License: 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 4
| Project: Maix_Toolbox Author: sipeed File: keras_to_tensorflow.py License: Apache License 2.0 | 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
